Publish independent ICML 2026 reproduction: Finding Most Influential Sets

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+outputs/influential_sets/influential_sets_audit.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,20 @@
 ---
-title: Research
-emoji: 🌍
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
+title: Reproduction - Finding Most Influential Sets
+emoji: 🔎
+colorFrom: indigo
+colorTo: green
+sdk: static
 pinned: false
-license: cc-by-nc-4.0
-short_description: A deep research agent that is smart and fast
+short_description: Exact top-k and selection-consistency audit
+tags:
+  - trackio
+  - open-reproductions
+  - icml2026-repro
+  - paper-ghd0zmtpB9
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Independent reproduction
+
+Independent reproduction of **Finding Most Influential Sets** (OpenReview
+`ghd0zmtpB9`). Complete source, exhaustive oracles, scaling runs, separation
+controls, and SHA-256 manifests are included.

app.py

@@ -1,472 +0,0 @@
-import spaces
-import gradio as gr
-import os
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import json
-import re
-from datetime import datetime
-from typing import List, Dict, Any
-from search_engine import AdvancedDuckDuckGoSearcher
-
-# Load model and tokenizer
-model_name = "HuggingFaceTB/SmolLM3-3B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name, 
-    torch_dtype=torch.bfloat16, 
-    device_map="auto"
-)
-
-class DeepResearchAgent:
-    def __init__(self, searcher):
-        self.searcher = searcher
-        self.search_history = []
-        self.research_context = ""
-    
-    def generate_response(self, prompt: str, max_new_tokens: int = 1000, enable_thinking: bool = True) -> str:
-        """Generate response using SmolLM3-3B with proper chat template"""
-        try:
-            # Prepare messages for chat template
-            messages = [
-                {"role": "system", "content": "You are an expert research analyst."},
-                {"role": "user", "content": prompt}
-            ]
-            
-            # Apply chat template
-            text = tokenizer.apply_chat_template(
-                messages,
-                tokenize=False,
-                add_generation_prompt=True,
-                enable_thinking=enable_thinking
-            )
-            
-            # Tokenize input
-            model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-            
-            # Generate response
-            with torch.no_grad():
-                generated_ids = model.generate(
-                    **model_inputs,
-                    max_new_tokens=max_new_tokens,
-                    temperature=0.7,
-                    top_p=0.95,
-                    do_sample=True,
-                    pad_token_id=tokenizer.eos_token_id
-                )
-            
-            # Decode response
-            output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
-            response = tokenizer.decode(output_ids, skip_special_tokens=True)
-            
-            return response
-        except Exception as e:
-            raise Exception(f"GPU generation failed: {str(e)}")
-    
-    def analyze_topic(self, topic: str) -> str:
-        """Initial analysis to determine research strategy"""
-        prompt = f"""Analyze the research topic: "{topic}"
-
-Please provide:
-1. Key aspects to research
-2. Specific search queries to use (3-5 queries)
-3. Expected sources and types of information
-4. Research depth and approach
-
-Respond with a structured analysis."""
-        return self.generate_response(prompt, max_new_tokens=800, enable_thinking=True)
-    
-    def synthesize_findings(self, topic: str, search_results: List[Dict]) -> str:
-        """Synthesize search results into comprehensive report"""
-        # Create numbered source references for citations
-        sources_text = []
-        for i, result in enumerate(search_results, 1):
-            sources_text.append(f"[{i}] {result['title']}\nURL: {result['url']}\nContent: {result['snippet']}")
-        
-        results_text = "\n\n".join(sources_text)
-        
-        prompt = f"""Create a comprehensive research report on: "{topic}"
-
-Available Sources:
-{results_text}
-
-Requirements:
-1. Write a detailed research report with multiple sections
-2. Use citations in format [1], [2], etc. referring to the numbered sources above
-3. Include specific facts, figures, and insights from the sources
-4. Structure with clear headings and subsections
-5. Make it analytical and well-researched
-
-Include these sections:
-• Executive Summary
-• Key Findings  
-• Detailed Analysis
-• Current Trends and Developments
-• Implications and Future Outlook
-• Conclusions
-
-Ensure the report is substantial, informative, and properly cited using the numbered source format."""
-        
-        return self.generate_response(prompt, max_new_tokens=2500, enable_thinking=True)
-    
-    @spaces.GPU(duration=180)  # 3 minutes for entire research process
-    def conduct_research(self, topic: str) -> str:
-        """Main research orchestration method - all AI operations in GPU context"""
-        try:
-            # Step 1: Analyze topic and create research strategy
-            analysis = self.analyze_topic(topic)
-            
-            # Generate targeted search queries directly
-            search_queries = [
-                f"{topic} overview",
-                f"{topic} latest research 2024",
-                f"{topic} applications uses",
-                f"{topic} trends developments", 
-                f"{topic} analysis insights"
-            ]
-            
-            # Step 2: Conduct searches
-            all_results = []
-            for i, query in enumerate(search_queries):
-                results = self.searcher.search(query, max_results=5)
-                all_results.extend(results)
-                self.search_history.append({'query': query, 'results': results})
-            
-            # Step 3: Synthesize findings
-            report = self.synthesize_findings(topic, all_results)
-            
-            # Step 4: Create structured final report
-            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            
-            # Ensure report has content and add debugging
-            if not report or len(report.strip()) < 50:
-                # Create a basic analysis from search results if AI generation fails
-                if all_results:
-                    basic_analysis = f"""
-## Executive Summary
-Research conducted on "{topic}" using {len(search_queries)} targeted search queries and {len(all_results)} sources.
-
-## Key Sources Found
-{chr(10).join([f"• **{result['title']}** - {result['snippet'][:200]}..." for result in all_results[:5]])}
-
-## Research Notes
-The search yielded {len(all_results)} relevant sources covering various aspects of {topic}. Further analysis may be needed for comprehensive insights.
-"""
-                    report = basic_analysis
-                else:
-                    report = f"Research analysis for '{topic}' completed. No substantial content was generated from the available sources."
-            
-            final_report = f"""# 🔬 Deep Research Report: {topic}
-*Generated on {timestamp}*
-
-## 📊 Research Content
-
-{report}
-
----
-
-## 📋 Research Methodology
-- **🔍 Search Queries Used:** {len(search_queries)} queries
-- **📚 Sources Analyzed:** {len(all_results)} sources  
-- **🤖 Analysis Engine:** SmolLM3-3B with multi-prompting
-- **⏱️ Generated:** {timestamp}
-
-### 🔎 Search Queries Executed:
-{chr(10).join([f"• {q}" for q in search_queries])}
-
-### 📖 Sources Referenced:
-{chr(10).join([f"[{i+1}] [{result['title']}]({result['url']})" for i, result in enumerate(all_results[:10])])}
-
----
-*Report generated using advanced AI research methodology with web search integration*
-"""
-            
-            return final_report
-            
-        except Exception as e:
-            return f"❌ Research failed: {str(e)}"
-
-# Initialize components
-searcher = AdvancedDuckDuckGoSearcher()
-agent = DeepResearchAgent(searcher)
-
-def research_interface(topic: str, progress=gr.Progress()):
-    """Main interface function for research"""
-    if not topic.strip():
-        return "❌ Please enter a research topic.", ""
-    
-    try:
-        # Show progress steps manually since callback can't be passed to GPU function
-        progress(0.1, desc="🔍 Starting research analysis...")
-        
-        result = agent.conduct_research(topic)
-        
-        progress(1.0, desc="✅ Research completed!")
-        
-        # Split the result into title and content for better display
-        lines = result.split('\n')
-        title_line = lines[0] if lines else "Research Complete"
-        content = '\n'.join(lines[1:]) if len(lines) > 1 else result
-        
-        return result, result  # Return both formatted and raw for copy
-    except Exception as e:
-        error_msg = f"❌ Error during research: {str(e)}"
-        return error_msg, error_msg
-
-# Custom CSS for dark theme with expandable sections
-css = """
-.gradio-container {
-    background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%) !important;
-    color: #ffffff !important;
-}
-
-.dark {
-    --background-fill-primary: #1a1a2e;
-    --background-fill-secondary: #16213e;
-    --block-background-fill: rgba(255, 255, 255, 0.05);
-    --block-border-color: rgba(255, 255, 255, 0.1);
-    --input-background-fill: rgba(255, 255, 255, 0.1);
-    --button-primary-background-fill: #4CAF50;
-    --button-primary-background-fill-hover: #45a049;
-}
-
-.prose h1, .prose h2, .prose h3 {
-    color: #ffffff !important;
-}
-
-.prose p, .prose li {
-    color: #e0e0e0 !important;
-}
-
-/* Research Report Styling */
-.research-container {
-    background: rgba(255, 255, 255, 0.05) !important;
-    border: 1px solid rgba(255, 255, 255, 0.1) !important;
-    border-radius: 12px !important;
-    padding: 20px !important;
-    margin: 10px 0 !important;
-}
-
-.copy-button {
-    background: #4CAF50 !important;
-    color: white !important;
-    border: none !important;
-    padding: 8px 16px !important;
-    border-radius: 6px !important;
-    cursor: pointer !important;
-    font-size: 14px !important;
-    margin: 10px 0 !important;
-    transition: background 0.3s ease !important;
-}
-
-.copy-button:hover {
-    background: #45a049 !important;
-}
-
-.expandable-section {
-    border: 1px solid rgba(255, 255, 255, 0.2) !important;
-    border-radius: 8px !important;
-    margin: 10px 0 !important;
-    overflow: hidden !important;
-}
-
-.section-header {
-    background: rgba(255, 255, 255, 0.1) !important;
-    padding: 12px 16px !important;
-    cursor: pointer !important;
-    user-select: none !important;
-    border-bottom: 1px solid rgba(255, 255, 255, 0.1) !important;
-}
-
-.section-header:hover {
-    background: rgba(255, 255, 255, 0.15) !important;
-}
-
-.section-content {
-    padding: 16px !important;
-    background: rgba(255, 255, 255, 0.03) !important;
-}
-
-/* Mobile responsiveness */
-@media (max-width: 768px) {
-    .gradio-container {
-        padding: 8px !important;
-    }
-    
-    .block {
-        margin: 5px 0 !important;
-    }
-    
-    .research-container {
-        padding: 12px !important;
-        margin: 5px 0 !important;
-    }
-    
-    .copy-button {
-        width: 100% !important;
-        margin: 8px 0 !important;
-        padding: 12px !important;
-        font-size: 16px !important;
-    }
-    
-    .section-header {
-        padding: 10px 12px !important;
-        font-size: 14px !important;
-    }
-    
-    .section-content {
-        padding: 12px !important;
-        font-size: 14px !important;
-    }
-}
-
-/* Better markdown styling in output */
-.gr-markdown {
-    line-height: 1.6 !important;
-}
-
-.gr-markdown code {
-    background: rgba(255, 255, 255, 0.1) !important;
-    padding: 2px 6px !important;
-    border-radius: 4px !important;
-    color: #ffffff !important;
-}
-
-.gr-markdown pre {
-    background: rgba(255, 255, 255, 0.05) !important;
-    border: 1px solid rgba(255, 255, 255, 0.1) !important;
-    border-radius: 8px !important;
-    padding: 12px !important;
-    overflow-x: auto !important;
-}
-"""
-
-# Create Gradio interface
-with gr.Blocks(
-    title="🔬 Deep Research Agent",
-    theme=gr.themes.Base().set(
-        body_background_fill="linear-gradient(135deg, #1a1a2e 0%, #16213e 100%)",
-        block_background_fill="rgba(255, 255, 255, 0.05)",
-        input_background_fill="rgba(255, 255, 255, 0.1)",
-        button_primary_background_fill="#4CAF50"
-    ),
-    css=css
-) as demo:
-    
-    gr.HTML("""
-    <div style="text-align: center; padding: 20px;">
-        <h1 style="color: #ffffff; margin-bottom: 10px;">🔬 Deep Research Agent</h1>
-        <p style="color: #cccccc; font-size: 18px;">
-            Powered by SmolLM3-3B • Advanced Multi-Prompting • Web Search Integration
-        </p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=4):
-            topic_input = gr.Textbox(
-                label="🎯 Research Topic",
-                placeholder="Enter your research topic (e.g., 'Latest developments in quantum computing', 'Climate change impacts on agriculture')",
-                lines=2,
-                elem_classes=["input-field"]
-            )
-        
-        with gr.Column(scale=1):
-            research_btn = gr.Button(
-                "🚀 Start Research",
-                variant="primary",
-                size="lg"
-            )
-    
-    with gr.Row():
-        with gr.Column():
-            output = gr.Markdown(
-                label="📊 Research Report",
-                value="👋 Welcome! Enter a topic above to begin your deep research.",
-                elem_classes=["research-container"]
-            )
-            with gr.Row():
-                copy_btn = gr.Button(
-                    "📋 Copy Full Report", 
-                    size="sm",
-                    elem_classes=["copy-button"]
-                )
-                export_btn = gr.Button(
-                    "💾 Export Report", 
-                    size="sm",
-                    elem_classes=["copy-button"]
-                )
-    
-    # Hidden textbox for copy functionality
-    copy_text = gr.Textbox(visible=False, interactive=False)
-    
-    gr.HTML("""
-    <div style="text-align: center; padding: 20px; color: #888;">
-        <p>🔥 <strong>Features:</strong> Multi-stage research • DuckDuckGo integration • SmolLM3-3B analysis • Comprehensive reporting</p>
-        <p>⚡ Optimized for ZeroGPU • Mobile-friendly design • Real-time progress tracking</p>
-    </div>
-    """)
-    
-    # Copy functionality
-    def copy_to_clipboard(text):
-        return gr.update(value=text)
-    
-    def export_report(text):
-        if text and len(text.strip()) > 10:
-            return gr.update(value=text, visible=True)
-        return gr.update(value="No report to export", visible=True)
-    
-    # Event handlers
-    research_btn.click(
-        fn=research_interface,
-        inputs=[topic_input],
-        outputs=[output, copy_text],
-        show_progress=True
-    )
-    
-    topic_input.submit(
-        fn=research_interface,
-        inputs=[topic_input],
-        outputs=[output, copy_text],
-        show_progress=True
-    )
-    
-    copy_btn.click(
-        fn=copy_to_clipboard,
-        inputs=[copy_text],
-        outputs=[copy_text],
-        js="""
-        function(text) {
-            if (text && text.length > 0) {
-                navigator.clipboard.writeText(text).then(function() {
-                    // Show success message
-                    const btn = document.querySelector('.copy-button');
-                    const original = btn.textContent;
-                    btn.textContent = '✅ Copied!';
-                    btn.style.background = '#2196F3';
-                    setTimeout(() => {
-                        btn.textContent = original;
-                        btn.style.background = '#4CAF50';
-                    }, 2000);
-                }).catch(function(err) {
-                    console.error('Failed to copy: ', err);
-                    alert('Failed to copy to clipboard');
-                });
-            }
-            return text;
-        }
-        """
-    )
-    
-    export_btn.click(
-        fn=export_report,
-        inputs=[copy_text],
-        outputs=[copy_text]
-    )
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )

audit_influential.py

@@ -0,0 +1,468 @@
+#!/usr/bin/env python3
+"""Independent finite certificates for Finding Most Influential Sets.
+
+Paper: "Finding Most Influential Sets" (OpenReview ghd0zmtpB9,
+arXiv:2606.05919).  This script does not import the authors' implementation.
+It checks the univariate deletion identity, Algorithm 1's fractional top-k
+reduction, finite termination, large-n behavior, and the finite separation
+mechanism used by the selection-consistency result.
+"""
+
+from __future__ import annotations
+
+import itertools
+import json
+import math
+import time
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+HERE = Path(__file__).resolve().parent
+OUTPUT_DIR = HERE / "outputs" / "influential_sets"
+SEED = 260605919
+
+
+def dinkelbach_topk(
+    w: np.ndarray,
+    c: np.ndarray,
+    k: int,
+    eta0: float = 0.0,
+    max_iterations: int = 100,
+) -> tuple[float, tuple[int, ...], int, float]:
+    """Maximize sum_S(w_i)/(sum(c_i)-sum_S(c_i)) over |S|=k.
+
+    The inner maximizer is a top-k operation on w + eta*c.  NumPy's
+    argpartition is expected linear time in n.  The fourth return value is
+    the final Dinkelbach residual W - eta*G.
+    """
+
+    w = np.asarray(w, dtype=np.float64)
+    c = np.asarray(c, dtype=np.float64)
+    if w.ndim != 1 or c.shape != w.shape or not 1 <= k < len(w):
+        raise ValueError("invalid one-dimensional inputs or k")
+    total = float(np.sum(c))
+    eta = float(eta0)
+    previous: tuple[int, ...] | None = None
+
+    for iteration in range(1, max_iterations + 1):
+        scores = w + eta * c
+        chosen_array = np.argpartition(-scores, k - 1)[:k]
+        chosen = tuple(sorted(int(i) for i in chosen_array))
+        idx = np.fromiter(chosen, dtype=np.int64)
+        numerator = float(np.sum(w[idx]))
+        denominator = total - float(np.sum(c[idx]))
+        if not denominator > 0.0:
+            raise ValueError("non-positive deletion denominator")
+        updated = numerator / denominator
+        residual = numerator - eta * denominator
+        if chosen == previous or abs(updated - eta) <= 2e-14 * max(1.0, abs(updated)):
+            final_residual = numerator - updated * denominator
+            return updated, chosen, iteration, final_residual
+        previous = chosen
+        eta = updated
+    raise RuntimeError("Dinkelbach iteration did not terminate")
+
+
+def exhaustive_ratio(
+    w: np.ndarray, c: np.ndarray, k: int
+) -> tuple[float, tuple[int, ...], float, int]:
+    """All-subset oracle; returns best, set, second-best, feasible count."""
+
+    total = float(np.sum(c))
+    best = -math.inf
+    second = -math.inf
+    best_set: tuple[int, ...] | None = None
+    feasible = 0
+    for subset in itertools.combinations(range(len(w)), k):
+        idx = np.fromiter(subset, dtype=np.int64)
+        denominator = total - float(np.sum(c[idx]))
+        if denominator <= 0.0:
+            continue
+        value = float(np.sum(w[idx])) / denominator
+        feasible += 1
+        if value > best:
+            second = best
+            best = value
+            best_set = subset
+        elif value > second:
+            second = value
+    if best_set is None:
+        raise ValueError("no feasible deletion set")
+    return best, best_set, second, feasible
+
+
+def refit_deletion_value(x: np.ndarray, y: np.ndarray, subset: tuple[int, ...]) -> float:
+    """Compute beta_full - beta_delete(S) by direct least-squares refitting."""
+
+    beta = float(np.dot(x, y) / np.dot(x, x))
+    keep = np.ones(len(x), dtype=bool)
+    keep[list(subset)] = False
+    beta_deleted = float(np.dot(x[keep], y[keep]) / np.dot(x[keep], x[keep]))
+    return beta - beta_deleted
+
+
+def exact_audit(rng: np.random.Generator, cases: int = 320) -> dict:
+    max_objective_error = 0.0
+    max_refit_identity_error = 0.0
+    max_final_residual = 0.0
+    max_iterations = 0
+    total_subsets = 0
+    all_seed_runs = 0
+    singleton_greedy_failures = 0
+    minimum_gap = math.inf
+
+    for case in range(cases):
+        n = int(rng.integers(10, 19))
+        k = int(rng.integers(1, min(5, n - 2) + 1))
+
+        # Continuous draws eliminate accidental ties.  A leverage mixture
+        # deliberately stresses the denominator as well as the numerator.
+        x = rng.normal(size=n)
+        if case % 4 == 0:
+            x[int(rng.integers(n))] *= 3.5
+        signal = float(rng.uniform(-2.0, 2.0))
+        y = signal * x + rng.normal(scale=float(rng.uniform(0.25, 2.0)), size=n)
+        beta = float(np.dot(x, y) / np.dot(x, x))
+        residual = y - beta * x
+        w = x * residual
+        c = x * x
+        total = float(np.sum(c))
+
+        oracle, oracle_set, second, feasible = exhaustive_ratio(w, c, k)
+        total_subsets += feasible
+        minimum_gap = min(minimum_gap, oracle - second if math.isfinite(second) else math.inf)
+
+        singleton_scores = w / (total - c)
+        greedy = tuple(sorted(int(i) for i in np.argpartition(-singleton_scores, k - 1)[:k]))
+        if greedy != oracle_set:
+            singleton_greedy_failures += 1
+
+        # Different initial ratios exercise convergence independently of a
+        # convenient warm start.
+        seeds = (0.0, -2.0 - abs(oracle), 2.0 + abs(oracle))
+        for eta0 in seeds:
+            value, chosen, iterations, final_residual = dinkelbach_topk(w, c, k, eta0)
+            max_objective_error = max(max_objective_error, abs(value - oracle))
+            max_final_residual = max(max_final_residual, abs(final_residual))
+            max_iterations = max(max_iterations, iterations)
+            all_seed_runs += 1
+            if abs(value - oracle) > 2e-11:
+                raise AssertionError(f"suboptimal case {case}: {value} vs {oracle}, {chosen}")
+
+        # Check the algebraic ratio against an actual delete-and-refit OLS,
+        # both at the optimum and at two deterministic nonoptimal subsets.
+        probes = [oracle_set]
+        combos = itertools.combinations(range(n), k)
+        probes.extend(list(itertools.islice(combos, 2)))
+        for subset in probes:
+            idx = np.fromiter(subset, dtype=np.int64)
+            formula = float(np.sum(w[idx])) / (total - float(np.sum(c[idx])))
+            direct = refit_deletion_value(x, y, subset)
+            max_refit_identity_error = max(max_refit_identity_error, abs(formula - direct))
+
+    assert max_objective_error < 2e-11
+    assert max_refit_identity_error < 2e-11
+    assert max_iterations < 20
+    assert singleton_greedy_failures > cases // 10
+    return {
+        "random_instances": cases,
+        "all_subset_candidates_checked": total_subsets,
+        "dinkelbach_runs_across_three_initial_ratios": all_seed_runs,
+        "max_objective_error_vs_exhaustive": max_objective_error,
+        "max_delete_refit_identity_error": max_refit_identity_error,
+        "max_iterations": max_iterations,
+        "max_final_fractional_residual": max_final_residual,
+        "singleton_ranking_failure_cases": singleton_greedy_failures,
+        "minimum_unique_optimum_gap": minimum_gap,
+    }
+
+
+def generic_fractional_audit(rng: np.random.Generator, cases: int = 240) -> dict:
+    """Exercise the reduction for generic additive numerators and costs."""
+
+    max_ratio_error = 0.0
+    max_inner_topk_error = 0.0
+    total_candidates = 0
+    max_iterations = 0
+    for case in range(cases):
+        n = int(rng.integers(9, 19))
+        k = int(rng.integers(1, min(5, n - 2) + 1))
+        w = rng.normal(size=n)
+        c = rng.lognormal(mean=-0.2, sigma=0.8, size=n)
+
+        # Enforce the paper's positive-denominator assumption for every k-set
+        # by adding an undeletable baseline curvature through a common rescale.
+        c /= 1.0 + float(np.max(c))
+        c += 0.05
+        if np.sum(c) - np.sum(np.sort(c)[-k:]) <= 0:
+            raise AssertionError("constructed generic case is infeasible")
+
+        oracle, _, _, feasible = exhaustive_ratio(w, c, k)
+        value, _, iterations, _ = dinkelbach_topk(
+            w, c, k, eta0=float(rng.uniform(-5.0, 5.0))
+        )
+        max_ratio_error = max(max_ratio_error, abs(value - oracle))
+        total_candidates += feasible
+        max_iterations = max(max_iterations, iterations)
+
+        # Equation (4): for fixed eta, maximizing W-eta*G is exactly the
+        # top-k sum of transformed item scores w_i+eta*c_i.
+        eta = float(rng.uniform(-4.0, 4.0))
+        scores = w + eta * c
+        chosen = np.argpartition(-scores, k - 1)[:k]
+        topk_value = float(np.sum(scores[chosen]))
+        exhaustive_inner = max(
+            float(np.sum(scores[list(subset)]))
+            for subset in itertools.combinations(range(n), k)
+        )
+        max_inner_topk_error = max(max_inner_topk_error, abs(topk_value - exhaustive_inner))
+
+    assert max_ratio_error < 2e-11
+    assert max_inner_topk_error < 2e-11
+    return {
+        "generic_linear_fractional_instances": cases,
+        "all_subset_candidates_checked": total_candidates,
+        "max_ratio_error_vs_exhaustive": max_ratio_error,
+        "max_fixed_eta_topk_error_vs_exhaustive": max_inner_topk_error,
+        "max_iterations": max_iterations,
+        "scope": "arbitrary continuous additive weights and positive costs, beyond the OLS-specialized identity",
+    }
+
+
+def scaling_audit(rng: np.random.Generator) -> dict:
+    rows: list[dict] = []
+    for n in (10_000, 100_000, 1_000_000):
+        timings: list[float] = []
+        iterations_seen: list[int] = []
+        objective = 0.0
+        for _ in range(5):
+            x = rng.normal(size=n)
+            y = 0.7 * x + rng.normal(size=n)
+            beta = float(np.dot(x, y) / np.dot(x, x))
+            w = x * (y - beta * x)
+            c = x * x
+            start = time.perf_counter()
+            objective, _, iterations, residual = dinkelbach_topk(w, c, 100)
+            timings.append(time.perf_counter() - start)
+            iterations_seen.append(iterations)
+            assert abs(residual) < 1e-9
+        rows.append(
+            {
+                "n": n,
+                "k": 100,
+                "median_seconds_algorithm_only": float(np.median(timings)),
+                "min_seconds": float(np.min(timings)),
+                "max_seconds": float(np.max(timings)),
+                "max_iterations": max(iterations_seen),
+                "objective_last_repeat": objective,
+            }
+        )
+    assert rows[-1]["median_seconds_algorithm_only"] < 10.0
+    assert max(row["max_iterations"] for row in rows) < 20
+    return {
+        "implementation": "independent NumPy argpartition top-k (expected O(n) per update)",
+        "repeats_per_size": 5,
+        "rows": rows,
+        "largest_n": rows[-1]["n"],
+        "largest_n_median_seconds": rows[-1]["median_seconds_algorithm_only"],
+        "max_iterations": max(row["max_iterations"] for row in rows),
+    }
+
+
+def recovery_rate(
+    rng: np.random.Generator,
+    n: int,
+    k: int,
+    error_amplitude: float,
+    trials: int,
+    gap: float,
+) -> float:
+    """Exact top-k set recovery with uniformly bounded generated-score error."""
+
+    oracle_w = np.full(n, 1.0 - gap, dtype=np.float64)
+    oracle_w[:k] = 1.0
+    c = np.ones(n, dtype=np.float64)
+    oracle = frozenset(range(k))
+    hits = 0
+    for _ in range(trials):
+        error = rng.uniform(-error_amplitude, error_amplitude, size=n)
+        _, chosen, _, _ = dinkelbach_topk(oracle_w + error, c, k)
+        hits += frozenset(chosen) == oracle
+    return hits / trials
+
+
+def separation_audit(rng: np.random.Generator) -> dict:
+    gap = 0.30
+    n = 200
+    k = 10
+    trials = 300
+    fractions = (0.10, 0.25, 0.49, 0.75, 1.25)
+    margin_rows = []
+    for fraction in fractions:
+        amplitude = fraction * gap
+        rate = recovery_rate(rng, n, k, amplitude, trials, gap)
+        margin_rows.append(
+            {
+                "uniform_error_as_fraction_of_gap": fraction,
+                "uniform_error_amplitude": amplitude,
+                "exact_set_recovery_rate": rate,
+            }
+        )
+
+    # A standard orthogonality diagnostic: if nuisance error is n^-1/4,
+    # first-order score error has that rate, while a Neyman-orthogonal score's
+    # leading product error has the squared n^-1/2 rate.  This is a finite
+    # generated-score stress test of the theorem's separation mechanism, not
+    # a replacement for its asymptotic proof.
+    sample_sizes = (100, 400, 1_600, 6_400)
+    rate_rows = []
+    constant = 2.0
+    for sample_n in sample_sizes:
+        orthogonal_error = constant * sample_n ** -0.5
+        first_order_error = constant * sample_n ** -0.25
+        orthogonal_recovery = recovery_rate(
+            rng, n, k, orthogonal_error, trials, gap
+        )
+        first_order_recovery = recovery_rate(
+            rng, n, k, first_order_error, trials, gap
+        )
+        rate_rows.append(
+            {
+                "sample_size": sample_n,
+                "orthogonal_product_error_n^-1/2": orthogonal_error,
+                "orthogonal_exact_recovery_rate": orthogonal_recovery,
+                "first_order_error_n^-1/4": first_order_error,
+                "first_order_exact_recovery_rate": first_order_recovery,
+            }
+        )
+
+    assert all(row["exact_set_recovery_rate"] == 1.0 for row in margin_rows[:3])
+    assert margin_rows[-1]["exact_set_recovery_rate"] < 0.95
+    assert rate_rows[-1]["orthogonal_exact_recovery_rate"] == 1.0
+    assert rate_rows[-1]["orthogonal_exact_recovery_rate"] > rate_rows[-1]["first_order_exact_recovery_rate"]
+    return {
+        "generated_score_dimension": n,
+        "selected_k": k,
+        "oracle_kth_to_kplus1_gap": gap,
+        "trials_per_cell": trials,
+        "bounded_margin_sweep": margin_rows,
+        "orthogonal_vs_first_order_rate_sweep": rate_rows,
+        "interpretation": (
+            "Finite generated-score certificate: errors below half the unique "
+            "top-k gap preserve the exact set. The n^-1/2 product-error sweep "
+            "models the separation mechanism enabled by Neyman orthogonality; "
+            "it is not claimed as a universal empirical proof of the theorem."
+        ),
+    }
+
+
+def negative_control() -> dict:
+    caught = False
+    message = ""
+    try:
+        dinkelbach_topk(np.array([100.0, 0.0, 0.0]), np.array([1.0, 0.0, 0.0]), 1)
+    except ValueError as exc:
+        caught = True
+        message = str(exc)
+    assert caught and "non-positive" in message
+    return {
+        "rank_deficient_deletion_rejected": caught,
+        "message": message,
+        "purpose": "checks the positive-denominator assumption rather than silently returning a ratio",
+    }
+
+
+def make_figure(summary: dict, path: Path) -> None:
+    fig, axes = plt.subplots(1, 3, figsize=(14.4, 4.25))
+
+    exact = summary["claim_1_exact_global_sets"]
+    exact_plot_values = [
+        max(exact["max_objective_error_vs_exhaustive"], 1e-18),
+        max(exact["max_delete_refit_identity_error"], 1e-18),
+    ]
+    axes[0].bar(
+        ["objective", "delete/refit"],
+        exact_plot_values,
+        color=["#4f46e5", "#0d9488"],
+    )
+    axes[0].set_yscale("log")
+    axes[0].set_ylabel("maximum absolute error")
+    axes[0].set_title(f"{exact['random_instances']} exhaustive instances")
+    axes[0].grid(axis="y", alpha=0.25)
+
+    scale = summary["claim_2_linear_topk_and_termination"]["rows"]
+    axes[1].plot(
+        [row["n"] for row in scale],
+        [row["median_seconds_algorithm_only"] for row in scale],
+        marker="o",
+        color="#dc2626",
+        linewidth=2,
+    )
+    axes[1].set_xscale("log")
+    axes[1].set_yscale("log")
+    axes[1].set_xlabel("n")
+    axes[1].set_ylabel("median seconds")
+    axes[1].set_title("Expected-linear top-k scaling")
+    axes[1].grid(alpha=0.25)
+
+    sweep = summary["claim_3_selection_separation"]["bounded_margin_sweep"]
+    axes[2].plot(
+        [row["uniform_error_as_fraction_of_gap"] for row in sweep],
+        [row["exact_set_recovery_rate"] for row in sweep],
+        marker="o",
+        color="#7c3aed",
+        linewidth=2,
+    )
+    axes[2].axvline(0.5, color="#111827", linestyle="--", linewidth=1, label="half-gap bound")
+    axes[2].set_ylim(-0.03, 1.04)
+    axes[2].set_xlabel("uniform score error / oracle gap")
+    axes[2].set_ylabel("exact set recovery")
+    axes[2].set_title("Separation stress test")
+    axes[2].legend(frameon=False)
+    axes[2].grid(alpha=0.25)
+
+    fig.suptitle("Finding Most Influential Sets — independent exact audit", fontsize=14, fontweight="bold")
+    fig.tight_layout()
+    fig.savefig(path, dpi=180, bbox_inches="tight")
+    plt.close(fig)
+
+
+def main() -> None:
+    rng = np.random.default_rng(SEED)
+    started = time.perf_counter()
+    summary = {
+        "paper": {
+            "title": "Finding Most Influential Sets",
+            "openreview_id": "ghd0zmtpB9",
+            "arxiv_id": "2606.05919",
+        },
+        "audit": {
+            "implementation": "independent Python/NumPy; no author code imported",
+            "seed": SEED,
+            "scope": "finite univariate residualized OLS identity, fractional top-k algorithm, and generated-score separation",
+        },
+        "claim_1_exact_global_sets": exact_audit(rng),
+        "claim_1_generic_linear_fractional_reduction": generic_fractional_audit(rng),
+        "claim_2_linear_topk_and_termination": scaling_audit(rng),
+        "claim_3_selection_separation": separation_audit(rng),
+        "negative_control": negative_control(),
+    }
+    summary["audit"]["wall_seconds"] = time.perf_counter() - started
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    results_path = OUTPUT_DIR / "influential_sets_results.json"
+    figure_path = OUTPUT_DIR / "influential_sets_audit.png"
+    results_path.write_text(json.dumps(summary, indent=2) + "\n")
+    make_figure(summary, figure_path)
+    print(json.dumps(summary, indent=2))
+    print(f"\nWrote {results_path.name} and {figure_path.name}")
+
+
+if __name__ == "__main__":
+    main()

index.html

@@ -0,0 +1,54 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Reproduction: Finding Most Influential Sets</title>
+    <link rel="stylesheet" href="./logbook.css" />
+  </head>
+  <body>
+    <div id="app">
+      <aside id="sidebar">
+        <div id="book-head">
+          <img id="book-wordmark" src="./trackio-wordmark-dark.png" alt="" />
+          <div id="book-title" class="sr-only">Logbook</div>
+        </div>
+        <nav id="tree"></nav>
+        <div id="sidebar-foot" hidden>
+          <button id="connect-btn" type="button">
+            <span class="ico">ⓘ</span> Collaborate with your agent
+          </button>
+        </div>
+      </aside>
+      <main id="content">
+        <div id="page"></div>
+      </main>
+    </div>
+
+    <div id="modal" hidden>
+      <div class="modal-backdrop"></div>
+      <div class="modal-card" role="dialog" aria-modal="true">
+        <div class="modal-head">
+          <div class="modal-title">
+            <img class="modal-logo" src="./trackio-logo.png" alt="" />
+            Collaborate with your agent
+          </div>
+          <div class="modal-actions">
+            <button id="copy-agent" class="btn">Copy for agent</button>
+            <button id="modal-close" class="btn icon" aria-label="Close">×</button>
+          </div>
+        </div>
+        <div class="modal-body">
+          <p class="modal-intro">
+            Point your coding agent at this logbook. It reads a compact,
+            token-efficient version — and if you've given it write access to this
+            Space, it can add findings that sync back automatically.
+          </p>
+          <ol id="connect-steps"></ol>
+        </div>
+      </div>
+    </div>
+
+    <script src="./logbook.js"></script>
+  </body>
+</html>

logbook.css

@@ -0,0 +1,1602 @@
+:root {
+  --bg: #ffffff;
+  --paper: #fdfcf9;
+  --panel: #ffffff;
+  --ink: #1f2937;
+  --muted: #6b7280;
+  --line: #e5e7eb;
+  --accent: #f97316;
+  --accent-strong: #ea580c;
+  --accent-soft: #fff7ed;
+  --accent-line: rgba(249, 115, 22, 0.16);
+  --grid-line: rgba(31, 41, 55, 0.045);
+  --code-bg: #f3f4f6;
+  --radius: 12px;
+  --serif: ui-serif, "Iowan Old Style", "Palatino Linotype", Georgia, serif;
+  --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial,
+    sans-serif;
+  --mono: "SFMono-Regular", "Cascadia Mono", "JetBrains Mono", Menlo, Consolas,
+    ui-monospace, monospace;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+html,
+body {
+  margin: 0;
+  padding: 0;
+}
+
+html {
+  scroll-behavior: smooth;
+}
+
+body {
+  background: var(--bg);
+  color: var(--ink);
+  font-family: var(--sans);
+  font-size: 13px;
+  line-height: 1.65;
+  -webkit-font-smoothing: antialiased;
+}
+
+#app {
+  display: flex;
+  min-height: 100vh;
+}
+
+/* ---- sidebar (composition-book cover) ---- */
+#sidebar {
+  width: 280px;
+  flex: 0 0 280px;
+  background: #17181c;
+  color: #e7e7ea;
+  position: sticky;
+  top: 0;
+  height: 100vh;
+  overflow-y: auto;
+  padding: 22px 16px;
+  display: flex;
+  flex-direction: column;
+}
+
+#book-head {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  padding: 8px;
+  margin-bottom: 12px;
+  border-radius: 10px;
+  cursor: pointer;
+  transition: background 0.12s;
+}
+#book-head:hover {
+  background: rgba(255, 255, 255, 0.05);
+}
+#book-wordmark {
+  width: 154px;
+  height: auto;
+  object-fit: contain;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border: 0;
+}
+
+#tree {
+  flex: 1;
+  padding-top: 8px;
+}
+
+#tree a {
+  display: block;
+  padding: 6px 10px;
+  border-radius: 8px;
+  color: #c3c4cb;
+  text-decoration: none;
+  font-size: 14px;
+  transition: background 0.12s, color 0.12s;
+}
+
+#tree a:hover {
+  background: rgba(255, 255, 255, 0.06);
+  color: #ffffff;
+}
+
+#tree a.active {
+  background: rgba(249, 115, 22, 0.16);
+  color: #fdba74;
+  font-weight: 600;
+}
+
+#tree a .tree-mark {
+  color: #6b6d76;
+}
+
+#tree a:hover .tree-mark,
+#tree a.active .tree-mark {
+  color: inherit;
+  opacity: 0.6;
+}
+
+#tree .depth-1 {
+  padding-left: 22px;
+}
+#tree .depth-2 {
+  padding-left: 34px;
+}
+#tree .depth-3 {
+  padding-left: 46px;
+}
+
+
+/* ---- content ---- */
+#content {
+  flex: 1;
+  min-width: 0;
+  padding: 48px 40px 120px;
+  background-color: var(--paper);
+  background-image:
+    linear-gradient(var(--grid-line) 1px, transparent 1px),
+    linear-gradient(90deg, var(--grid-line) 1px, transparent 1px);
+  background-size: 26px 26px;
+  background-position: center top;
+}
+
+#page {
+  width: 100%;
+  min-width: 0;
+  max-width: 1052px;
+  margin: 0 auto;
+}
+
+.page-section {
+  scroll-margin-top: 40px;
+  padding: 0 0 35px;
+  margin: 0 0 32px;
+}
+
+.page-section:last-child {
+  margin-bottom: 0;
+}
+
+.page-layout {
+  display: grid;
+  grid-template-columns: minmax(0, 760px) 248px;
+  gap: 44px;
+  align-items: start;
+}
+
+.page-body {
+  min-width: 0;
+}
+
+.resource-anchor {
+  display: block;
+  height: 0;
+  overflow: hidden;
+}
+
+/* ---- pinned notes ---- */
+.pinned-notes {
+  margin: 30px 0 0;
+}
+.pinned-notes-list .cell {
+  margin: 0;
+  border-color: rgba(249, 115, 22, 0.55);
+}
+.pinned-notes-list .cell + .cell {
+  margin-top: 12px;
+}
+.cell.pinned-source {
+  border-color: rgba(249, 115, 22, 0.55);
+}
+.book-intro.has-pinned-notes {
+  border-bottom: none;
+  padding-bottom: 22px;
+  margin-bottom: 30px;
+}
+.book-intro.book-intro-tight {
+  border-bottom: none;
+  padding-bottom: 4px;
+  margin-bottom: 20px;
+}
+
+#page h1 {
+  font-family: var(--serif);
+  font-size: 34px;
+  line-height: 1.15;
+  letter-spacing: -0.02em;
+  margin: 0 0 8px;
+  overflow-wrap: anywhere;
+}
+
+#page .page-section:not(.book-intro) h1 {
+  font-size: 26px;
+}
+
+#page h2 {
+  font-family: var(--serif);
+  font-size: 24px;
+  margin: 36px 0 10px;
+}
+
+#page h3 {
+  font-size: 17px;
+  font-weight: 700;
+  margin: 26px 0 2px;
+  letter-spacing: -0.01em;
+}
+
+#page h3::before {
+  content: "";
+  display: inline-block;
+  width: 7px;
+  height: 7px;
+  border-radius: 2px;
+  background: var(--accent);
+  margin-right: 10px;
+  vertical-align: middle;
+  transform: translateY(-1px);
+}
+
+#page p {
+  margin: 10px 0;
+}
+
+#page blockquote {
+  margin: 14px 0;
+  padding: 2px 16px;
+  border-left: 3px solid #fdba74;
+  color: var(--muted);
+}
+
+#page hr {
+  display: none;
+}
+
+#page code {
+  font-family: var(--mono);
+  font-size: 0.86em;
+  background: var(--code-bg);
+  padding: 2px 6px;
+  border-radius: 6px;
+}
+
+#page pre {
+  max-width: 100%;
+  background: var(--code-bg);
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  padding: 14px 16px;
+  overflow-x: auto;
+}
+#page pre code {
+  background: none;
+  padding: 0;
+  font-size: 11.5px;
+}
+
+/* ---- code blocks + collapsible accordion ---- */
+#page pre.hl {
+  background: #17181c;
+  border: none;
+  color: #e7e7ea;
+  font-size: 13px;
+  line-height: 1.58;
+}
+#page pre.hl code {
+  color: inherit;
+  font-family: var(--mono);
+}
+.code-accordion {
+  border: 1px solid rgba(249, 115, 22, 0.2);
+  border-radius: 8px;
+  overflow: hidden;
+  margin: 12px 0;
+  background: #17181c;
+}
+.code-accordion summary {
+  list-style: none;
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  gap: 9px;
+  padding: 9px 12px;
+  font-family: var(--mono);
+  font-size: 11.5px;
+  font-weight: 700;
+  color: #e7e7ea;
+  background: #1e2027;
+  user-select: none;
+  overflow-wrap: anywhere;
+}
+.code-accordion summary::-webkit-details-marker {
+  display: none;
+}
+.code-accordion summary::after {
+  content: "▸";
+  margin-left: auto;
+  color: var(--accent);
+  transition: transform 0.12s;
+  transform: rotate(180deg);
+}
+.code-accordion[open] summary::after {
+  transform: rotate(90deg);
+}
+.code-accordion .code-ico {
+  color: var(--accent);
+  font-weight: 700;
+}
+.code-accordion pre.hl {
+  margin: 0;
+  border-radius: 0;
+  border: none;
+  border-top: 1px solid rgba(249, 115, 22, 0.16);
+}
+.tok-comment {
+  color: #7a7d87;
+  font-style: italic;
+}
+.tok-string {
+  color: #a5d6a7;
+}
+.tok-keyword {
+  color: #fdba74;
+}
+.tok-number {
+  color: #7fd0e0;
+}
+
+#page a {
+  color: var(--accent);
+}
+
+#page ul {
+  padding-left: 20px;
+}
+
+.ts {
+  font-family: var(--mono);
+  font-size: 12px;
+  color: var(--muted);
+  background: none;
+  padding: 0;
+}
+
+/* ---- notebook-style cells ---- */
+.cell {
+  max-width: 100%;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  background: rgba(255, 255, 255, 0.86);
+  margin: 18px 0;
+  overflow: hidden;
+  box-shadow: 0 2px 10px rgba(31, 41, 55, 0.035);
+}
+.cell-head {
+  display: flex;
+  justify-content: space-between;
+  gap: 16px;
+  align-items: center;
+  padding: 14px 18px;
+  background: rgba(255, 255, 255, 0.92);
+  border-bottom: 1px solid var(--line);
+}
+.cell-head.no-title {
+  justify-content: flex-end;
+  padding-top: 10px;
+  padding-bottom: 10px;
+}
+.cell-title {
+  flex: 1;
+  min-width: 0;
+  font-size: 13px;
+  font-weight: 650;
+  color: var(--ink);
+  line-height: 1.35;
+  overflow-wrap: anywhere;
+}
+.cell-meta {
+  flex: 0 0 auto;
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  font-family: var(--sans);
+  font-size: 13px;
+  color: var(--muted);
+}
+.cell-open {
+  flex: 0 0 auto;
+  font-family: var(--mono);
+  font-size: 12px;
+  color: var(--accent);
+  text-decoration: none;
+}
+.cell-open:hover {
+  color: var(--accent-strong);
+}
+.cell-body {
+  min-width: 0;
+  padding: 14px 18px 18px;
+}
+.cell.dashboard .cell-body {
+  padding: 0;
+}
+#page .cell-body h1,
+#page .cell-body h2 {
+  font-family: var(--sans);
+  font-size: 17px;
+  font-weight: 700;
+  letter-spacing: -0.01em;
+  line-height: 1.35;
+  margin: 22px 0 6px;
+}
+#page .cell-body > :first-child {
+  margin-top: 0;
+}
+#page .cell-body > :last-child {
+  margin-bottom: 0;
+}
+.cell.code .cell-head {
+  background: #fbfbfc;
+}
+.figure-fit {
+  position: relative;
+  overflow: hidden;
+  min-height: 160px;
+  border: 1px solid var(--line);
+  border-radius: 8px;
+  background: #fff;
+}
+.figure-fit[hidden] {
+  display: none;
+}
+.figure-fit:fullscreen,
+.figure-fit:-webkit-full-screen {
+  width: 100%;
+  height: 100%;
+  border: none;
+  border-radius: 0;
+}
+.figure-frame {
+  display: block;
+  width: 100%;
+  min-height: 160px;
+  border: none;
+  background: #fff;
+}
+.figure-frame[hidden],
+.figure-raw[hidden] {
+  display: none;
+}
+.fig-switch {
+  position: relative;
+  display: inline-flex;
+  flex: 0 0 auto;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: var(--code-bg);
+  padding: 2px;
+}
+.fig-switch button {
+  position: relative;
+  z-index: 1;
+  flex: 1;
+  min-width: 62px;
+  border: none;
+  background: none;
+  font-family: var(--sans);
+  font-size: 12px;
+  font-weight: 600;
+  color: var(--muted);
+  padding: 3px 12px;
+  border-radius: 999px;
+  cursor: pointer;
+  transition: color 0.15s;
+}
+.fig-switch button.active {
+  color: var(--accent-strong);
+}
+.fig-switch-thumb {
+  position: absolute;
+  top: 2px;
+  bottom: 2px;
+  left: 2px;
+  width: calc(50% - 2px);
+  border-radius: 999px;
+  background: var(--panel);
+  border: 1px solid rgba(249, 115, 22, 0.35);
+  box-shadow: 0 1px 4px rgba(31, 41, 55, 0.08);
+  transition: transform 0.18s ease;
+}
+.fig-switch.raw .fig-switch-thumb {
+  transform: translateX(100%);
+}
+#page .figure-raw pre {
+  margin: 0;
+  max-height: 420px;
+  overflow: auto;
+  font-family: var(--mono);
+  font-size: 13px;
+  line-height: 1.55;
+  background: var(--code-bg);
+  border: 1px solid var(--line);
+  border-radius: 8px;
+  padding: 12px 14px;
+}
+/* ---- figure fullscreen ---- */
+.cell-fullscreen {
+  position: relative;
+  display: inline-flex;
+  flex: 0 0 auto;
+}
+.cell-fullscreen-btn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  width: 26px;
+  height: 26px;
+  padding: 0;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: var(--code-bg);
+  color: var(--muted);
+  cursor: pointer;
+  transition: color 0.15s, border-color 0.15s, background 0.15s;
+}
+.cell-fullscreen-btn:hover {
+  color: var(--accent-strong);
+  border-color: rgba(249, 115, 22, 0.35);
+  background: var(--accent-soft);
+}
+.cell-fullscreen-btn svg {
+  width: 14px;
+  height: 14px;
+}
+/* ---- copyable snippets ---- */
+.snippet {
+  position: relative;
+}
+.copy-snippet {
+  position: absolute;
+  top: 7px;
+  right: 8px;
+  width: 24px;
+  height: 24px;
+  border: none;
+  border-radius: 6px;
+  background: rgba(255, 255, 255, 0.08);
+  color: #9a9da8;
+  font-size: 12px;
+  line-height: 1;
+  cursor: pointer;
+  opacity: 0;
+  transition: opacity 0.12s, color 0.12s, background 0.12s;
+}
+.snippet:hover .copy-snippet,
+.jp-out:hover .copy-snippet,
+.figure-raw:hover .copy-snippet,
+.code-accordion summary:hover .copy-snippet {
+  opacity: 1;
+}
+.copy-snippet:hover {
+  color: #ffffff;
+  background: rgba(255, 255, 255, 0.16);
+}
+.copy-snippet.copied {
+  color: #52d08a;
+  opacity: 1;
+}
+.code-accordion .code-name {
+  user-select: text;
+  cursor: text;
+}
+.jp-out,
+.figure-raw {
+  position: relative;
+}
+.jp-out .copy-snippet,
+.figure-raw .copy-snippet {
+  background: var(--code-bg);
+  color: var(--muted);
+  border: 1px solid var(--line);
+}
+.jp-out .copy-snippet:hover,
+.figure-raw .copy-snippet:hover {
+  color: var(--accent-strong);
+  background: var(--panel);
+}
+
+/* ---- jupyter-style code cells ---- */
+.jp {
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  overflow: hidden;
+  margin: 12px 0;
+  background: var(--panel);
+}
+.jp-gutter {
+  flex: 0 0 46px;
+  padding: 13px 0 0 13px;
+  font-family: var(--mono);
+  font-size: 10.5px;
+  letter-spacing: 0.07em;
+  text-transform: uppercase;
+  font-weight: 600;
+  user-select: none;
+}
+.jp-in {
+  display: flex;
+  background: #17181c;
+}
+.jp-in .jp-gutter {
+  color: #6f727d;
+}
+.jp-in-body {
+  flex: 1;
+  min-width: 0;
+}
+#page .jp-in-body pre.hl {
+  margin: 0;
+  border: none;
+  border-radius: 0;
+  background: none;
+  padding: 12px 16px 12px 0;
+}
+.jp-in-body .code-accordion {
+  margin: 0;
+  border: none;
+  border-top: 1px solid rgba(255, 255, 255, 0.09);
+  border-radius: 0;
+  background: none;
+}
+.jp-in-body .code-accordion summary {
+  background: none;
+  padding: 9px 16px 9px 0;
+}
+.jp-in-body .code-accordion pre.hl {
+  border-top: 1px solid rgba(255, 255, 255, 0.09);
+}
+.jp-meta {
+  padding: 5px 14px;
+  font-family: var(--mono);
+  font-size: 11.5px;
+  color: var(--muted);
+  background: #fbfbfc;
+  border-top: 1px solid var(--line);
+}
+.jp-out {
+  display: flex;
+  border-top: 1px solid var(--line);
+  background: var(--panel);
+}
+.jp-out .jp-gutter {
+  color: var(--accent-strong);
+}
+.jp-out-body {
+  flex: 1;
+  min-width: 0;
+}
+#page .jp-out-pre {
+  min-width: 0;
+  margin: 0;
+  border: none;
+  border-radius: 0;
+  background: none;
+  color: var(--ink);
+  font-family: var(--mono);
+  font-size: 13px;
+  line-height: 1.55;
+  padding: 12px 16px 12px 0;
+  white-space: pre;
+  overflow-x: auto;
+  overflow-y: auto;
+  max-height: 26em;
+}
+.jp-artifacts {
+  display: flex;
+  flex-direction: column;
+}
+.jp-out-body .jp-out-pre + .jp-artifacts {
+  border-top: 1px solid var(--line);
+}
+.out-artifact {
+  display: flex;
+  align-items: baseline;
+  gap: 8px;
+  padding: 9px 16px 9px 0;
+  text-decoration: none;
+  color: inherit;
+}
+.out-artifact + .out-artifact {
+  border-top: 1px solid var(--line);
+}
+a.out-artifact:hover .out-artifact-name {
+  color: var(--accent-strong);
+}
+.out-artifact-ico {
+  flex: 0 0 auto;
+  font-size: 13px;
+}
+.out-artifact-name {
+  font-family: var(--mono);
+  font-size: 12.5px;
+  font-weight: 600;
+  color: var(--ink);
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+.out-artifact-meta {
+  flex: 0 0 auto;
+  margin-left: auto;
+  padding-left: 12px;
+  font-size: 12px;
+  color: var(--muted);
+  white-space: nowrap;
+}
+.out-artifact-state.open {
+  color: var(--accent);
+  font-weight: 600;
+}
+.trackio-embed {
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  overflow: hidden;
+  background: var(--panel);
+}
+.trackio-cell-meta {
+  display: flex;
+  gap: 6px;
+  flex-wrap: wrap;
+  justify-content: flex-end;
+}
+
+/* ---- unfurl cards ---- */
+.unfurl {
+  display: block;
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  background: var(--panel);
+  margin: 12px 0;
+  overflow: hidden;
+  text-decoration: none;
+  color: inherit;
+  transition: border-color 0.14s, box-shadow 0.14s;
+}
+.unfurl:hover {
+  border-color: #cfcbe6;
+  box-shadow: 0 4px 18px rgba(30, 20, 80, 0.06);
+}
+
+.unfurl-body {
+  padding: 13px 16px;
+  display: flex;
+  gap: 12px;
+  align-items: flex-start;
+}
+
+.unfurl-ico {
+  font-size: 20px;
+  line-height: 1.3;
+  flex: 0 0 auto;
+}
+
+.unfurl-main {
+  min-width: 0;
+  flex: 1;
+}
+
+.unfurl-kind {
+  font-family: var(--mono);
+  font-size: 10.5px;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+  color: var(--accent);
+  font-weight: 600;
+}
+
+.unfurl-title {
+  font-weight: 650;
+  font-size: 15px;
+  margin: 1px 0 2px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.unfurl-desc {
+  color: var(--muted);
+  font-size: 13.5px;
+  line-height: 1.45;
+}
+
+.unfurl-meta {
+  margin-top: 6px;
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+}
+
+.chip {
+  font-size: 11.5px;
+  background: var(--code-bg);
+  border-radius: 999px;
+  padding: 2px 9px;
+  color: var(--muted);
+  font-family: var(--mono);
+}
+
+.unfurl-raw {
+  font-family: var(--mono);
+  font-size: 11px;
+  color: var(--muted);
+  border-top: 1px solid var(--line);
+  padding: 7px 16px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.unfurl.embed {
+  padding: 0;
+  overflow: hidden;
+}
+.embed-head {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  padding: 10px 14px;
+  border-bottom: 1px solid var(--line);
+}
+.embed-head .unfurl-kind {
+  flex: 0 0 auto;
+}
+.embed-title {
+  flex: 1;
+  min-width: 0;
+  font-weight: 650;
+  font-size: 14px;
+  color: var(--ink);
+  text-decoration: none;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+.embed-title:hover {
+  color: var(--accent);
+}
+.embed-open {
+  flex: 0 0 auto;
+  font-family: var(--mono);
+  font-size: 12px;
+  color: var(--accent);
+  text-decoration: none;
+}
+.embed-frame {
+  display: block;
+  width: 100%;
+  height: 560px;
+  border: 0;
+  background: var(--code-bg);
+}
+
+.dashboard-shell {
+  display: block;
+}
+.dashboard-shell .dashboard-frame {
+  display: block;
+  width: 100%;
+  height: 900px;
+  border: 0;
+  background: var(--code-bg);
+}
+
+.unfurl.image {
+  padding: 0;
+}
+.unfurl.image img {
+  display: block;
+  width: 100%;
+  height: auto;
+  max-height: 460px;
+  object-fit: contain;
+  background: var(--code-bg);
+}
+
+.artifact-chip {
+  border: 1px solid var(--line);
+  background: var(--panel);
+  border-radius: var(--radius);
+  padding: 10px 14px;
+  margin: 8px 0;
+  font-size: 14px;
+}
+.cell.dashboard .artifact-chip {
+  margin: 14px 18px 18px;
+}
+.artifact-chip code {
+  color: var(--accent);
+}
+
+/* ---- task board ---- */
+.board-wrap {
+  overflow-x: auto;
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  margin: 12px 0 20px;
+  background: var(--panel);
+}
+table.board {
+  border-collapse: collapse;
+  width: 100%;
+  font-size: 14px;
+}
+table.board th,
+table.board td {
+  text-align: left;
+  padding: 9px 14px;
+  border-bottom: 1px solid var(--line);
+  vertical-align: top;
+}
+table.board thead th {
+  background: var(--accent-soft);
+  font-size: 12px;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: #9a4a12;
+  font-weight: 600;
+  border-bottom: 1px solid var(--line);
+}
+table.board tbody tr:last-child td {
+  border-bottom: none;
+}
+table.board .col-check {
+  text-align: center;
+  width: 92px;
+  white-space: nowrap;
+}
+table.board tr.section-row td {
+  background: var(--accent-soft);
+  text-align: center;
+  font-weight: 700;
+  font-size: 13px;
+  color: var(--accent-strong);
+  padding: 7px 14px;
+  letter-spacing: 0.02em;
+}
+.box {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  width: 18px;
+  height: 18px;
+  border: 1.5px solid #cfcbe0;
+  border-radius: 5px;
+  font-size: 12px;
+  color: #fff;
+  line-height: 1;
+}
+.box.on {
+  background: var(--accent);
+  border-color: var(--accent);
+}
+.who-chip {
+  display: inline-block;
+  padding: 3px 12px;
+  border-radius: 999px;
+  font-size: 12.5px;
+  font-weight: 600;
+  white-space: nowrap;
+}
+.who-chip.muted {
+  background: var(--code-bg);
+  color: var(--muted);
+  font-weight: 500;
+}
+
+/* ---- status badges + clickable rows ---- */
+table.board .col-status {
+  width: 130px;
+  white-space: nowrap;
+}
+.badge {
+  display: inline-block;
+  padding: 3px 11px;
+  border-radius: 999px;
+  font-size: 12px;
+  font-weight: 600;
+  letter-spacing: 0.01em;
+}
+.badge.gray {
+  background: var(--code-bg);
+  color: var(--muted);
+}
+.badge.amber {
+  background: var(--accent-soft);
+  color: #b45309;
+}
+.badge.green {
+  background: #e6f7ee;
+  color: #1a8a55;
+}
+.badge.red {
+  background: #fde8ec;
+  color: #c62a4b;
+}
+table.board tr.linked-row {
+  cursor: pointer;
+}
+table.board tr.linked-row:hover td {
+  background: var(--accent-soft);
+}
+table.board tr.linked-row a {
+  color: var(--ink);
+  font-weight: 600;
+  text-decoration: none;
+}
+table.board tr.linked-row:hover a {
+  color: var(--accent-strong);
+}
+
+/* ---- agent read hint ---- */
+.agent-hint {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+  gap: 8px;
+  margin: 4px 0 22px;
+  font-size: 12.5px;
+  color: var(--muted);
+}
+#page .agent-hint code {
+  background: var(--code-bg);
+  padding: 2px 9px;
+  border-radius: 6px;
+  font-family: var(--mono);
+  font-size: 12px;
+  font-weight: 500;
+  color: var(--ink);
+}
+.agent-hint .copy {
+  flex: 0 0 auto;
+  background: none;
+  color: var(--muted);
+  border: 1px solid var(--line);
+  border-radius: 6px;
+  width: 22px;
+  height: 22px;
+  font-size: 11px;
+  line-height: 1;
+  cursor: pointer;
+  transition: color 0.12s, border-color 0.12s;
+}
+.agent-hint .copy:hover {
+  color: var(--accent-strong);
+  border-color: var(--accent);
+}
+.agent-hint .copy.copied {
+  color: #1a8a55;
+  border-color: #1a8a55;
+}
+.agent-hint-note {
+  margin-left: auto;
+  font-size: 12px;
+  color: var(--muted);
+}
+
+/* ---- logbook summary stats ---- */
+.logbook-stats {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 12px;
+  margin: 0 0 28px;
+}
+.stat-tile {
+  position: relative;
+  display: inline-flex;
+  align-items: center;
+  gap: 11px;
+  border: 1px solid var(--line);
+  background: var(--panel);
+  border-radius: var(--radius);
+  padding: 12px 23px;
+  font: inherit;
+  text-align: left;
+  cursor: pointer;
+  transition: border-color 0.12s, box-shadow 0.12s;
+}
+.stat-tile:hover:not([disabled]) {
+  border-color: rgba(249, 115, 22, 0.45);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.06);
+}
+.stat-tile:focus-visible {
+  outline: 2px solid var(--accent);
+  outline-offset: 2px;
+}
+.stat-tile[disabled] {
+  cursor: default;
+  opacity: 0.7;
+}
+.stat-tile.open {
+  border-color: rgba(249, 115, 22, 0.6);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.08);
+}
+.stat-icon {
+  width: 24px;
+  height: 24px;
+  flex: 0 0 24px;
+  object-fit: contain;
+  align-self: center;
+}
+.stat-text {
+  display: flex;
+  align-items: baseline;
+  gap: 8px;
+  white-space: nowrap;
+  line-height: 1;
+}
+.stat-num {
+  font-family: var(--mono);
+  font-size: 20px;
+  font-weight: 600;
+  line-height: 1;
+  color: var(--accent-strong);
+}
+.stat-label {
+  font-size: 15px;
+  line-height: 1;
+  color: var(--muted);
+}
+.stat-caret {
+  margin-left: 2px;
+  font-size: 10px;
+  color: var(--muted);
+  align-self: center;
+  transition: transform 0.12s;
+}
+.stat-tile.open .stat-caret {
+  transform: rotate(180deg);
+}
+.stat-popover {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  margin-top: 6px;
+  min-width: 300px;
+  max-width: min(460px, 92vw);
+  max-height: 340px;
+  overflow-y: auto;
+  z-index: 20;
+  background: var(--panel);
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  box-shadow: 0 8px 28px rgba(31, 41, 55, 0.12);
+  padding: 6px;
+}
+.stat-popover[hidden] {
+  display: none;
+}
+.stat-pop-head {
+  padding: 6px 10px 8px;
+  font-size: 11.5px;
+  font-weight: 700;
+  letter-spacing: 0.03em;
+  text-transform: uppercase;
+  color: var(--muted);
+}
+.stat-row {
+  display: flex;
+  align-items: flex-start;
+  gap: 10px;
+  padding: 9px 11px;
+  border-radius: 9px;
+  border: 1px solid transparent;
+  text-decoration: none;
+  color: inherit;
+  cursor: pointer;
+}
+.stat-row:hover {
+  border-color: rgba(249, 115, 22, 0.4);
+  background: var(--accent-soft);
+}
+.stat-row-ico {
+  font-size: 15px;
+  line-height: 1.3;
+  flex: 0 0 auto;
+}
+.stat-row-main {
+  min-width: 0;
+  flex: 1;
+}
+.stat-row-title {
+  font-family: var(--mono);
+  font-size: 12.5px;
+  font-weight: 600;
+  color: var(--ink);
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+.stat-row-meta {
+  margin-top: 2px;
+  font-size: 12px;
+  color: var(--muted);
+}
+.stat-row-state.open {
+  color: var(--accent);
+  font-weight: 600;
+  border-radius: 5px;
+  padding: 1px 5px;
+  margin: -1px -2px;
+}
+.stat-row-state.open:hover {
+  background: rgba(249, 115, 22, 0.14);
+  text-decoration: underline;
+}
+.art-ico {
+  width: 1em;
+  height: 1em;
+  object-fit: contain;
+  vertical-align: -0.15em;
+}
+
+/* ---- scroll-to-resource highlight ---- */
+.res-flash {
+  animation: res-flash 1.5s ease;
+  border-radius: 8px;
+}
+@keyframes res-flash {
+  0%,
+  25% {
+    box-shadow: 0 0 0 3px var(--accent);
+  }
+  100% {
+    box-shadow: 0 0 0 3px rgba(249, 115, 22, 0);
+  }
+}
+
+/* ---- inline resource chips ---- */
+#page .res-chip {
+  display: inline-flex;
+  align-items: center;
+  gap: 5px;
+  max-width: 100%;
+  padding: 0 9px 0 6px;
+  margin: 0 1px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: var(--panel);
+  font-family: var(--mono);
+  font-size: 0.78em;
+  font-weight: 600;
+  color: var(--ink);
+  text-decoration: none;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  vertical-align: middle;
+  line-height: 1.65;
+  transform: translateY(-0.08em);
+  transition: border-color 0.12s, background 0.12s, color 0.12s;
+}
+.res-chip-ico {
+  font-size: 1.05em;
+  line-height: 1;
+}
+#page .res-chip:hover,
+#page .res-chip.res-hl {
+  border-color: var(--accent);
+  background: var(--accent-soft);
+  color: var(--accent-strong);
+}
+#page a.res-link.res-hl {
+  background: var(--accent-soft);
+  border-radius: 4px;
+}
+.rail-item.res-hl {
+  border-color: var(--accent);
+  background: var(--accent-soft);
+  box-shadow: 0 3px 12px rgba(249, 115, 22, 0.14);
+}
+.rail-item.res-hl .rail-title {
+  color: var(--accent-strong);
+}
+.rail-item.rail-local {
+  cursor: default;
+}
+.artifact-chip.res-hl {
+  border-color: var(--accent);
+  background: var(--accent-soft);
+}
+
+/* ---- contextual resources rail ---- */
+.context-rail {
+  position: relative;
+  width: 248px;
+}
+.context-rail[hidden] {
+  display: none;
+}
+.rail-kind {
+  display: flex;
+  align-items: center;
+  gap: 5px;
+  font-family: var(--mono);
+  font-size: 10px;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+  font-weight: 600;
+  color: var(--accent);
+  margin-bottom: 4px;
+}
+.rail-item {
+  position: absolute;
+  left: 0;
+  right: 0;
+  display: block;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  background: var(--panel);
+  padding: 9px 12px;
+  margin-bottom: 8px;
+  text-decoration: none;
+  color: inherit;
+  transition: border-color 0.14s, box-shadow 0.14s;
+}
+.rail-item:hover {
+  border-color: rgba(249, 115, 22, 0.45);
+  box-shadow: 0 3px 12px rgba(31, 41, 55, 0.06);
+}
+.rail-title {
+  font-family: var(--mono);
+  font-size: 12.5px;
+  font-weight: 600;
+  color: var(--ink);
+  overflow-wrap: anywhere;
+  line-height: 1.4;
+}
+.rail-item:hover .rail-title {
+  color: var(--accent-strong);
+}
+.rail-meta {
+  font-size: 11.5px;
+  color: var(--muted);
+  margin-top: 2px;
+}
+
+@media (max-width: 1400px) {
+  .page-layout {
+    display: block;
+  }
+  .context-rail {
+    width: 100%;
+    margin-top: 28px;
+    position: static;
+    min-height: 0 !important;
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+    gap: 10px;
+  }
+  .context-rail[hidden] {
+    display: none;
+  }
+  .context-rail .rail-item {
+    position: static;
+    margin-bottom: 0;
+  }
+}
+
+/* ---- connect footer + modal ---- */
+#sidebar-foot {
+  margin-top: auto;
+  padding-top: 14px;
+  border-top: 1px solid rgba(255, 255, 255, 0.1);
+}
+
+#connect-btn {
+  width: 100%;
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  background: rgba(255, 255, 255, 0.05);
+  color: #c3c4cb;
+  border: 1px solid rgba(255, 255, 255, 0.12);
+  border-radius: 9px;
+  padding: 9px 12px;
+  font-size: 13.5px;
+  font-family: var(--sans);
+  cursor: pointer;
+  transition: background 0.12s, color 0.12s, border-color 0.12s;
+}
+#connect-btn:hover {
+  background: rgba(249, 115, 22, 0.14);
+  border-color: rgba(249, 115, 22, 0.4);
+  color: #fdba74;
+}
+#connect-btn .ico {
+  font-size: 15px;
+}
+
+#modal[hidden] {
+  display: none;
+}
+#modal {
+  position: fixed;
+  inset: 0;
+  z-index: 100;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 24px;
+}
+.modal-backdrop {
+  position: absolute;
+  inset: 0;
+  background: rgba(20, 18, 30, 0.5);
+  backdrop-filter: blur(2px);
+}
+.modal-card {
+  position: relative;
+  background: var(--panel);
+  border-radius: 16px;
+  width: 100%;
+  max-width: 620px;
+  max-height: 85vh;
+  overflow-y: auto;
+  box-shadow: 0 24px 70px rgba(20, 15, 50, 0.28);
+}
+.modal-head {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 12px;
+  padding: 18px 22px;
+  border-bottom: 1px solid var(--line);
+  position: sticky;
+  top: 0;
+  background: var(--panel);
+}
+.modal-title {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  font-family: var(--serif);
+  font-size: 21px;
+  letter-spacing: -0.01em;
+}
+.modal-logo {
+  width: 26px;
+  height: 26px;
+  object-fit: contain;
+}
+.modal-actions {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+.btn {
+  font-family: var(--sans);
+  font-size: 13.5px;
+  font-weight: 600;
+  border: 1px solid var(--line);
+  background: var(--panel);
+  color: var(--ink);
+  border-radius: 9px;
+  padding: 8px 13px;
+  cursor: pointer;
+  transition: background 0.12s, border-color 0.12s, color 0.12s;
+}
+.btn:hover {
+  border-color: var(--accent);
+  color: var(--accent-strong);
+}
+.btn.copied {
+  border-color: #1a8a55;
+  color: #1a8a55;
+}
+.btn.icon {
+  font-size: 18px;
+  line-height: 1;
+  padding: 6px 11px;
+  font-weight: 400;
+}
+.modal-body {
+  padding: 20px 22px 26px;
+}
+.modal-intro {
+  margin: 0 0 20px;
+  color: var(--muted);
+  line-height: 1.55;
+}
+#connect-steps {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+}
+#connect-steps li {
+  margin-bottom: 18px;
+}
+.step-title {
+  font-weight: 600;
+  font-size: 14.5px;
+  margin-bottom: 8px;
+}
+.codeblock {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  background: #17181c;
+  border-radius: 10px;
+  padding: 11px 12px 11px 15px;
+}
+.codeblock code {
+  flex: 1;
+  min-width: 0;
+  overflow-x: auto;
+  white-space: nowrap;
+  font-family: var(--mono);
+  font-size: 13px;
+  color: #f0efff;
+  background: none;
+  padding: 0;
+}
+.codeblock .copy {
+  flex: 0 0 auto;
+  background: rgba(255, 255, 255, 0.08);
+  color: #c3c4cb;
+  border: 1px solid rgba(255, 255, 255, 0.14);
+  border-radius: 7px;
+  width: 30px;
+  height: 30px;
+  font-size: 14px;
+  cursor: pointer;
+  transition: background 0.12s, color 0.12s;
+}
+.codeblock .copy:hover {
+  background: rgba(249, 115, 22, 0.2);
+  color: #fdba74;
+}
+.codeblock .copy.copied {
+  color: #52d08a;
+}
+
+@media (max-width: 720px) {
+  #app {
+    flex-direction: column;
+  }
+  #sidebar {
+    width: 100%;
+    flex: none;
+    height: auto;
+    position: static;
+  }
+  #content {
+    display: block;
+    width: 100%;
+    padding: 28px 20px 80px;
+    overflow-x: hidden;
+  }
+  #page {
+    width: 100%;
+    max-width: 100%;
+  }
+  #page h1 {
+    font-size: 30px;
+  }
+  .cell-head {
+    align-items: flex-start;
+    flex-direction: column;
+    gap: 4px;
+  }
+}

logbook.js

@@ -0,0 +1,2275 @@
+(function () {
+  "use strict";
+
+  let MANIFEST = null;
+  const PAGE_CACHE = {};
+  const UNFURL_CACHE = {};
+  const LIVE_RELOAD_MS = 1500;
+  const FIGURE_FRAME_WINDOWS = new Set();
+  let FIGURE_NAVIGATION_READY = false;
+
+  function esc(s) {
+    return String(s)
+      .replace(/&/g, "&")
+      .replace(/</g, "&lt;")
+      .replace(/>/g, "&gt;")
+      .replace(/"/g, "&quot;")
+      .replace(/'/g, "&#39;");
+  }
+
+  function flattenTree(node, depth, acc) {
+    acc.push({ node: node, depth: depth });
+    (node.children || []).forEach((c) => flattenTree(c, depth + 1, acc));
+    return acc;
+  }
+
+  function findNode(node, slug) {
+    if (node.slug === slug) return node;
+    for (const c of node.children || []) {
+      const hit = findNode(c, slug);
+      if (hit) return hit;
+    }
+    return null;
+  }
+
+  /* -------------------- minimal markdown -------------------- */
+
+  function inline(text) {
+    let t = esc(text);
+    t = t.replace(/`([^`]+)`/g, (_, c) => `<code>${c}</code>`);
+    t = t.replace(/\*\*([^*]+)\*\*/g, (_, c) => `<strong>${c}</strong>`);
+    t = t.replace(/\[([^\]]+)\]\(([^)]+)\)/g, (_, txt, url) => {
+      const safe = esc(url);
+      const attrs = /^https?:/.test(url) ? ' target="_blank" rel="noopener"' : "";
+      const item = /^https?:/.test(url) ? classifyResource(url) : null;
+      const data = item
+        ? ` class="res-link" data-res-url="${esc(item.url)}"`
+        : "";
+      return `<a href="${safe}"${attrs}${data}>${txt}</a>`;
+    });
+    t = t.replace(/(^|[\s(])(https?:\/\/[^\s<>)"'`]+)/g, (m, pre, url) => {
+      let rest = "";
+      const cut = url.search(/&quot;|&#39;|&lt;|&gt;/);
+      if (cut !== -1) {
+        rest = url.slice(cut);
+        url = url.slice(0, cut);
+      }
+      const trailing = (url.match(/[.,;:!?`]+$/) || [""])[0];
+      const clean = trailing ? url.slice(0, -trailing.length) : url;
+      if (!clean) return m;
+      const item = classifyResource(clean);
+      if (item) return `${pre}${resChipHtml(item)}${trailing}${rest}`;
+      return `${pre}<a href="${clean}" target="_blank" rel="noopener">${clean}</a>${trailing}${rest}`;
+    });
+    return t;
+  }
+
+  function resChipHtml(item) {
+    return (
+      `<a class="res-chip" href="${esc(item.url)}" target="_blank" ` +
+      `rel="noopener" data-res-url="${esc(item.url)}">` +
+      `<span class="res-chip-ico">${RESOURCE_ICONS[item.kind]}</span>` +
+      `${esc(item.id)}</a>`
+    );
+  }
+
+  const URL_ONLY = /^(https?:\/\/[^\s]+)$/;
+  const DETECTED_URL =
+    /(https?:\/\/[^\s<>)\]"'`]+|trackio-local-dashboard:\/\/[^\s<>)\]"'`]+|trackio-artifact:\/\/[^\s<>)\]"'`]+|trackio-local-path:\/\/[^\s<>)\]"'`]+)/g;
+
+  function renderMarkdown(md, container) {
+    const cellRe = /(^|\n)---\n<!-- trackio-cell\n([\s\S]*?)\n-->\n([\s\S]*?)(?=\n---\n<!-- trackio-cell\n|\s*$)/g;
+    const tokens = [];
+    let pos = 0;
+    let found = false;
+    let match;
+    while ((match = cellRe.exec(md))) {
+      found = true;
+      tokens.push({
+        kind: "md",
+        text: md.slice(pos, match.index + match[1].length),
+      });
+      tokens.push({
+        kind: "cell",
+        meta: parseCellMeta(match[2]),
+        body: match[3],
+      });
+      pos = match.index + match[0].length;
+    }
+    tokens.push({ kind: "md", text: found ? md.slice(pos) : md });
+
+    for (let i = 0; i < tokens.length; i++) {
+      const t = tokens[i];
+      if (t.kind === "md") {
+        renderMarkdownPlain(t.text, container);
+        continue;
+      }
+      if (t.consumed) continue;
+      if (t.meta.type === "code") {
+        const arts = [];
+        for (let j = i + 1; j < tokens.length; j++) {
+          const n = tokens[j];
+          if (n.kind === "md") {
+            if (n.text.trim() === "") continue;
+            break;
+          }
+          if (n.meta.type === "artifact") {
+            arts.push(n);
+            n.consumed = true;
+            continue;
+          }
+          break;
+        }
+        renderCell(t.meta, t.body, container, arts);
+      } else {
+        renderCell(t.meta, t.body, container);
+      }
+    }
+  }
+
+  function parseCellMeta(raw) {
+    try {
+      return JSON.parse(raw);
+    } catch (e) {
+      return { type: "markdown", title: "Note" };
+    }
+  }
+
+  function renderMarkdownPlain(md, container) {
+    const lines = md.replace(/<!--[\s\S]*?-->/g, "").split("\n");
+    let i = 0;
+    let para = [];
+
+    function flushPara() {
+      if (!para.length) return;
+      const joined = para.join(" ").trim();
+      para = [];
+      if (!joined) return;
+      if (/^trackio-artifact:\/\/\S+$/.test(joined)) return;
+      if (/^trackio-local-path:\/\/\S+$/.test(joined)) return;
+      if (joined.indexOf("📦 Artifact") !== -1) {
+        const div = document.createElement("div");
+        div.className = "artifact-chip";
+        div.innerHTML = ARTIFACT_ICON_IMG + inline(joined.replace(/📦\s*/, ""));
+        container.appendChild(div);
+        return;
+      }
+      if (URL_ONLY.test(joined) || IMG_PATH.test(joined)) {
+        const el = renderStandaloneUrl(joined);
+        if (el) container.appendChild(el);
+        return;
+      }
+      const p = document.createElement("p");
+      p.innerHTML = inline(joined);
+      container.appendChild(p);
+    }
+
+    while (i < lines.length) {
+      const line = lines[i];
+      const trimmed = line.trim();
+
+      if (trimmed === "") {
+        flushPara();
+        i++;
+        continue;
+      }
+      const fence = trimmed.match(/^(`{3,}|~{3,})(.*)$/);
+      if (fence) {
+        flushPara();
+        const marker = fence[1][0];
+        const closeRe = new RegExp("^" + marker + "{" + fence[1].length + ",}\\s*$");
+        const info = fence[2].trim();
+        const buf = [];
+        i++;
+        while (i < lines.length && !closeRe.test(lines[i].trim())) {
+          buf.push(lines[i]);
+          i++;
+        }
+        i++;
+        const lang = (info.split(/\s+/)[0] || "").toLowerCase();
+        const tm = info.match(/title=(\S+)/);
+        container.appendChild(
+          renderCode(buf.join("\n"), lang, tm ? tm[1] : null)
+        );
+        continue;
+      }
+      if (trimmed === "---") {
+        flushPara();
+        container.appendChild(document.createElement("hr"));
+        i++;
+        continue;
+      }
+      const h = trimmed.match(/^(#{1,4})\s+(.*)$/);
+      if (h) {
+        flushPara();
+        const el = document.createElement("h" + h[1].length);
+        el.innerHTML = inline(h[2]);
+        container.appendChild(el);
+        i++;
+        continue;
+      }
+      if (
+        trimmed.startsWith("|") &&
+        i + 1 < lines.length &&
+        /^\|?[\s:|-]*-{2,}[\s:|-]*\|?$/.test(lines[i + 1].trim())
+      ) {
+        flushPara();
+        const rows = [];
+        while (i < lines.length && lines[i].trim().startsWith("|")) {
+          rows.push(parseRow(lines[i].trim()));
+          i++;
+        }
+        renderTable(rows, container);
+        continue;
+      }
+      if (trimmed.startsWith("> ")) {
+        flushPara();
+        const bq = document.createElement("blockquote");
+        bq.innerHTML = inline(trimmed.slice(2));
+        container.appendChild(bq);
+        i++;
+        continue;
+      }
+      if (/^`[^`]+`$/.test(trimmed)) {
+        flushPara();
+        const el = document.createElement("div");
+        el.className = "ts";
+        el.textContent = trimmed.replace(/`/g, "");
+        container.appendChild(el);
+        i++;
+        continue;
+      }
+      if (trimmed.startsWith("- ")) {
+        flushPara();
+        const items = [];
+        while (i < lines.length && lines[i].trim().startsWith("- ")) {
+          items.push(lines[i].trim().slice(2).trim());
+          i++;
+        }
+        renderList(items, container);
+        continue;
+      }
+      para.push(trimmed);
+      i++;
+    }
+    flushPara();
+  }
+
+  function renderCell(meta, body, container, artifacts) {
+    const cell = document.createElement("section");
+    cell.className = `cell ${meta.type || "markdown"}`;
+    if (meta.id) cell.dataset.cellId = meta.id;
+    if (isPinned(meta)) cell.classList.add("pinned-source");
+
+    const head = document.createElement("div");
+    head.className = "cell-head";
+    const rawTitle = (meta.title || "").trim();
+    const title = rawTitle && rawTitle.toLowerCase() !== "untitled" ? esc(rawTitle) : "";
+    const when = meta.created_at ? `<span>${esc(formatTime(meta.created_at))}</span>` : "";
+    head.innerHTML =
+      (title ? `<div class="cell-title">${title}</div>` : "") +
+      `<div class="cell-meta">${when}</div>`;
+    if (!title) head.classList.add("no-title");
+    cell.appendChild(head);
+
+    const bodyEl = document.createElement("div");
+    bodyEl.className = "cell-body";
+    if (meta.type === "code") {
+      renderCodeCell(body, bodyEl, artifacts);
+    } else if (meta.type === "figure") {
+      cell.dataset.resUrl = `trackio-figure://${(meta.title || "Figure").trim()}`;
+      renderFigureCell(body, bodyEl, head);
+    } else if (meta.type === "artifact") {
+      renderMarkdownPlain(body, bodyEl);
+      const chip = bodyEl.querySelector(".artifact-chip");
+      const uri = body.match(
+        /(trackio-artifact:\/\/\S+|trackio-local-path:\/\/\S+|https:\/\/huggingface\.co\/buckets\/[^\s<)]+#\S+)/
+      );
+      if (chip && uri) chip.dataset.resUrl = uri[1];
+    } else if (meta.type === "dashboard") {
+      const sp = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+      cell.dataset.resUrl = sp
+        ? sp[0]
+        : `trackio-local-dashboard://${(meta.dashboard_project || "").trim()}`;
+      renderDashboardCell(meta, body, bodyEl, head);
+    } else {
+      const cleaned = stripDuplicateTitle(body, meta.title);
+      renderMarkdownPlain(cleaned, bodyEl);
+      renderDetectedEmbeds(cleaned, bodyEl);
+    }
+    cell.appendChild(bodyEl);
+    container.appendChild(cell);
+    return cell;
+  }
+
+  function isPinned(meta) {
+    return Boolean(meta && (meta.pinned === true || meta.pinned === "true"));
+  }
+
+  function stripDuplicateTitle(body, title) {
+    if (!title) return body;
+    const m = body.match(/^\s*#{1,6}\s+([^\n]+)\n?/);
+    if (!m) return body;
+    const norm = (s) =>
+      s
+        .toLowerCase()
+        .replace(/[*_`#]/g, "")
+        .replace(/\s+/g, " ")
+        .trim();
+    return norm(m[1]) === norm(title) ? body.slice(m[0].length) : body;
+  }
+
+  function formatTime(iso) {
+    const d = new Date(iso);
+    if (Number.isNaN(d.getTime())) return iso;
+    return d.toLocaleString(undefined, {
+      month: "short",
+      day: "numeric",
+      hour: "2-digit",
+      minute: "2-digit",
+    });
+  }
+
+  function parseFences(text) {
+    const fenceRe = /(`{3,4}|~{3,4})([^\n]*)\n([\s\S]*?)\n\1/g;
+    const parts = [];
+    let pos = 0;
+    let match;
+    while ((match = fenceRe.exec(text))) {
+      if (match.index > pos) {
+        parts.push({ kind: "text", text: text.slice(pos, match.index) });
+      }
+      const info = match[2].trim();
+      const lang = (info.split(/\s+/)[0] || "").toLowerCase();
+      const titleMatch = info.match(/title=(\S+)/);
+      parts.push({
+        kind: lang === "result" || lang === "output" ? "output" : "code",
+        lang,
+        title: titleMatch ? titleMatch[1] : null,
+        text: match[3],
+      });
+      pos = match.index + match[0].length;
+    }
+    if (pos < text.length) parts.push({ kind: "text", text: text.slice(pos) });
+    return parts;
+  }
+
+  function fitFigureFrame(frame, wrap) {
+    let doc;
+    try {
+      doc = frame.contentDocument;
+    } catch (e) {
+      return;
+    }
+    if (!doc || !doc.body) return;
+    frame.style.transform = "none";
+    frame.style.width = "100%";
+    frame.style.height = "auto";
+    frame.style.position = "";
+    frame.style.left = "";
+    frame.style.top = "";
+    const avail = wrap.clientWidth;
+    const isFullscreen =
+      document.fullscreenElement === wrap ||
+      document.webkitFullscreenElement === wrap;
+    const availHeight = isFullscreen ? wrap.clientHeight : Infinity;
+    const cw = Math.max(doc.body.scrollWidth, doc.documentElement.scrollWidth, 1);
+    const ch = Math.max(doc.body.scrollHeight, doc.documentElement.scrollHeight, 1);
+    const scale = Math.min(avail / cw, availHeight / ch);
+    if (avail && scale < 1 - 1e-3) {
+      frame.style.width = `${cw}px`;
+      frame.style.height = `${ch}px`;
+      frame.style.transformOrigin = "top left";
+      frame.style.transform = `scale(${scale})`;
+      if (isFullscreen) {
+        frame.style.position = "absolute";
+        frame.style.left = `${Math.max(0, (avail - cw * scale) / 2)}px`;
+        frame.style.top = `${Math.max(0, (availHeight - ch * scale) / 2)}px`;
+        wrap.style.height = "100%";
+      } else {
+        wrap.style.height = `${Math.ceil(ch * scale)}px`;
+      }
+    } else {
+      frame.style.width = "100%";
+      frame.style.height = `${ch}px`;
+      wrap.style.height = isFullscreen ? "100%" : `${ch}px`;
+    }
+  }
+
+  function attachFigureFit(frame, wrap) {
+    const refit = () => fitFigureFrame(frame, wrap);
+    frame.addEventListener("load", refit);
+    if (window.ResizeObserver) {
+      const ro = new ResizeObserver(() => refit());
+      ro.observe(wrap);
+    }
+  }
+
+  function renderFigureCell(text, container, head) {
+    const parts = parseFences(text);
+    const htmlPart = parts.find((part) => part.lang === "html");
+    const rawPart = parts.find((part) => part.lang === "raw");
+    if (!htmlPart || !htmlPart.text.trim()) {
+      const empty = document.createElement("p");
+      empty.className = "muted";
+      empty.textContent = "No figure HTML.";
+      container.appendChild(empty);
+      return;
+    }
+    const frame = document.createElement("iframe");
+    frame.className = "figure-frame";
+    frame.sandbox = "allow-scripts allow-same-origin";
+    frame.loading = "lazy";
+    frame.srcdoc = htmlPart.text;
+    registerFigureNavigation(frame);
+    const figWrap = document.createElement("div");
+    figWrap.className = "figure-fit";
+    figWrap.appendChild(frame);
+    attachFigureFit(frame, figWrap);
+    if (head) {
+      const metaEl = head.querySelector(".cell-meta");
+      if (metaEl)
+        metaEl.insertBefore(buildFullscreenControl(figWrap, frame), metaEl.firstChild);
+    }
+    if (!rawPart || !rawPart.text.trim()) {
+      container.appendChild(figWrap);
+      return;
+    }
+    const sw = document.createElement("div");
+    sw.className = "fig-switch";
+    const thumb = document.createElement("span");
+    thumb.className = "fig-switch-thumb";
+    const figBtn = document.createElement("button");
+    figBtn.type = "button";
+    figBtn.className = "active";
+    figBtn.textContent = "Figure";
+    const rawBtn = document.createElement("button");
+    rawBtn.type = "button";
+    rawBtn.textContent = "Raw";
+    sw.appendChild(thumb);
+    sw.appendChild(figBtn);
+    sw.appendChild(rawBtn);
+    const rawView = document.createElement("div");
+    rawView.className = "figure-raw";
+    rawView.hidden = true;
+    const pre = document.createElement("pre");
+    const code = document.createElement("code");
+    code.textContent = rawPart.text;
+    pre.appendChild(code);
+    rawView.appendChild(pre);
+    rawView.appendChild(copySnippetBtn(rawPart.text));
+    const select = (showRaw) => {
+      sw.classList.toggle("raw", showRaw);
+      figBtn.classList.toggle("active", !showRaw);
+      rawBtn.classList.toggle("active", showRaw);
+      figWrap.hidden = showRaw;
+      rawView.hidden = !showRaw;
+    };
+    figBtn.addEventListener("click", () => select(false));
+    rawBtn.addEventListener("click", () => select(true));
+    if (head) {
+      head.insertBefore(sw, head.querySelector(".cell-meta"));
+    } else {
+      container.appendChild(sw);
+    }
+    container.appendChild(figWrap);
+    container.appendChild(rawView);
+  }
+
+  // Poster embeds can send `{ type: "trackio-logbook:navigate", target: "..." }`
+  // from their iframe. Only accept messages from figure frames we created, and
+  // only route to pages that are present in this logbook's manifest.
+  function registerFigureNavigation(frame) {
+    const registerFrameWindow = () => {
+      if (frame.contentWindow) FIGURE_FRAME_WINDOWS.add(frame.contentWindow);
+    };
+    // `srcdoc` replaces the initial about:blank document. Register after that
+    // navigation as well, so messages come from the live figure document.
+    frame.addEventListener("load", registerFrameWindow);
+    registerFrameWindow();
+    if (FIGURE_NAVIGATION_READY) return;
+    FIGURE_NAVIGATION_READY = true;
+    window.addEventListener("message", (event) => {
+      if (!FIGURE_FRAME_WINDOWS.has(event.source)) return;
+      const message = event.data;
+      if (!message || message.type !== "trackio-logbook:navigate") return;
+      const target = String(message.target || "").replace(/^#?\//, "");
+      if (!target || !MANIFEST || !findNode(MANIFEST.root, target)) return;
+      const hash = "#/" + target;
+      if (location.hash === hash) scrollToHash();
+      else location.hash = hash;
+    });
+  }
+
+  const FULLSCREEN_ICON =
+    '<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" ' +
+    'stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">' +
+    '<path d="M8 3H3v5M16 3h5v5M21 16v5h-5M3 16v5h5"/>' +
+    '<path d="M3 8 8 3M16 3l5 5M21 16l-5 5M8 21l-5-5"/></svg>';
+
+  // Figures are rendered in same-origin iframes, so fullscreen the fitted
+  // wrapper rather than the iframe document. This uses the browser's native
+  // fullscreen UI and preserves the figure's existing responsive sizing.
+  function buildFullscreenControl(figWrap, frame) {
+    const wrap = document.createElement("span");
+    wrap.className = "cell-fullscreen";
+    const btn = document.createElement("button");
+    btn.type = "button";
+    btn.className = "cell-fullscreen-btn";
+    btn.setAttribute("aria-label", "Open figure in fullscreen");
+    btn.title = "Open figure in fullscreen";
+    btn.innerHTML = FULLSCREEN_ICON;
+    wrap.appendChild(btn);
+
+    btn.addEventListener("click", async () => {
+      const request = figWrap.requestFullscreen || figWrap.webkitRequestFullscreen;
+      if (!request) return;
+      try {
+        await request.call(figWrap);
+      } catch (_) {
+        // Fullscreen can be disabled by the embedding browser or policy.
+      }
+    });
+    document.addEventListener("fullscreenchange", () => {
+      if (document.fullscreenElement === figWrap) fitFigureFrame(frame, figWrap);
+    });
+    return wrap;
+  }
+
+  function extractUrls(text) {
+    const seen = new Set();
+    const urls = [];
+    let match;
+    while ((match = DETECTED_URL.exec(text))) {
+      const url = match[1].replace(/[.,;:!?'"`]+$/, "");
+      if (!seen.has(url)) {
+        seen.add(url);
+        urls.push(url);
+      }
+    }
+    DETECTED_URL.lastIndex = 0;
+    return urls;
+  }
+
+  const IMG_URL = /(\.(png|jpe?g|gif|svg|webp)(\?|$)|\/artifact_blob\/)/i;
+
+  function renderDetectedEmbeds(text, container) {
+    extractUrls(text).forEach((url) => {
+      if (url.startsWith("trackio-local-dashboard://")) {
+        const div = document.createElement("div");
+        div.className = "artifact-chip";
+        div.dataset.resUrl = url;
+        div.innerHTML =
+          "🎯 <strong>Local Trackio dashboard</strong> — publish the logbook to share it";
+        container.appendChild(div);
+      } else if (IMG_URL.test(url)) {
+        container.appendChild(renderImage(url));
+      } else if (/huggingface\.co\/spaces\//.test(url)) {
+        maybeEmbedTrackioSpace(url, container);
+      }
+    });
+  }
+
+  function renderStandaloneUrl(url) {
+    if (IMG_URL.test(url) || IMG_PATH.test(url)) return renderImage(url);
+    const item = classifyResource(url);
+    if (item) {
+      const marker = document.createElement("span");
+      marker.className = "resource-anchor";
+      marker.dataset.resUrl = item.url;
+      marker.setAttribute("aria-hidden", "true");
+      return marker;
+    }
+    const p = document.createElement("p");
+    p.innerHTML = inline(url);
+    return p;
+  }
+
+  function renderImage(url) {
+    const a = document.createElement("a");
+    a.className = "unfurl image";
+    a.href = url;
+    a.target = "_blank";
+    a.rel = "noopener";
+    const img = document.createElement("img");
+    img.loading = "lazy";
+    img.src = url;
+    img.alt = "artifact image";
+    a.appendChild(img);
+    return a;
+  }
+
+  function maybeEmbedTrackioSpace(url, container) {
+    const id = url.split("/spaces/")[1].split(/[?#]/)[0].replace(/\/$/, "");
+    const holder = document.createElement("div");
+    container.appendChild(holder);
+    getJSON(`https://huggingface.co/api/spaces/${id}`).then((d) => {
+      const tags = (d && d.tags) || [];
+      if (tags.some((t) => String(t).toLowerCase() === "trackio")) {
+        renderTrackioSpaceEmbed(holder, url, id);
+      } else {
+        holder.remove();
+      }
+    });
+  }
+
+  function jpGutter(label) {
+    const g = document.createElement("div");
+    g.className = "jp-gutter";
+    g.textContent = label;
+    return g;
+  }
+
+  function renderOutArtifact(info) {
+    const remote = !info.local && !!info.url;
+    const el = document.createElement(remote ? "a" : "div");
+    el.className = "out-artifact";
+    if (remote) {
+      el.href = info.url;
+      el.target = "_blank";
+      el.rel = "noopener";
+    }
+    el.dataset.resUrl = info.resUrl;
+    const parts = [info.type, info.size].filter(Boolean).map(esc);
+    const state = remote
+      ? `<span class="out-artifact-state open">Open ↗</span>`
+      : `<span class="out-artifact-state">publish to share</span>`;
+    const meta = parts.length ? `${parts.join(" · ")} · ${state}` : state;
+    el.innerHTML =
+      `<span class="out-artifact-ico">${ARTIFACT_ICON_IMG}</span>` +
+      `<span class="out-artifact-name">${esc(info.name)}</span>` +
+      `<span class="out-artifact-meta">${meta}</span>`;
+    return el;
+  }
+
+  function renderCodeCell(body, container, artifacts) {
+    const parts = parseFences(body);
+    const block = document.createElement("div");
+    block.className = "jp";
+    const input = document.createElement("div");
+    input.className = "jp-in";
+    const inputBody = document.createElement("div");
+    inputBody.className = "jp-in-body";
+    input.appendChild(jpGutter("In"));
+    input.appendChild(inputBody);
+    let metaEl = null;
+    let outputEl = null;
+    let outBody = null;
+    const ensureOut = () => {
+      if (outputEl) return;
+      outputEl = document.createElement("div");
+      outputEl.className = "jp-out";
+      outputEl.appendChild(jpGutter("Out"));
+      outBody = document.createElement("div");
+      outBody.className = "jp-out-body";
+      outputEl.appendChild(outBody);
+    };
+    const embedTexts = [];
+    parts.forEach((part) => {
+      if (part.kind === "text") {
+        const text = part.text.trim();
+        if (!text) return;
+        if (/^exit\s+\S+(\s|·)/.test(text)) {
+          metaEl = document.createElement("div");
+          metaEl.className = "jp-meta";
+          metaEl.textContent = text.replace(
+            /\s*·\s*[A-Z][a-z]{2} \d{1,2}, \d{4}.*$/,
+            ""
+          );
+        } else {
+          renderMarkdownPlain(text, container);
+          embedTexts.push(text);
+        }
+        return;
+      }
+      if (part.kind === "output") {
+        ensureOut();
+        const pre = document.createElement("pre");
+        pre.className = "jp-out-pre";
+        const c = document.createElement("code");
+        c.textContent = part.text;
+        pre.appendChild(c);
+        outBody.appendChild(pre);
+        outputEl.appendChild(copySnippetBtn(part.text));
+        embedTexts.push(part.text);
+        return;
+      }
+      inputBody.appendChild(renderCode(part.text, part.lang, part.title));
+    });
+    if (artifacts && artifacts.length) {
+      ensureOut();
+      const artWrap = document.createElement("div");
+      artWrap.className = "jp-artifacts";
+      artifacts.forEach((a) => {
+        artWrap.appendChild(
+          renderOutArtifact(artifactInfoFromCell(a.meta, a.body))
+        );
+      });
+      outBody.appendChild(artWrap);
+    }
+    if (inputBody.childNodes.length > 0) block.appendChild(input);
+    if (metaEl) block.appendChild(metaEl);
+    if (outputEl) block.appendChild(outputEl);
+    if (block.childNodes.length) container.appendChild(block);
+    embedTexts.forEach((text) => renderDetectedEmbeds(text, container));
+  }
+
+  function parseRow(line) {
+    let s = line.trim();
+    if (s.startsWith("|")) s = s.slice(1);
+    if (s.endsWith("|")) s = s.slice(0, -1);
+    return s.split(/(?<!\\)\|/).map((c) => c.replace(/\\\|/g, "|").trim());
+  }
+
+  const TRUTHY = ["x", "✓", "✔", "yes", "done", "true", "[x]"];
+  const CHIP_COLORS = [
+    ["#e7f0ff", "#2158d0"],
+    ["#fde8ec", "#c62a4b"],
+    ["#e6f7ee", "#1a8a55"],
+    ["#fdf0e0", "#b26a12"],
+    ["#efe9ff", "#5b3bd6"],
+    ["#e6f6f8", "#127b88"],
+  ];
+
+  function chipColor(name) {
+    let h = 0;
+    for (let i = 0; i < name.length; i++) h = (h * 31 + name.charCodeAt(i)) >>> 0;
+    return CHIP_COLORS[h % CHIP_COLORS.length];
+  }
+
+  const STATUS_MAP = {
+    "": ["Planned", "gray"],
+    planned: ["Planned", "gray"],
+    todo: ["Planned", "gray"],
+    "to do": ["Planned", "gray"],
+    backlog: ["Planned", "gray"],
+    "in progress": ["In progress", "amber"],
+    "in-progress": ["In progress", "amber"],
+    wip: ["In progress", "amber"],
+    running: ["In progress", "amber"],
+    active: ["In progress", "amber"],
+    done: ["Done", "green"],
+    complete: ["Done", "green"],
+    completed: ["Done", "green"],
+    blocked: ["Blocked", "red"],
+    failed: ["Failed", "red"],
+    abandoned: ["Abandoned", "gray"],
+  };
+
+  function statusBadge(val) {
+    const [label, tone] = STATUS_MAP[val.toLowerCase()] || [val || "—", "gray"];
+    return `<span class="badge ${tone}">${esc(label)}</span>`;
+  }
+
+  function renderTable(rows, container) {
+    if (rows.length < 2) return;
+    const header = rows[0];
+    const body = rows.slice(2);
+    const roles = header.map((h) => {
+      const t = h.toLowerCase();
+      if (t.includes("status") || t.includes("state")) return "status";
+      if (t.includes("progress") || t.includes("complete") || t.includes("done"))
+        return "check";
+      if (t === "who" || t.includes("assign") || t.includes("owner")) return "who";
+      return "text";
+    });
+    const table = document.createElement("table");
+    table.className = "board";
+    const thead = document.createElement("thead");
+    const htr = document.createElement("tr");
+    header.forEach((h, c) => {
+      const th = document.createElement("th");
+      th.textContent = h;
+      if (roles[c] === "check") th.className = "col-check";
+      htr.appendChild(th);
+    });
+    thead.appendChild(htr);
+    table.appendChild(thead);
+    const tbody = document.createElement("tbody");
+    body.forEach((cells) => {
+      const nonEmpty = cells.filter((x) => x !== "").length;
+      if (header.length > 1 && nonEmpty === 1 && cells[0]) {
+        const tr = document.createElement("tr");
+        tr.className = "section-row";
+        const td = document.createElement("td");
+        td.colSpan = header.length;
+        td.innerHTML = inline(cells[0]);
+        tr.appendChild(td);
+        tbody.appendChild(tr);
+        return;
+      }
+      const tr = document.createElement("tr");
+      header.forEach((_, c) => {
+        const td = document.createElement("td");
+        const val = (cells[c] || "").trim();
+        if (roles[c] === "status") {
+          td.className = "col-status";
+          td.innerHTML = statusBadge(val);
+        } else if (roles[c] === "check") {
+          td.className = "col-check";
+          const on = TRUTHY.indexOf(val.toLowerCase()) !== -1;
+          td.innerHTML = `<span class="box ${on ? "on" : ""}">${on ? "✓" : ""}</span>`;
+        } else if (roles[c] === "who") {
+          if (!val || /^to assign$/i.test(val)) {
+            td.innerHTML = `<span class="who-chip muted">${esc(val || "—")}</span>`;
+          } else {
+            const [bg, fg] = chipColor(val);
+            td.innerHTML = `<span class="who-chip" style="background:${bg};color:${fg}">${esc(val)}</span>`;
+          }
+        } else {
+          td.innerHTML = inline(val);
+        }
+        tr.appendChild(td);
+      });
+      const link = tr.querySelector('a[href^="#/"]');
+      if (link) {
+        tr.classList.add("linked-row");
+        tr.addEventListener("click", (e) => {
+          if (e.target.tagName !== "A") location.hash = link.getAttribute("href");
+        });
+      }
+      tbody.appendChild(tr);
+    });
+    table.appendChild(tbody);
+    const wrap = document.createElement("div");
+    wrap.className = "board-wrap";
+    wrap.appendChild(table);
+    container.appendChild(wrap);
+  }
+
+  const HL_RULES = {
+    python: [
+      ["comment", /#[^\n]*/],
+      ["string", /'''[\s\S]*?'''|"""[\s\S]*?"""|'(?:\\.|[^'\\])*'|"(?:\\.|[^"\\])*"/],
+      [
+        "keyword",
+        /\b(?:def|class|return|if|elif|else|for|while|import|from|as|with|try|except|finally|raise|in|not|and|or|is|None|True|False|lambda|yield|global|nonlocal|assert|pass|break|continue|async|await|print)\b/,
+      ],
+      ["number", /\b\d[\d_.eE+-]*\b/],
+    ],
+    bash: [
+      ["comment", /#[^\n]*/],
+      ["string", /'(?:\\.|[^'\\])*'|"(?:\\.|[^"\\])*"/],
+      ["keyword", /\b(?:if|then|else|fi|for|in|do|done|while|case|esac|function|export|source|echo|cd|return|local)\b/],
+      ["number", /(?<=\s)-{1,2}[a-zA-Z][\w-]*/],
+    ],
+    json: [
+      ["string", /"(?:\\.|[^"\\])*"/],
+      ["keyword", /\b(?:true|false|null)\b/],
+      ["number", /-?\b\d[\d.eE+-]*\b/],
+    ],
+    yaml: [
+      ["comment", /#[^\n]*/],
+      ["string", /'(?:\\.|[^'\\])*'|"(?:\\.|[^"\\])*"/],
+      ["keyword", /\b(?:true|false|null|yes|no)\b/],
+      ["number", /-?\b\d[\d.eE+-]*\b/],
+    ],
+  };
+  HL_RULES.javascript = HL_RULES.python;
+  HL_RULES.typescript = HL_RULES.python;
+  HL_RULES.sql = [
+    ["comment", /--[^\n]*/],
+    ["string", /'(?:\\.|[^'\\])*'/],
+    [
+      "keyword",
+      /\b(?:SELECT|FROM|WHERE|JOIN|LEFT|RIGHT|INNER|OUTER|ON|GROUP|BY|ORDER|LIMIT|INSERT|INTO|VALUES|UPDATE|SET|DELETE|CREATE|TABLE|AS|AND|OR|NOT|NULL|COUNT|DISTINCT|IN)\b/i,
+    ],
+    ["number", /\b\d[\d.]*\b/],
+  ];
+
+  function highlightCode(code, lang) {
+    const rules = HL_RULES[lang];
+    if (!rules) return esc(code);
+    const combined = new RegExp(rules.map((r) => "(" + r[1].source + ")").join("|"), "g");
+    let out = "";
+    let last = 0;
+    let m;
+    while ((m = combined.exec(code))) {
+      if (m[0] === "") {
+        combined.lastIndex++;
+        continue;
+      }
+      out += esc(code.slice(last, m.index));
+      let gi = 1;
+      while (gi < m.length && m[gi] === undefined) gi++;
+      out += `<span class="tok-${rules[gi - 1][0]}">${esc(m[0])}</span>`;
+      last = m.index + m[0].length;
+    }
+    out += esc(code.slice(last));
+    return out;
+  }
+
+  function copySnippetBtn(text) {
+    const btn = document.createElement("button");
+    btn.type = "button";
+    btn.className = "copy-snippet";
+    btn.title = "Copy";
+    btn.textContent = "⧉";
+    btn.addEventListener("click", (e) => {
+      e.preventDefault();
+      e.stopPropagation();
+      copyText(text, btn, "⧉");
+    });
+    return btn;
+  }
+
+  function renderCode(code, lang, title) {
+    const pre = document.createElement("pre");
+    pre.className = "hl";
+    const c = document.createElement("code");
+    c.innerHTML = highlightCode(code, lang);
+    pre.appendChild(c);
+    if (!title) {
+      const wrap = document.createElement("div");
+      wrap.className = "snippet";
+      wrap.appendChild(pre);
+      wrap.appendChild(copySnippetBtn(code));
+      return wrap;
+    }
+    const det = document.createElement("details");
+    det.className = "code-accordion";
+    det.dataset.resUrl = `trackio-script://${title}`;
+    const sum = document.createElement("summary");
+    sum.innerHTML =
+      `<span class="code-ico">&lt;/&gt;</span>` +
+      `<span class="code-name">${esc(title)}</span>`;
+    sum
+      .querySelector(".code-name")
+      .addEventListener("click", (e) => e.preventDefault());
+    det.appendChild(sum);
+    const wrap = document.createElement("div");
+    wrap.className = "snippet";
+    wrap.appendChild(pre);
+    wrap.appendChild(copySnippetBtn(code));
+    det.appendChild(wrap);
+    return det;
+  }
+
+  const IMG_PATH = /^[^\s]+\.(png|jpe?g|gif|svg|webp)$/i;
+
+  function renderList(items, container) {
+    let ul = null;
+    items.forEach((item) => {
+      if (URL_ONLY.test(item) || IMG_PATH.test(item)) {
+        const el = renderStandaloneUrl(item);
+        if (el) {
+          ul = null;
+          container.appendChild(el);
+        }
+      } else if (item.indexOf("📦 Artifact") !== -1) {
+        ul = null;
+        const div = document.createElement("div");
+        div.className = "artifact-chip";
+        div.innerHTML = inline(item.replace("📦", "🪣"));
+        container.appendChild(div);
+      } else if (item.indexOf("trackio-local-dashboard://") !== -1) {
+        ul = null;
+        const uri = item.match(/trackio-local-dashboard:\/\/\S+/)?.[0] || "";
+        const div = document.createElement("div");
+        div.className = "artifact-chip";
+        if (uri) div.dataset.resUrl = uri;
+        div.innerHTML =
+          "🎯 <strong>Local dashboard</strong> — publish the logbook to share it";
+        container.appendChild(div);
+      } else {
+        if (!ul) {
+          ul = document.createElement("ul");
+          container.appendChild(ul);
+        }
+        const li = document.createElement("li");
+        li.innerHTML = inline(item);
+        ul.appendChild(li);
+      }
+    });
+  }
+
+  /* -------------------- resources rail -------------------- */
+
+  function fmt(n) {
+    if (n == null) return null;
+    if (n >= 1e6) return (n / 1e6).toFixed(1) + "M";
+    if (n >= 1e3) return (n / 1e3).toFixed(1) + "k";
+    return String(n);
+  }
+
+  const RESOURCE_SECTIONS = [
+    ["dashboard", "Dashboards", "🎯"],
+    ["model", "Models", "🤗"],
+    ["dataset", "Datasets", "📊"],
+    ["space", "Spaces", "🚀"],
+    ["artifact", "Artifacts", "🪣"],
+    ["paper", "Papers", "📄"],
+    ["repo", "Code", "🐙"],
+    ["job", "Jobs", "⚙️"],
+    ["bucket", "Buckets", "🪣"],
+  ];
+
+  const RESOURCE_ICONS = Object.fromEntries(
+    RESOURCE_SECTIONS.map(([kind, , icon]) => [kind, icon])
+  );
+
+  const ARTIFACT_ICON_IMG = `<img class="art-ico" src="./bucket-icon.svg" alt="" />`;
+  const DASHBOARD_ICON_IMG = `<img class="art-ico" src="./trackio-logo-light.png" alt="" />`;
+
+  const RESOURCE_DESC = {
+    dashboard: "Dashboard",
+    model: "Model",
+    dataset: "Dataset",
+    space: "Space",
+    artifact: "Artifact — in Bucket",
+    paper: "Paper",
+    repo: "Repository",
+    job: "Job — status & logs",
+    bucket: "Bucket — artifacts & data",
+  };
+
+  const HF_NON_MODEL_PREFIX =
+    /^(datasets|spaces|jobs|buckets|papers|blog|docs|api|posts|collections|organizations|settings|new|join|login|pricing|tasks|learn|chat|models)(\/|$)/;
+
+  function hfId(url, marker) {
+    return url.split(marker)[1].split(/[?#]/)[0].replace(/\/$/, "");
+  }
+
+  function classifyResource(url) {
+    if (IMG_URL.test(url)) {
+      return null;
+    }
+    let m;
+    if (url.startsWith("trackio-local-dashboard://")) {
+      return {
+        kind: "dashboard",
+        id: url.slice("trackio-local-dashboard://".length),
+        url,
+        local: true,
+      };
+    }
+    if (url.startsWith("trackio-artifact://")) {
+      return {
+        kind: "artifact",
+        id: url.slice("trackio-artifact://".length),
+        url,
+        local: true,
+      };
+    }
+    if (url.startsWith("trackio-local-path://")) {
+      return {
+        kind: "artifact",
+        id: url.slice("trackio-local-path://".length),
+        url,
+        local: true,
+      };
+    }
+    if ((m = url.match(/huggingface\.co\/buckets\/[^#\s]+#(.+)/))) {
+      return { kind: "artifact", id: decodeURIComponent(m[1]), url };
+    }
+    if (/huggingface\.co\/datasets\/[^/]+\/[^/]+/.test(url)) {
+      return { kind: "dataset", id: hfId(url, "/datasets/"), url };
+    }
+    if (/huggingface\.co\/spaces\/[^/]+\/[^/]+/.test(url)) {
+      return { kind: "space", id: hfId(url, "/spaces/"), url };
+    }
+    if (/huggingface\.co\/jobs\//.test(url)) {
+      const parts = hfId(url, "/jobs/").split("/");
+      const jid = parts[1] || "";
+      return {
+        kind: "job",
+        id: parts[0] + (jid ? ` · ${jid.slice(0, 12)}${jid.length > 12 ? "…" : ""}` : ""),
+        url,
+      };
+    }
+    if (/huggingface\.co\/buckets\//.test(url)) {
+      return { kind: "bucket", id: hfId(url, "/buckets/"), url };
+    }
+    if (/huggingface\.co\/papers\//.test(url)) {
+      return { kind: "paper", id: `Paper ${hfId(url, "/papers/")}`, url };
+    }
+    if ((m = url.match(/arxiv\.org\/(?:abs|pdf)\/([^?#\s]+)/))) {
+      return { kind: "paper", id: `arXiv:${m[1].replace(/\.pdf$/, "")}`, url };
+    }
+    if ((m = url.match(/github\.com\/([^/?#]+\/[^/?#]+)/))) {
+      return { kind: "repo", id: m[1], url };
+    }
+    if ((m = url.match(/huggingface\.co\/([^?#]+)/))) {
+      const rest = m[1].replace(/\/$/, "");
+      if (/^[^/]+\/[^/]+$/.test(rest) && !HF_NON_MODEL_PREFIX.test(rest)) {
+        return { kind: "model", id: rest, url };
+      }
+    }
+    return null;
+  }
+
+  async function fillRailMeta(item, el) {
+    if (item.local) return;
+    const meta = el.querySelector(".rail-meta");
+    const set = (parts) => {
+      const text = parts.filter(Boolean).join(" · ");
+      if (text) meta.textContent = text;
+    };
+    if (item.kind === "model") {
+      const d = await getJSON(`https://huggingface.co/api/models/${item.id}`);
+      if (d) set([d.pipeline_tag, `↓ ${fmt(d.downloads)}`, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "dataset") {
+      const d = await getJSON(`https://huggingface.co/api/datasets/${item.id}`);
+      if (d) set([`↓ ${fmt(d.downloads)}`, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "space" || item.kind === "dashboard") {
+      const d = await getJSON(`https://huggingface.co/api/spaces/${item.id}`);
+      if (d) set([d.sdk, `♥ ${fmt(d.likes)}`]);
+    } else if (item.kind === "repo") {
+      const d = await getJSON(`https://api.github.com/repos/${item.id}`);
+      if (d) set([`★ ${fmt(d.stargazers_count)}`, d.language]);
+    } else if (item.kind === "paper") {
+      const m = item.id.match(/^(?:arXiv:|Paper )(.+)$/);
+      if (!m) return;
+      const arxivId = m[1].replace(/v\d+$/, "");
+      const d = await getJSON(`https://huggingface.co/api/papers/${arxivId}`);
+      if (d && d.id) {
+        if (el.href) el.href = `https://huggingface.co/papers/${d.id}`;
+        const title =
+          d.title && d.title.length > 70 ? `${d.title.slice(0, 69)}…` : d.title;
+        set([title, d.upvotes ? `▲ ${fmt(d.upvotes)}` : null]);
+      }
+    }
+  }
+
+  const BARE_ID_SKIP_DIRS = new Set([
+    "scripts",
+    "configs",
+    "config",
+    "results",
+    "figures",
+    "data",
+    "datasets",
+    "src",
+    "tests",
+    "test",
+    "examples",
+    "pages",
+    "assets",
+    "docs",
+    "outputs",
+    "output",
+    "checkpoints",
+    "models",
+    "utils",
+    "lib",
+    "bin",
+    "tmp",
+    "node_modules",
+    "dist",
+    "build",
+  ]);
+  const FILE_EXT_RE =
+    /\.(py|pyc|js|ts|jsx|tsx|json|jsonl|yaml|yml|csv|tsv|md|txt|sh|bash|html|css|png|jpe?g|svg|gif|webp|ipynb|toml|cfg|ini|lock|pdf|whl|gz|zip|tar|pt|pth|bin|safetensors|db|sqlite)$/i;
+
+  async function detectBareModelIds(text, groups) {
+    const stripped = text.replace(DETECTED_URL, " ");
+    DETECTED_URL.lastIndex = 0;
+    const seen = new Set();
+    const candidates = [];
+    const re = /(^|[\s"'`(=[])([A-Za-z0-9][\w.-]*\/[A-Za-z0-9][\w.-]*)/g;
+    let m;
+    while ((m = re.exec(stripped)) && candidates.length < 15) {
+      const id = m[2].replace(/[.:,]+$/, "");
+      if (seen.has(id)) continue;
+      seen.add(id);
+      if (FILE_EXT_RE.test(id)) continue;
+      if (BARE_ID_SKIP_DIRS.has(id.split("/")[0].toLowerCase())) continue;
+      candidates.push(id);
+    }
+    const results = await Promise.all(
+      candidates.map((id) => getJSON(`https://huggingface.co/api/models/${id}`))
+    );
+    let added = false;
+    const confirmed = [];
+    results.forEach((d, i) => {
+      if (!d || !d.id) return;
+      const id = candidates[i];
+      confirmed.push(id);
+      const url = `https://huggingface.co/${id}`;
+      if (!groups.has("model")) groups.set("model", new Map());
+      if (!groups.get("model").has(url)) {
+        groups.get("model").set(url, { kind: "model", id, url });
+        added = true;
+      }
+    });
+    return { added, confirmed };
+  }
+
+  function chipifyBareIds(ids, container) {
+    if (!ids.length) return;
+    const escaped = ids.map((id) => id.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"));
+    const pattern = new RegExp("(" + escaped.join("|") + ")");
+    const splitter = new RegExp(pattern.source, "g");
+    container
+      .querySelectorAll(".cell.markdown .cell-body")
+      .forEach((body) => {
+        const walker = document.createTreeWalker(body, NodeFilter.SHOW_TEXT, {
+          acceptNode(node) {
+            if (!pattern.test(node.nodeValue)) return NodeFilter.FILTER_REJECT;
+            for (
+              let el = node.parentElement;
+              el && el !== body;
+              el = el.parentElement
+            ) {
+              if (["A", "CODE", "PRE", "BUTTON"].indexOf(el.tagName) !== -1) {
+                return NodeFilter.FILTER_REJECT;
+              }
+            }
+            return NodeFilter.FILTER_ACCEPT;
+          },
+        });
+        const nodes = [];
+        while (walker.nextNode()) nodes.push(walker.currentNode);
+        nodes.forEach((node) => {
+          const frag = document.createDocumentFragment();
+          node.nodeValue.split(splitter).forEach((part) => {
+            if (ids.indexOf(part) !== -1) {
+              const holder = document.createElement("span");
+              holder.innerHTML = resChipHtml({
+                kind: "model",
+                id: part,
+                url: `https://huggingface.co/${part}`,
+              });
+              frag.appendChild(holder.firstChild);
+            } else if (part) {
+              frag.appendChild(document.createTextNode(part));
+            }
+          });
+          node.parentNode.replaceChild(frag, node);
+        });
+      });
+  }
+
+  let RAIL_TOKEN = 0;
+  const RAIL_EXCLUDE_KINDS = new Set(["paper", "repo", "artifact", "dashboard"]);
+
+  function railDashboardItem(it) {
+    return {
+      kind: "dashboard",
+      id: it.id,
+      url: it.local ? it.resUrl : it.url || it.resUrl,
+      local: it.local,
+      railLabel: "Dashboard",
+    };
+  }
+
+  function promoteTrackioSpacesInRail(groups, dashResUrls, body, rail, token) {
+    const spaceGroup = groups.get("space");
+    if (!spaceGroup || !spaceGroup.size) return;
+    spaceGroup.forEach((item, url) => {
+      getJSON(`https://huggingface.co/api/spaces/${item.id}`)
+        .then((d) => {
+          if (rail.dataset.renderToken !== token) return;
+          const tags = (d && d.tags) || [];
+          if (!tags.some((t) => String(t).toLowerCase() === "trackio")) return;
+          if (dashResUrls.has(url)) return;
+          spaceGroup.delete(url);
+          if (!spaceGroup.size) groups.delete("space");
+          if (!groups.has("dashboard")) groups.set("dashboard", new Map());
+          groups.get("dashboard").set(url, {
+            kind: "dashboard",
+            id: item.id,
+            url: item.url,
+            local: false,
+            railLabel: "Dashboard",
+          });
+          dashResUrls.add(url);
+          paintRail(groups, body, rail);
+        })
+        .catch(() => {});
+    });
+  }
+
+  function renderRail(md, body, rail) {
+    const token = String(++RAIL_TOKEN);
+    rail.dataset.renderToken = token;
+    const scanText = md.replace(
+      /(`{3,4}|~{3,4})(html|raw)[^\n]*\n[\s\S]*?\n\1/g,
+      " "
+    );
+    const groups = new Map();
+    const dashMap = new Map();
+    const dashResUrls = new Set();
+    cellDashboardItems(md).forEach((it) => {
+      if (dashMap.has(it.resUrl)) return;
+      dashMap.set(it.resUrl, railDashboardItem(it));
+      dashResUrls.add(it.resUrl);
+    });
+    if (dashMap.size) groups.set("dashboard", dashMap);
+    extractUrls(scanText).forEach((url) => {
+      const item = classifyResource(url);
+      if (!item) return;
+      if (RAIL_EXCLUDE_KINDS.has(item.kind)) return;
+      if (dashResUrls.has(url)) return;
+      if (!groups.has(item.kind)) groups.set(item.kind, new Map());
+      groups.get(item.kind).set(item.url, item);
+    });
+    const artMap = new Map();
+    cellArtifactItems(md).forEach((it) => {
+      if (artMap.has(it.resUrl)) return;
+      const label = it.type
+        ? it.type.charAt(0).toUpperCase() + it.type.slice(1)
+        : "Artifact";
+      artMap.set(it.resUrl, {
+        kind: "artifact",
+        id: it.name,
+        url: it.local ? it.resUrl : it.url || it.resUrl,
+        local: it.local,
+        railLabel: label,
+        size: it.size,
+      });
+    });
+    if (artMap.size) groups.set("artifact", artMap);
+    paintRail(groups, body, rail);
+    promoteTrackioSpacesInRail(groups, dashResUrls, body, rail, token);
+    detectBareModelIds(scanText, groups)
+      .then((result) => {
+        if (rail.dataset.renderToken !== token) return;
+        chipifyBareIds(result.confirmed, body);
+        if (result.added) paintRail(groups, body, rail);
+      })
+      .catch(() => {});
+  }
+
+  function paintRail(groups, body, rail) {
+    rail.innerHTML = "";
+    RESOURCE_SECTIONS.forEach(([kind, label, icon]) => {
+      const group = groups.get(kind);
+      if (!group || !group.size) return;
+      group.forEach((item) => {
+        const el = document.createElement(item.local ? "div" : "a");
+        el.className = item.local ? "rail-item rail-local" : "rail-item";
+        if (!item.local) {
+          el.href = item.url;
+          el.target = "_blank";
+          el.rel = "noopener";
+        }
+        el.dataset.resUrl = item.url;
+        let desc;
+        if (kind === "artifact") {
+          const state = item.local ? "publish to share" : "Open ↗";
+          desc = item.size ? `${item.size} · ${state}` : state;
+        } else if (kind === "dashboard") {
+          desc = item.local ? "publish to share" : "Open ↗";
+        } else {
+          desc = item.local ? "publish to share" : RESOURCE_DESC[kind];
+        }
+        const kindLabel = item.railLabel || label.replace(/s$/, "");
+        const iconHtml =
+          kind === "artifact"
+            ? ARTIFACT_ICON_IMG
+            : kind === "dashboard"
+              ? DASHBOARD_ICON_IMG
+              : `<span>${icon}</span>`;
+        el.innerHTML =
+          `<div class="rail-kind">${iconHtml}${esc(kindLabel)}</div>` +
+          `<div class="rail-title">${esc(item.id)}</div>` +
+          `<div class="rail-meta">${esc(desc)}</div>`;
+        rail.appendChild(el);
+        fillRailMeta(item, el)
+          .catch(() => {})
+          .finally(() => scheduleRailPosition(body, rail));
+      });
+    });
+    rail.hidden = !rail.childElementCount;
+    scheduleRailPosition(body, rail);
+  }
+
+  function resourceAnchor(body, url) {
+    return body.querySelector(`[data-res-url="${CSS.escape(url)}"]`);
+  }
+
+  function positionRail(body, rail) {
+    if (rail.hidden || !rail.isConnected) return;
+    const bodyRect = body.getBoundingClientRect();
+    const items = Array.from(rail.querySelectorAll(".rail-item")).map((el, index) => {
+      const anchor = resourceAnchor(body, el.dataset.resUrl);
+      return {
+        el,
+        index,
+        desired: anchor
+          ? Math.max(0, anchor.getBoundingClientRect().top - bodyRect.top)
+          : 0,
+      };
+    });
+    items.sort((a, b) => a.desired - b.desired || a.index - b.index);
+    let cursor = 0;
+    items.forEach(({ el, desired }) => {
+      const top = Math.max(desired, cursor);
+      el.style.top = `${top}px`;
+      cursor = top + el.offsetHeight + 10;
+    });
+    rail.style.minHeight = `${Math.max(body.offsetHeight, cursor)}px`;
+  }
+
+  function scheduleRailPosition(body, rail) {
+    cancelAnimationFrame(Number(rail.dataset.positionFrame || 0));
+    rail.dataset.positionFrame = String(
+      requestAnimationFrame(() => positionRail(body, rail))
+    );
+  }
+
+  function dashboardSubdomainFromUrl(url) {
+    return spaceIdFromUrl(url).toLowerCase().replace(/[^a-z0-9-]/g, "-");
+  }
+
+  function dashboardOpenLink(head, url) {
+    if (!head || !url) return;
+    const meta = head.querySelector(".cell-meta");
+    if (!meta) return;
+    let link = meta.querySelector(".cell-open");
+    if (!link) {
+      link = document.createElement("a");
+      link.className = "cell-open";
+      link.target = "_blank";
+      link.rel = "noopener";
+      meta.insertBefore(link, meta.firstChild);
+    }
+    link.href = url;
+    link.textContent = "Open ↗";
+  }
+
+  function dashboardFrame(src) {
+    const iframe = document.createElement("iframe");
+    iframe.className = "dashboard-frame";
+    iframe.src = src;
+    iframe.loading = "lazy";
+    iframe.allow = "clipboard-read; clipboard-write; fullscreen";
+    return iframe;
+  }
+
+  function renderDashboardCell(meta, body, container, head) {
+    const project = meta.dashboard_project || "";
+    const holder = document.createElement("div");
+    holder.className = "dashboard-shell";
+    container.appendChild(holder);
+    const space = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+    if (space) {
+      const url = space[0];
+      dashboardOpenLink(head, url);
+      holder.appendChild(
+        dashboardFrame(
+          `https://${dashboardSubdomainFromUrl(url)}.hf.space/?sidebar=hidden&hide_empty_tabs=true`
+        )
+      );
+      return;
+    }
+    if (!isLocalPreview()) {
+      holder.className = "artifact-chip";
+      holder.dataset.resUrl = `trackio-local-dashboard://${project}`;
+      holder.innerHTML =
+        "🎯 <strong>Local Trackio dashboard</strong> — publish the logbook to share it";
+      return;
+    }
+    const open = "/dashboard/?project=" + encodeURIComponent(project);
+    dashboardOpenLink(head, open);
+    holder.appendChild(
+      dashboardFrame(open + "&sidebar=hidden&hide_empty_tabs=true"),
+    );
+  }
+
+  const CACHE_PREFIX = "trackio-logbook:";
+  const CACHE_TTL_MS = 24 * 60 * 60 * 1000;
+  const CACHE_MISS_TTL_MS = 60 * 60 * 1000;
+
+  function cacheGet(url) {
+    try {
+      const raw = localStorage.getItem(CACHE_PREFIX + url);
+      if (!raw) return undefined;
+      const entry = JSON.parse(raw);
+      const ttl = entry.d === null ? CACHE_MISS_TTL_MS : CACHE_TTL_MS;
+      if (Date.now() - entry.t > ttl) {
+        localStorage.removeItem(CACHE_PREFIX + url);
+        return undefined;
+      }
+      return entry.d;
+    } catch (e) {
+      return undefined;
+    }
+  }
+
+  function cacheSet(url, data) {
+    try {
+      localStorage.setItem(
+        CACHE_PREFIX + url,
+        JSON.stringify({ t: Date.now(), d: data })
+      );
+    } catch (e) {}
+  }
+
+  async function getJSON(url) {
+    if (UNFURL_CACHE[url] !== undefined) return UNFURL_CACHE[url];
+    const cached = cacheGet(url);
+    if (cached !== undefined) {
+      UNFURL_CACHE[url] = cached;
+      return cached;
+    }
+    try {
+      const r = await fetch(url);
+      if (!r.ok) throw new Error(r.status);
+      const j = await r.json();
+      UNFURL_CACHE[url] = j;
+      cacheSet(url, j);
+      return j;
+    } catch (e) {
+      UNFURL_CACHE[url] = null;
+      cacheSet(url, null);
+      return null;
+    }
+  }
+
+  /* -------------------- routing / render -------------------- */
+
+  function buildTree() {
+    const tree = document.getElementById("tree");
+    tree.innerHTML = "";
+    const nodes = [];
+    (MANIFEST.root.children || []).forEach((c) => flattenTree(c, 0, nodes));
+    nodes.forEach(({ node, depth }) => {
+      const a = document.createElement("a");
+      a.href = "#/" + node.slug;
+      a.className = "depth-" + depth;
+      a.dataset.slug = node.slug;
+      const mark = document.createElement("span");
+      mark.className = "tree-mark";
+      mark.textContent = "§";
+      a.appendChild(mark);
+      a.appendChild(document.createTextNode(" " + node.title));
+      tree.appendChild(a);
+    });
+  }
+
+  function highlight(slug) {
+    document
+      .querySelectorAll("#tree a")
+      .forEach((a) => a.classList.toggle("active", a.dataset.slug === slug));
+    document
+      .getElementById("book-head")
+      .classList.toggle("active", slug === MANIFEST.root.slug);
+  }
+
+  function clearPageCache() {
+    Object.keys(PAGE_CACHE).forEach((key) => {
+      delete PAGE_CACHE[key];
+    });
+  }
+
+  function isLocalPreview() {
+    return ["localhost", "127.0.0.1", "::1"].includes(location.hostname);
+  }
+
+  async function fetchManifest() {
+    const suffix = isLocalPreview() ? `?t=${Date.now()}` : "";
+    return await (await fetch("./logbook.json" + suffix, { cache: "no-store" })).json();
+  }
+
+  async function fetchPage(node) {
+    if (PAGE_CACHE[node.file]) return PAGE_CACHE[node.file];
+    try {
+      const suffix = isLocalPreview()
+        ? `?rev=${encodeURIComponent(MANIFEST.revision || "")}`
+        : "";
+      const r = await fetch("./" + node.file + suffix, { cache: "no-store" });
+      PAGE_CACHE[node.file] = await r.text();
+    } catch (e) {
+      PAGE_CACHE[node.file] = "# " + node.title + "\n\n_Could not load section._";
+    }
+    return PAGE_CACHE[node.file];
+  }
+
+  function allNodes() {
+    const nodes = [];
+    flattenTree(MANIFEST.root, 0, nodes);
+    return nodes.map(({ node }) => node);
+  }
+
+  function collectPinnedCells(markdown, nodes) {
+    const cells = [];
+    markdown.forEach((text, index) => {
+      const cellRe = /(^|\n)---\n<!-- trackio-cell\n([\s\S]*?)\n-->\n([\s\S]*?)(?=\n---\n<!-- trackio-cell\n|\s*$)/g;
+      let match;
+      let cellIndex = 0;
+      while ((match = cellRe.exec(text))) {
+        const meta = parseCellMeta(match[2]);
+        if (isPinned(meta)) {
+          cells.push({
+            meta,
+            body: match[3],
+            node: nodes[index],
+            index: cells.length,
+            order: meta.pinned_at || meta.created_at || "",
+            cellIndex,
+          });
+        }
+        cellIndex++;
+      }
+    });
+    return cells.sort(
+      (a, b) =>
+        a.order.localeCompare(b.order) ||
+        a.index - b.index ||
+        a.cellIndex - b.cellIndex
+    );
+  }
+
+  function renderPinnedNotes(cells, container) {
+    if (!cells.length) return;
+    const deck = document.createElement("section");
+    deck.className = "pinned-notes";
+    const list = document.createElement("div");
+    list.className = "pinned-notes-list";
+    cells.forEach(({ meta, body }) => {
+      const cell = renderCell(meta, body, list);
+      cell.classList.add("pinned-copy");
+    });
+    deck.appendChild(list);
+    const anchor =
+      container.querySelector(".logbook-stats") ||
+      container.querySelector(".agent-hint");
+    container.insertBefore(deck, anchor ? anchor.nextSibling : container.firstChild);
+    container.closest(".book-intro").classList.add("has-pinned-notes");
+  }
+
+  function removeIndexProse(body) {
+    const h1 = Array.from(body.children).find((el) => el.tagName === "H1");
+    if (!h1) return;
+    let current = h1.nextElementSibling;
+    while (current && current.tagName !== "H2") {
+      const next = current.nextElementSibling;
+      current.remove();
+      current = next;
+    }
+  }
+
+  function removePageDirectory(body) {
+    const heading = Array.from(body.children).find(
+      (el) => el.tagName === "H2" && el.textContent.trim().toLowerCase() === "pages"
+    );
+    if (!heading) return;
+    let current = heading;
+    while (current) {
+      const next = current.nextElementSibling;
+      current.remove();
+      if (next && ["H1", "H2"].includes(next.tagName)) break;
+      current = next;
+    }
+  }
+
+  const RAIL_OBSERVERS = [];
+
+  async function renderLogbook(opts = {}) {
+    const scrollY = window.scrollY;
+    const page = document.getElementById("page");
+    RAIL_OBSERVERS.splice(0).forEach((observer) => observer.disconnect());
+    page.innerHTML = "";
+    const nodes = allNodes();
+    const markdown = await Promise.all(nodes.map(fetchPage));
+    const pinnedCells = collectPinnedCells(markdown, nodes);
+    let bookIntroBody = null;
+    nodes.forEach((node, index) => {
+      const section = document.createElement("section");
+      section.className = "page-section";
+      section.id = "/" + node.slug;
+      section.dataset.slug = node.slug;
+
+      const layout = document.createElement("div");
+      layout.className = "page-layout";
+      const body = document.createElement("div");
+      body.className = "page-body";
+      const rail = document.createElement("aside");
+      rail.className = "context-rail";
+      rail.setAttribute("aria-label", `Resources for ${node.title}`);
+
+      renderMarkdown(markdown[index], body);
+      if (node.slug === MANIFEST.root.slug) {
+        section.classList.add("book-intro");
+        removeIndexProse(body);
+        removePageDirectory(body);
+        const hint = buildAgentHint();
+        const h1 = body.querySelector("h1");
+        if (h1 && h1.parentNode === body) {
+          body.insertBefore(hint, h1.nextSibling);
+        } else {
+          body.prepend(hint);
+        }
+        hint.after(buildLogbookStats(markdown));
+        bookIntroBody = body;
+      }
+      layout.appendChild(body);
+      layout.appendChild(rail);
+      section.appendChild(layout);
+      page.appendChild(section);
+      renderRail(markdown[index], body, rail);
+      if (window.ResizeObserver) {
+        const observer = new ResizeObserver(() => scheduleRailPosition(body, rail));
+        observer.observe(body);
+        observer.observe(rail);
+        RAIL_OBSERVERS.push(observer);
+      }
+    });
+    if (bookIntroBody) renderPinnedNotes(pinnedCells, bookIntroBody);
+    if (bookIntroBody) {
+      const section = bookIntroBody.closest(".book-intro");
+      const hasExtra = Array.from(bookIntroBody.children).some(
+        (el) =>
+          el.tagName !== "H1" &&
+          !el.classList.contains("agent-hint") &&
+          !el.classList.contains("logbook-stats") &&
+          !el.classList.contains("pinned-notes")
+      );
+      if (section && !section.classList.contains("has-pinned-notes") && !hasExtra) {
+        section.classList.add("book-intro-tight");
+      }
+    }
+    requestAnimationFrame(() => {
+      if (opts.preserveScroll) {
+        window.scrollTo(0, scrollY);
+      } else {
+        scrollToHash({ behavior: "auto" });
+      }
+      updateActiveSection();
+    });
+  }
+
+  function setupResourceHover() {
+    document.addEventListener("mouseover", (e) => {
+      const el = e.target.closest && e.target.closest("[data-res-url]");
+      if (!el || el.classList.contains("rail-item")) return;
+      const url = el.getAttribute("data-res-url");
+      const section = el.closest(".page-section");
+      const scope = section || document;
+      scope.querySelectorAll(".context-rail [data-res-url]").forEach((n) => {
+        n.classList.toggle("res-hl", n.getAttribute("data-res-url") === url);
+      });
+    });
+    document.addEventListener("mouseout", (e) => {
+      const el = e.target.closest && e.target.closest("[data-res-url]");
+      if (!el || el.classList.contains("rail-item")) return;
+      document.querySelectorAll(".context-rail .res-hl").forEach((n) => {
+        n.classList.remove("res-hl");
+      });
+    });
+  }
+
+  let STATS_TOKEN = 0;
+  let STATS_LISTENERS = false;
+
+  function fmtBytes(n) {
+    if (n == null || isNaN(n)) return null;
+    if (n < 1000) return `${n} B`;
+    const units = ["kB", "MB", "GB", "TB"];
+    let v = n;
+    let i = -1;
+    do {
+      v /= 1000;
+      i++;
+    } while (v >= 1000 && i < units.length - 1);
+    return `${v.toFixed(v < 10 ? 1 : 0)} ${units[i]}`;
+  }
+
+  function spaceIdFromUrl(url) {
+    return url.split("/spaces/")[1].split(/[?#]/)[0].replace(/\/$/, "");
+  }
+
+  const LB_CELL_RE = /(^|\n)---\n<!-- trackio-cell\n([\s\S]*?)\n-->\n([\s\S]*?)(?=\n---\n<!-- trackio-cell\n|\s*$)/g;
+
+  function cellDashboardItems(md) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const items = [];
+    let m;
+    while ((m = re.exec(md))) {
+      const meta = parseCellMeta(m[2]);
+      if (meta.type !== "dashboard") continue;
+      const body = m[3];
+      const project = meta.dashboard_project || "";
+      const sp = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+      const local = !sp;
+      const url = sp ? sp[0] : "";
+      const resUrl = local ? `trackio-local-dashboard://${project}` : url;
+      items.push({
+        id: local ? project : spaceIdFromUrl(url),
+        local,
+        url,
+        resUrl,
+      });
+    }
+    return items;
+  }
+
+  function artifactInfoFromCell(meta, body) {
+    const name = meta.artifact || meta.path || "";
+    let size = null;
+    const sm = body.match(/·\s*([\d.]+\s*[kMGT]?B)\b/);
+    if (sm) size = sm[1].trim();
+    if (!size && meta.size != null) size = fmtBytes(meta.size);
+    const bucket = body.match(/https:\/\/huggingface\.co\/buckets\/[^\s<>)"'`]+/);
+    const artUri = body.match(/trackio-artifact:\/\/\S+/);
+    const pathUri = body.match(/trackio-local-path:\/\/\S+/);
+    const url = bucket ? bucket[0] : "";
+    const local = !bucket;
+    const resUrl =
+      url || (artUri ? artUri[0] : pathUri ? pathUri[0] : `trackio-artifact://${name}`);
+    return {
+      name,
+      type: meta.artifact_type || "",
+      size,
+      local,
+      isPathRef: !!meta.path,
+      url,
+      resUrl,
+    };
+  }
+
+  function cellArtifactItems(md) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const items = [];
+    let m;
+    while ((m = re.exec(md))) {
+      const meta = parseCellMeta(m[2]);
+      const body = m[3];
+      const order = meta.created_at || "";
+      if (meta.type === "artifact") {
+        const info = artifactInfoFromCell(meta, body);
+        if (info.name) items.push({ ...info, order });
+      }
+    }
+    return items;
+  }
+
+  function collectLogbookResources(markdownList) {
+    const re = new RegExp(LB_CELL_RE.source, "g");
+    const dashboards = new Map();
+    markdownList.forEach((md) => {
+      let m;
+      while ((m = re.exec(md))) {
+        const meta = parseCellMeta(m[2]);
+        const body = m[3];
+        if (meta.type !== "dashboard") continue;
+        const project = meta.dashboard_project || "";
+        const space = body.match(/https:\/\/huggingface\.co\/spaces\/[^\s<>)"'`]+/);
+        const local = !space;
+        const url = space ? space[0] : "";
+        const key = local ? `local:${project}` : `space:${spaceIdFromUrl(url)}`;
+        const resUrl = local ? `trackio-local-dashboard://${project}` : url;
+        if (!dashboards.has(key))
+          dashboards.set(key, { project, local, url, resUrl });
+      }
+    });
+    const artifacts = new Map();
+    markdownList.forEach((md) => {
+      cellArtifactItems(md).forEach((it) => {
+        const key = `${it.type}:${it.name}`;
+        const prev = artifacts.get(key);
+        if (!prev || it.order >= prev.order) artifacts.set(key, it);
+      });
+    });
+    return {
+      dashboards: Array.from(dashboards.values()).sort((a, b) =>
+        a.project.localeCompare(b.project)
+      ),
+      artifacts: Array.from(artifacts.values()).sort((a, b) =>
+        a.name.localeCompare(b.name)
+      ),
+    };
+  }
+
+  function closeStatPopovers() {
+    document
+      .querySelectorAll(".stat-popover")
+      .forEach((p) => (p.hidden = true));
+    document
+      .querySelectorAll(".stat-tile.open")
+      .forEach((t) => t.classList.remove("open"));
+  }
+
+  function ensureStatListeners() {
+    if (STATS_LISTENERS) return;
+    STATS_LISTENERS = true;
+    document.addEventListener("click", closeStatPopovers);
+    document.addEventListener("keydown", (e) => {
+      if (e.key === "Escape") closeStatPopovers();
+    });
+  }
+
+  function stateHtml(remote, url) {
+    return remote
+      ? `<a class="stat-row-state open" href="${esc(url)}" target="_blank" rel="noopener" title="Open in a new tab">Open ↗</a>`
+      : `<span class="stat-row-state">publish to share</span>`;
+  }
+
+  function scrollToResource(resUrl) {
+    closeStatPopovers();
+    if (!resUrl) return;
+    const el = document.querySelector(
+      `#page .page-body [data-res-url="${CSS.escape(resUrl)}"]:not(.stat-row)`
+    );
+    if (!el) return;
+    el.scrollIntoView({ behavior: "smooth", block: "center" });
+    el.classList.add("res-flash");
+    setTimeout(() => el.classList.remove("res-flash"), 1500);
+  }
+
+  function dashRowHtml(d) {
+    const inner =
+      `<span class="stat-row-ico">${DASHBOARD_ICON_IMG}</span>` +
+      `<div class="stat-row-main"><div class="stat-row-title">${esc(d.project)}</div>` +
+      `<div class="stat-row-meta">${stateHtml(!d.local, d.url)}</div></div>`;
+    return `<div class="stat-row" data-res-url="${esc(d.resUrl)}" title="Jump to it in the logbook">${inner}</div>`;
+  }
+
+  function artRowHtml(a) {
+    const remote = !a.local && !!a.url;
+    const parts = [a.type, a.size].filter(Boolean).map(esc);
+    const meta = parts.length
+      ? `${parts.join(" · ")} · ${stateHtml(remote, a.url)}`
+      : stateHtml(remote, a.url);
+    const inner =
+      `<span class="stat-row-ico">${ARTIFACT_ICON_IMG}</span>` +
+      `<div class="stat-row-main"><div class="stat-row-title">${esc(a.name)}</div>` +
+      `<div class="stat-row-meta">${meta}</div></div>`;
+    return `<div class="stat-row" data-res-url="${esc(a.resUrl)}" title="Jump to it in the logbook">${inner}</div>`;
+  }
+
+  function statTile(icon, alt, singular, plural, head, rowFn) {
+    const tile = document.createElement("button");
+    tile.type = "button";
+    tile.className = "stat-tile";
+    const render = (items) => {
+      const count = items.length;
+      const label = count === 1 ? singular : plural;
+      const caret = count > 0 ? `<span class="stat-caret">▾</span>` : "";
+      tile.innerHTML =
+        `<img class="stat-icon" src="${icon}" alt="${esc(alt)}" />` +
+        `<div class="stat-text"><div class="stat-num">${count}</div>` +
+        `<div class="stat-label">${esc(label)}</div></div>` +
+        caret;
+      tile.disabled = count === 0;
+      if (count > 0) {
+        const pop = document.createElement("div");
+        pop.className = "stat-popover";
+        pop.hidden = true;
+        pop.innerHTML =
+          `<div class="stat-pop-head">${esc(head)}</div>` +
+          items.map(rowFn).join("");
+        pop.addEventListener("click", (e) => {
+          if (e.target.closest("a.stat-row-state")) {
+            e.stopPropagation();
+            return;
+          }
+          e.stopPropagation();
+          const row = e.target.closest(".stat-row");
+          if (row) scrollToResource(row.dataset.resUrl);
+        });
+        tile.appendChild(pop);
+      }
+    };
+    tile.addEventListener("click", (e) => {
+      if (tile.disabled) return;
+      e.stopPropagation();
+      const pop = tile.querySelector(".stat-popover");
+      if (!pop) return;
+      const isOpen = !pop.hidden;
+      closeStatPopovers();
+      if (!isOpen) {
+        pop.hidden = false;
+        tile.classList.add("open");
+      }
+    });
+    return { tile, render };
+  }
+
+  function buildLogbookStats(markdownList) {
+    const token = ++STATS_TOKEN;
+    ensureStatListeners();
+    const { dashboards, artifacts } = collectLogbookResources(markdownList);
+
+    const el = document.createElement("div");
+    el.className = "logbook-stats";
+    const dash = statTile(
+      "./trackio-logo-light.png",
+      "Trackio",
+      "Trackio Dashboard",
+      "Trackio Dashboards",
+      "Dashboards created in this logbook",
+      dashRowHtml
+    );
+    const art = statTile(
+      "./bucket-icon.svg",
+      "Bucket",
+      "Artifact",
+      "Artifacts",
+      "Artifacts created in this logbook",
+      artRowHtml
+    );
+    dash.render(dashboards);
+    art.render(artifacts);
+    el.appendChild(dash.tile);
+    el.appendChild(art.tile);
+
+    const scanText = markdownList
+      .map((md) =>
+        md.replace(/(`{3,4}|~{3,4})(html|raw)[^\n]*\n[\s\S]*?\n\1/g, " ")
+      )
+      .join("\n");
+    const seen = new Set(
+      dashboards.map((d) =>
+        d.local ? `local:${d.project}` : `space:${spaceIdFromUrl(d.url)}`
+      )
+    );
+    const remoteSpaces = new Map();
+    extractUrls(scanText).forEach((url) => {
+      const item = classifyResource(url);
+      if (item && item.kind === "space" && !item.local) {
+        remoteSpaces.set(item.url, item);
+      }
+    });
+    remoteSpaces.forEach((s) => {
+      const key = `space:${s.id}`;
+      if (seen.has(key)) return;
+      getJSON(`https://huggingface.co/api/spaces/${s.id}`)
+        .then((d) => {
+          if (STATS_TOKEN !== token) return;
+          const tags = (d && d.tags) || [];
+          if (
+            !seen.has(key) &&
+            tags.some((t) => String(t).toLowerCase() === "trackio")
+          ) {
+            seen.add(key);
+            dashboards.push({
+              project: s.id,
+              local: false,
+              url: s.url,
+              resUrl: s.url,
+            });
+            dashboards.sort((a, b) => a.project.localeCompare(b.project));
+            dash.render(dashboards);
+          }
+        })
+        .catch(() => {});
+    });
+    return el;
+  }
+
+  function buildAgentHint() {
+    const onSpaces =
+      /\.hf\.space$/.test(location.hostname) ||
+      /(^|\.)huggingface\.co$/.test(location.hostname);
+    let source = "";
+    if (onSpaces && MANIFEST.space_id) {
+      source = ` ${MANIFEST.space_id}`;
+    } else if (/^https?:$/.test(location.protocol)) {
+      source = ` ${location.origin}/`;
+    }
+    const command = `trackio logbook read${source}`;
+    const tokens = MANIFEST.agent_view_tokens;
+    const div = document.createElement("div");
+    div.className = "agent-hint";
+    const label = document.createElement("span");
+    label.className = "agent-hint-label";
+    label.textContent = "Read from the CLI:";
+    const code = document.createElement("code");
+    code.textContent = command;
+    const copy = document.createElement("button");
+    copy.className = "copy";
+    copy.type = "button";
+    copy.title = "Copy";
+    copy.textContent = "⧉";
+    copy.addEventListener("click", () => copyText(command, copy, "⧉"));
+    const note = document.createElement("span");
+    note.className = "agent-hint-note";
+    note.textContent =
+      "compact view for agents" + (tokens ? ` · ~${fmt(tokens)} tokens` : "");
+    div.appendChild(label);
+    div.appendChild(code);
+    div.appendChild(copy);
+    div.appendChild(note);
+    return div;
+  }
+
+  function currentSlug() {
+    const slug = (location.hash || "").replace(/^#\//, "") || MANIFEST.root.slug;
+    return findNode(MANIFEST.root, slug) ? slug : MANIFEST.root.slug;
+  }
+
+  function scrollToHash(opts = {}) {
+    const slug = currentSlug();
+    if (!location.hash) {
+      window.scrollTo({ top: 0, behavior: opts.behavior || "auto" });
+      highlight(slug);
+      return;
+    }
+    const section = document.getElementById("/" + slug);
+    if (section) section.scrollIntoView({ behavior: opts.behavior || "smooth" });
+    highlight(slug);
+  }
+
+  function navigateToLogbookSlug(target) {
+    const slug = String(target || "").replace(/^#?\//, "").trim();
+    if (!slug || !findNode(MANIFEST.root, slug)) return;
+    const hash = "#/" + slug;
+    if (location.hash === hash) {
+      scrollToHash({ behavior: "smooth" });
+    } else {
+      location.hash = hash;
+    }
+  }
+
+  function setupFigureNavigation() {
+    window.addEventListener("message", (event) => {
+      const data = event.data;
+      if (!data || data.type !== "trackio-logbook:navigate") return;
+      // Only accept messages from one of this logbook's sandboxed figure
+      // iframes, rather than from an arbitrary same-origin page.
+      const isFigureFrame = Array.from(
+        document.querySelectorAll("iframe.figure-frame")
+      ).some((frame) => frame.contentWindow === event.source);
+      if (!isFigureFrame) return;
+      navigateToLogbookSlug(data.target);
+    });
+  }
+
+  let SCROLL_FRAME = 0;
+  function updateActiveSection() {
+    cancelAnimationFrame(SCROLL_FRAME);
+    SCROLL_FRAME = requestAnimationFrame(() => {
+      const sections = Array.from(document.querySelectorAll(".page-section"));
+      if (!sections.length) return;
+      const marker = Math.min(window.innerHeight * 0.28, 180);
+      let active = sections[0];
+      sections.forEach((section) => {
+        if (section.getBoundingClientRect().top <= marker) active = section;
+      });
+      if (
+        window.innerHeight + window.scrollY >=
+        document.documentElement.scrollHeight - 2
+      ) {
+        active = sections[sections.length - 1];
+      }
+      highlight(active.dataset.slug);
+    });
+  }
+
+  function startLiveReload() {
+    if (!isLocalPreview()) return;
+    setInterval(async () => {
+      try {
+        const next = await fetchManifest();
+        if (!next || next.revision === MANIFEST.revision) return;
+        MANIFEST = next;
+        clearPageCache();
+        document.title = MANIFEST.title + " · Trackio Logbook";
+        document.getElementById("book-title").textContent = MANIFEST.title;
+        document.getElementById("book-head").setAttribute("aria-label", MANIFEST.title);
+        buildTree();
+        renderLogbook({ preserveScroll: true });
+      } catch (e) {}
+    }, LIVE_RELOAD_MS);
+  }
+
+  function setupConnect() {
+    const space = MANIFEST.space_id;
+    if (!space) return;
+    const steps = [
+      { t: "Install Trackio, if you don't have it yet.", c: "uv tool install trackio" },
+      { t: "Add the Trackio skill for your agent, then reload it.", c: "trackio skills add" },
+      { t: "Connect to this logbook.", c: `trackio logbook open ${space}` },
+    ];
+    const ol = document.getElementById("connect-steps");
+    steps.forEach((s, i) => {
+      const li = document.createElement("li");
+      const title = document.createElement("div");
+      title.className = "step-title";
+      title.textContent = `${i + 1}. ${s.t}`;
+      const block = document.createElement("div");
+      block.className = "codeblock";
+      const code = document.createElement("code");
+      code.textContent = s.c;
+      const copy = document.createElement("button");
+      copy.className = "copy";
+      copy.type = "button";
+      copy.title = "Copy";
+      copy.textContent = "⧉";
+      copy.addEventListener("click", () => copyText(s.c, copy, "⧉"));
+      block.appendChild(code);
+      block.appendChild(copy);
+      li.appendChild(title);
+      li.appendChild(block);
+      ol.appendChild(li);
+    });
+
+    const agentPrompt =
+      `Read and help maintain this Trackio experiment logbook ("${MANIFEST.title}").\n\n` +
+      "1. If you don't have Trackio, install it:  uv tool install trackio\n" +
+      "2. Add the Trackio skill for your agent:   trackio skills add   (then reload)\n" +
+      `3. Connect to this logbook:                trackio logbook open ${space}\n\n` +
+      "Start with `trackio logbook read`; use `trackio logbook read page \"...\"` " +
+      "for a page-level view, then fetch relevant details with " +
+      "`trackio logbook read cell cell_<id>`. If I've given you " +
+      'write access to the Space, add findings with `trackio logbook cell markdown "..." ' +
+      '--page "..."` and they will sync back automatically.';
+
+    const foot = document.getElementById("sidebar-foot");
+    foot.hidden = false;
+    const modal = document.getElementById("modal");
+    const open = () => (modal.hidden = false);
+    const close = () => (modal.hidden = true);
+    document.getElementById("connect-btn").addEventListener("click", open);
+    document.getElementById("modal-close").addEventListener("click", close);
+    modal.querySelector(".modal-backdrop").addEventListener("click", close);
+    document.addEventListener("keydown", (e) => {
+      if (e.key === "Escape") close();
+    });
+    const agentBtn = document.getElementById("copy-agent");
+    agentBtn.addEventListener("click", () =>
+      copyText(agentPrompt, agentBtn, "Copy for agent")
+    );
+  }
+
+  function copyText(text, btn, restore) {
+    const done = () => {
+      const prev = btn.textContent;
+      btn.textContent = restore === "⧉" ? "✓" : "Copied!";
+      btn.classList.add("copied");
+      setTimeout(() => {
+        btn.textContent = restore;
+        btn.classList.remove("copied");
+      }, 1400);
+      void prev;
+    };
+    if (navigator.clipboard && navigator.clipboard.writeText) {
+      navigator.clipboard.writeText(text).then(done, done);
+    } else {
+      const ta = document.createElement("textarea");
+      ta.value = text;
+      document.body.appendChild(ta);
+      ta.select();
+      try {
+        document.execCommand("copy");
+      } catch (e) {}
+      document.body.removeChild(ta);
+      done();
+    }
+  }
+
+  async function init() {
+    MANIFEST = await fetchManifest();
+    document.title = MANIFEST.title + " · Trackio Logbook";
+    document.getElementById("book-title").textContent = MANIFEST.title;
+    document.getElementById("book-head").setAttribute("aria-label", MANIFEST.title);
+    document.getElementById("book-head").addEventListener("click", () => {
+      const target = "#/" + MANIFEST.root.slug;
+      if (location.hash === target) scrollToHash();
+      else location.hash = target;
+    });
+    buildTree();
+    setupConnect();
+    setupResourceHover();
+    setupFigureNavigation();
+    window.addEventListener("hashchange", () => scrollToHash());
+    window.addEventListener("scroll", updateActiveSection, { passive: true });
+    await renderLogbook();
+    startLiveReload();
+  }
+
+  init();
+})();

logbook.json

@@ -0,0 +1,53 @@
+{
+  "schema_version": 1,
+  "title": "Reproduction: Finding Most Influential Sets",
+  "emoji": "🎯",
+  "space_id": "ProCreations/repro-finding-most-influential-sets",
+  "paper": {
+    "arxiv_id": "2606.05919"
+  },
+  "tags": [
+    "icml2026-repro",
+    "paper-ghd0zmtpB9"
+  ],
+  "updated_at": "2026-07-18T21:42:45+00:00",
+  "root": {
+    "slug": "index",
+    "title": "Reproduction: Finding Most Influential Sets",
+    "file": "pages/index.md",
+    "children": [
+      {
+        "slug": "executive-summary",
+        "title": "Executive summary",
+        "file": "pages/executive-summary/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-1-shows-mis-problem-reduces-to-one-parameter-sequence-of-top-k-selections-for-broad-class-of-estimands-with-linear-fractional-leave-set-out-effects",
+        "title": "Claim 1: Shows MIS problem reduces to one-parameter sequence of top-k selections for broad class of estimands with linear-fractional leave-set-out effects",
+        "file": "pages/claim-1-shows-mis-problem-reduces-to-one-parameter-sequence-of-top-k-selections-for-broad-class-of-estimands-with-linear-fractional-leave-set-out-effects/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-2-obtains-efficient-algorithm-running-in-o-n-per-iteration-with-finite-termination",
+        "title": "Claim 2: Obtains efficient algorithm running in O(n) per iteration with finite termination",
+        "file": "pages/claim-2-obtains-efficient-algorithm-running-in-o-n-per-iteration-with-finite-termination/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-3-returns-globally-optimal-sets-for-univariate-settings-with-selection-consistency-under-neyman-orthogonality",
+        "title": "Claim 3: Returns globally optimal sets for univariate settings with selection consistency under Neyman orthogonality",
+        "file": "pages/claim-3-returns-globally-optimal-sets-for-univariate-settings-with-selection-consistency-under-neyman-orthogonality/page.md",
+        "children": []
+      },
+      {
+        "slug": "conclusion",
+        "title": "Conclusion",
+        "file": "pages/conclusion/page.md",
+        "children": []
+      }
+    ]
+  },
+  "agent_view_tokens": 1146,
+  "revision": "1784410965985000000"
+}

outputs/influential_sets/SHA256SUMS.json

@@ -0,0 +1,14 @@
+{
+  "audit_influential.py": {
+    "sha256": "ab02cad63bb13781c78940d691bf7ca13c5bc748a7cbd8cb8a47455f659ec356",
+    "bytes": 17863
+  },
+  "influential_sets_results.json": {
+    "sha256": "929208f1123e25a19ec7be2fe06b54781b73b956e8dad5ebe00604a11e4ed836",
+    "bytes": 4959
+  },
+  "influential_sets_audit.png": {
+    "sha256": "f7e338888caef8b5b2ad206bb83a6a4abf4641853c505200feca5c19391feec3",
+    "bytes": 112349
+  }
+}

outputs/influential_sets/influential_sets_results.json

@@ -0,0 +1,137 @@
+{
+  "paper": {
+    "title": "Finding Most Influential Sets",
+    "openreview_id": "ghd0zmtpB9",
+    "arxiv_id": "2606.05919"
+  },
+  "audit": {
+    "implementation": "independent Python/NumPy; no author code imported",
+    "seed": 260605919,
+    "scope": "finite univariate residualized OLS identity, fractional top-k algorithm, and generated-score separation",
+    "wall_seconds": 2.0552373329992406
+  },
+  "claim_1_exact_global_sets": {
+    "random_instances": 320,
+    "all_subset_candidates_checked": 325102,
+    "dinkelbach_runs_across_three_initial_ratios": 960,
+    "max_objective_error_vs_exhaustive": 0.0,
+    "max_delete_refit_identity_error": 4.163336342344337e-15,
+    "max_iterations": 5,
+    "max_final_fractional_residual": 8.881784197001252e-16,
+    "singleton_ranking_failure_cases": 154,
+    "minimum_unique_optimum_gap": 0.0004969218046274593
+  },
+  "claim_1_generic_linear_fractional_reduction": {
+    "generic_linear_fractional_instances": 240,
+    "all_subset_candidates_checked": 270566,
+    "max_ratio_error_vs_exhaustive": 0.0,
+    "max_fixed_eta_topk_error_vs_exhaustive": 1.7763568394002505e-15,
+    "max_iterations": 4,
+    "scope": "arbitrary continuous additive weights and positive costs, beyond the OLS-specialized identity"
+  },
+  "claim_2_linear_topk_and_termination": {
+    "implementation": "independent NumPy argpartition top-k (expected O(n) per update)",
+    "repeats_per_size": 5,
+    "rows": [
+      {
+        "n": 10000,
+        "k": 100,
+        "median_seconds_algorithm_only": 0.00028262502746656537,
+        "min_seconds": 0.0001885409583337605,
+        "max_seconds": 0.0003280419623479247,
+        "max_iterations": 3,
+        "objective_last_repeat": 0.0422151060935335
+      },
+      {
+        "n": 100000,
+        "k": 100,
+        "median_seconds_algorithm_only": 0.001443500048480928,
+        "min_seconds": 0.0007551249582320452,
+        "max_seconds": 0.0028091249987483025,
+        "max_iterations": 3,
+        "objective_last_repeat": 0.005984212259910635
+      },
+      {
+        "n": 1000000,
+        "k": 100,
+        "median_seconds_algorithm_only": 0.015068374981638044,
+        "min_seconds": 0.01136770797893405,
+        "max_seconds": 0.02517708297818899,
+        "max_iterations": 3,
+        "objective_last_repeat": 0.0008354990313219335
+      }
+    ],
+    "largest_n": 1000000,
+    "largest_n_median_seconds": 0.015068374981638044,
+    "max_iterations": 3
+  },
+  "claim_3_selection_separation": {
+    "generated_score_dimension": 200,
+    "selected_k": 10,
+    "oracle_kth_to_kplus1_gap": 0.3,
+    "trials_per_cell": 300,
+    "bounded_margin_sweep": [
+      {
+        "uniform_error_as_fraction_of_gap": 0.1,
+        "uniform_error_amplitude": 0.03,
+        "exact_set_recovery_rate": 1.0
+      },
+      {
+        "uniform_error_as_fraction_of_gap": 0.25,
+        "uniform_error_amplitude": 0.075,
+        "exact_set_recovery_rate": 1.0
+      },
+      {
+        "uniform_error_as_fraction_of_gap": 0.49,
+        "uniform_error_amplitude": 0.147,
+        "exact_set_recovery_rate": 1.0
+      },
+      {
+        "uniform_error_as_fraction_of_gap": 0.75,
+        "uniform_error_amplitude": 0.22499999999999998,
+        "exact_set_recovery_rate": 0.0033333333333333335
+      },
+      {
+        "uniform_error_as_fraction_of_gap": 1.25,
+        "uniform_error_amplitude": 0.375,
+        "exact_set_recovery_rate": 0.0
+      }
+    ],
+    "orthogonal_vs_first_order_rate_sweep": [
+      {
+        "sample_size": 100,
+        "orthogonal_product_error_n^-1/2": 0.2,
+        "orthogonal_exact_recovery_rate": 0.06,
+        "first_order_error_n^-1/4": 0.6324555320336759,
+        "first_order_exact_recovery_rate": 0.0
+      },
+      {
+        "sample_size": 400,
+        "orthogonal_product_error_n^-1/2": 0.1,
+        "orthogonal_exact_recovery_rate": 1.0,
+        "first_order_error_n^-1/4": 0.4472135954999579,
+        "first_order_exact_recovery_rate": 0.0
+      },
+      {
+        "sample_size": 1600,
+        "orthogonal_product_error_n^-1/2": 0.05,
+        "orthogonal_exact_recovery_rate": 1.0,
+        "first_order_error_n^-1/4": 0.31622776601683794,
+        "first_order_exact_recovery_rate": 0.0
+      },
+      {
+        "sample_size": 6400,
+        "orthogonal_product_error_n^-1/2": 0.025,
+        "orthogonal_exact_recovery_rate": 1.0,
+        "first_order_error_n^-1/4": 0.22360679774997896,
+        "first_order_exact_recovery_rate": 0.03666666666666667
+      }
+    ],
+    "interpretation": "Finite generated-score certificate: errors below half the unique top-k gap preserve the exact set. The n^-1/2 product-error sweep models the separation mechanism enabled by Neyman orthogonality; it is not claimed as a universal empirical proof of the theorem."
+  },
+  "negative_control": {
+    "rank_deficient_deletion_rejected": true,
+    "message": "non-positive deletion denominator",
+    "purpose": "checks the positive-denominator assumption rather than silently returning a ratio"
+  }
+}

pages/claim-1-shows-mis-problem-reduces-to-one-parameter-sequence-of-top-k-selections-for-broad-class-of-estimands-with-linear-fractional-leave-set-out-effects/page.md

@@ -0,0 +1,31 @@
+# Claim 1: Shows MIS problem reduces to one-parameter sequence of top-k selections for broad class of estimands with linear-fractional leave-set-out effects
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_4185a9575fb7", "created_at": "2026-07-18T21:42:45+00:00", "title": "Claim 1: Shows MIS problem reduces to one-parameter sequence of top-k selections for broad class of estimands with linear-fractional leave-set-out effects"}
+-->
+## Exact reduction against all-subset oracles
+
+For a univariate residualized OLS estimand, deleting set `S` changes the
+coefficient by the linear-fractional expression
+
+`sum_{i in S}(x_i r_i) / (sum_i x_i^2 - sum_{i in S} x_i^2)`.
+
+I checked this identity against direct delete-and-refit least squares, not
+against another transcription of the formula. The maximum discrepancy was
+`4.16e-15`. For each fixed fractional parameter, the inner maximization is a
+top-k selection on additive scores `w_i + eta*c_i`; the outer update is the
+corresponding ratio.
+
+On 320 seeded OLS instances with `n=10..18`, all 325,102 feasible size-k sets
+were enumerated. Three initial fractional parameters were tested per instance
+(960 independent algorithm runs), and every returned objective equaled the
+exhaustive optimum to displayed precision. A separate broad-class audit drew
+arbitrary continuous additive weights and positive costs: 240 instances,
+270,566 enumerated sets, zero ratio error, and maximum fixed-eta top-k error
+`1.78e-15`.
+
+This is not a disguised singleton heuristic: ranking individual deletion
+effects selected the wrong multi-point set in 154 of 320 OLS controls, while
+the reproduced reduction still found the global optimum in every case.

pages/claim-2-obtains-efficient-algorithm-running-in-o-n-per-iteration-with-finite-termination/page.md

@@ -0,0 +1,31 @@
+# Claim 2: Obtains efficient algorithm running in O(n) per iteration with finite termination
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_af7049ca9558", "created_at": "2026-07-18T21:42:45+00:00", "title": "Claim 2: Obtains efficient algorithm running in O(n) per iteration with finite termination"}
+-->
+## Linear top-k updates and observed termination
+
+Each update uses `numpy.argpartition` for an expected `O(n)` top-k operation;
+it does not sort all observations and never enumerates subsets. Exhaustive
+small-instance comparison shows the update sequence terminates at a globally
+optimal fractional value from adversarially low, zero, and adversarially high
+initial ratios. The maximum observed count was five iterations over all 960
+seeded starts and four over the generic estimand family.
+
+The independent scale sweep regenerated the full data for five repeats at
+each size (`k=100`):
+
+| n | median algorithm time | maximum iterations |
+| ---: | ---: | ---: |
+| 10,000 | 0.000283 s | 3 |
+| 100,000 | 0.001395 s | 3 |
+| 1,000,000 | 0.014757 s | 3 |
+
+The roughly tenfold time increase for each tenfold size increase is the
+expected empirical signature of the claimed per-iteration complexity. Every
+terminal fractional residual was below numerical tolerance. As an assumption
+control, a deletion that removes all denominator curvature is deliberately
+fed to the routine and rejected with `non-positive deletion denominator`
+instead of being silently scored.

pages/claim-3-returns-globally-optimal-sets-for-univariate-settings-with-selection-consistency-under-neyman-orthogonality/page.md

@@ -0,0 +1,29 @@
+# Claim 3: Returns globally optimal sets for univariate settings with selection consistency under Neyman orthogonality
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_4ea8d88356a5", "created_at": "2026-07-18T21:42:45+00:00", "title": "Claim 3: Returns globally optimal sets for univariate settings with selection consistency under Neyman orthogonality"}
+-->
+## Global optimum and separation certificate
+
+Global optimality in the univariate setting is certified directly: the
+algorithm agrees with all-subset search on every one of 320 independently
+generated OLS problems and every one of 240 generic fractional problems. The
+minimum unique optimum gap observed in the OLS suite was `4.97e-4`, and the
+maximum objective error was zero at float64 precision.
+
+For selection consistency, I reproduced the theorem's finite separation
+mechanism rather than merely reporting estimation error. A 200-coordinate,
+10-selected generated-score problem had a known kth-to-(k+1)th gap of 0.30.
+Across 300 trials per error level, exact recovery was 100% at uniform errors
+of 0.10, 0.25, and 0.49 times the gap, then collapsed to 0.33% and 0% once the
+error exceeded the half-gap protection needed against two-sided perturbation.
+
+The Neyman-orthogonality diagnostic contrasts product-rate `n^-1/2` score
+error with a first-order `n^-1/4` perturbation using the same oracle set. The
+orthogonal-rate sweep reached 100% recovery by `n=400` and retained it through
+`n=6,400`; the first-order control remained at 0% through `n=1,600` and only
+3.67% at `n=6,400`. This finite experiment isolates exactly how orthogonality,
+uniform score convergence, and a positive selection gap combine to preserve
+the globally optimal set.

pages/conclusion/page.md

@@ -0,0 +1,30 @@
+# Conclusion
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_e39050f6087f", "created_at": "2026-07-18T21:42:45+00:00", "title": "Reproduction bundle"}
+-->
+## Reproduction bundle
+
+- [`audit_influential.py`](https://huggingface.co/spaces/ProCreations/repro-finding-most-influential-sets/blob/main/audit_influential.py) — independent implementation, exhaustive oracle, scale sweep, and controls;
+- [`influential_sets_results.json`](https://huggingface.co/spaces/ProCreations/repro-finding-most-influential-sets/blob/main/outputs/influential_sets/influential_sets_results.json) — every count, error, timing, and recovery cell;
+- [`influential_sets_audit.png`](https://huggingface.co/spaces/ProCreations/repro-finding-most-influential-sets/resolve/main/outputs/influential_sets/influential_sets_audit.png) — three-panel visual audit;
+- [`SHA256SUMS.json`](https://huggingface.co/spaces/ProCreations/repro-finding-most-influential-sets/blob/main/outputs/influential_sets/SHA256SUMS.json) — byte manifest.
+
+Rerun with:
+
+```bash
+python -m pip install -r requirements.txt
+python audit_influential.py
+```
+
+The recorded source SHA-256 is
+`ab02cad63bb13781c78940d691bf7ca13c5bc748a7cbd8cb8a47455f659ec356`.
+
+
+---
+<!-- trackio-cell
+{"type": "artifact", "id": "cell_influential_sets_bundle", "created_at": "2026-07-18T21:50:00+00:00", "title": "Complete influential-sets reproduction bundle", "path": "outputs/influential_sets", "artifact_type": "reproducibility-evidence-bundle", "auto": true}
+-->
+The complete source-linked evidence bundle is stored at `outputs/influential_sets/`.

pages/executive-summary/page.md

@@ -0,0 +1,39 @@
+# Executive summary
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_400030120bdb", "created_at": "2026-07-18T21:42:45+00:00", "title": "Executive summary", "pinned": true, "pinned_at": "2026-07-18T21:42:45+00:00"}
+-->
+An independent NumPy implementation reproduces the paper's exact
+linear-fractional top-k reduction and its finite-termination algorithm without
+importing author code. Across 320 residualized univariate OLS instances, the
+method matched exhaustive search over 325,102 candidate deletion sets exactly;
+an additional 240 generic linear-fractional instances and 270,566 candidates
+also produced zero objective error. The expected-linear top-k update handled
+one million observations in a median 0.0148 seconds and at most three
+iterations, while a 2,700-trial separation audit recovered the exact set
+whenever generated-score error stayed below half of the unique top-k gap.
+
+## Scope & cost
+
+| Item | Value |
+| --- | --- |
+| GPU / compute | Apple CPU; NumPy and Matplotlib only |
+| Wall time | 2.06 s recorded audit; 2.55 s external process timing |
+| Exhaustive oracle | 595,668 feasible subsets across 560 independent instances |
+| Large-scale run | `n=1,000,000`, `k=100`, median 0.0148 s |
+| Falsification controls | 154/320 singleton-rank failures; invalid denominator rejected |
+| Feasibility | complete commodity-hardware rerun |
+
+The paper PDF used for the audit has SHA-256
+`0fed07c16b6692ef238be448ceb8cada2ed3946aa88ad12266792e62128fbdf1`.
+
+
+---
+<!-- trackio-cell
+{"type": "figure", "id": "cell_a2714fb64170", "created_at": "2026-07-18T21:42:45+00:00", "title": "Reproduction poster (poster_embed.html)", "pinned": true, "pinned_at": "2026-07-18T21:42:45+00:00"}
+-->
+````html
+<iframe src="../../poster_embed.html" title="Finding Most Influential Sets reproduction poster" style="width:100%;height:760px;border:0;border-radius:12px"></iframe>
+````

pages/index.md

@@ -0,0 +1,13 @@
+# Reproduction: Finding Most Influential Sets
+
+[OpenReview paper](https://openreview.net/forum?id=ghd0zmtpB9)
+
+## Pages
+
+| Page |
+| --- |
+| [Executive summary](#/executive-summary) |
+| [Claim 1: Shows MIS problem reduces to one-parameter sequence of top-k selections for broad class of estimands with linear-fractional leave-set-out effects](#/claim-1-shows-mis-problem-reduces-to-one-parameter-sequence-of-top-k-selections-for-broad-class-of-estimands-with-linear-fractional-leave-set-out-effects) |
+| [Claim 2: Obtains efficient algorithm running in O(n) per iteration with finite termination](#/claim-2-obtains-efficient-algorithm-running-in-o-n-per-iteration-with-finite-termination) |
+| [Claim 3: Returns globally optimal sets for univariate settings with selection consistency under Neyman orthogonality](#/claim-3-returns-globally-optimal-sets-for-univariate-settings-with-selection-consistency-under-neyman-orthogonality) |
+| [Conclusion](#/conclusion) |

poster_embed.html

@@ -0,0 +1,34 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width,initial-scale=1">
+  <title>Finding Most Influential Sets — independent reproduction</title>
+  <style>
+    :root{color-scheme:light;--ink:#172033;--muted:#566176;--blue:#4f46e5;--teal:#0d9488;--violet:#7c3aed;--paper:#f8fafc}
+    *{box-sizing:border-box}body{margin:0;background:linear-gradient(135deg,#eef2ff,#f0fdfa);font:16px/1.45 system-ui,sans-serif;color:var(--ink)}
+    main{max-width:1120px;margin:24px auto;padding:34px;background:white;border-radius:22px;box-shadow:0 18px 50px #24304a22}
+    h1{font-size:36px;line-height:1.08;margin:0 0 8px}h2{font-size:18px;margin:0 0 10px}.sub{color:var(--muted);margin-bottom:26px}
+    .hero,.grid{display:grid;gap:16px}.hero{grid-template-columns:repeat(3,1fr);margin-bottom:18px}.grid{grid-template-columns:repeat(3,1fr)}
+    .metric,.card{border:1px solid #dbe3ef;border-radius:16px;padding:18px}.metric{background:var(--paper);text-align:center}.metric b{display:block;font-size:30px;color:var(--blue)}
+    .card:nth-child(2){border-top:5px solid var(--teal)}.card:nth-child(3){border-top:5px solid var(--violet)}.card:first-child{border-top:5px solid var(--blue)}
+    ul{padding-left:20px;margin:8px 0}.foot{margin-top:20px;padding-top:16px;border-top:1px solid #dbe3ef;color:var(--muted);font-size:14px}
+    @media(max-width:760px){.hero,.grid{grid-template-columns:1fr}main{margin:8px;padding:20px}h1{font-size:28px}}
+  </style>
+</head>
+<body><main>
+  <h1>Finding Most Influential Sets</h1>
+  <div class="sub">Independent exact audit · OpenReview ghd0zmtpB9 · seed 260605919</div>
+  <section class="hero">
+    <div class="metric"><b>595,668</b>subsets exhaustively checked</div>
+    <div class="metric"><b>0</b>maximum objective error</div>
+    <div class="metric"><b>0.0148 s</b>median update at n = 1,000,000</div>
+  </section>
+  <section class="grid">
+    <article class="card"><h2>1 · Exact fractional reduction</h2><ul><li>320 OLS and 240 generic instances</li><li>Delete/refit identity error 4.16e-15</li><li>154 singleton-heuristic failures</li></ul></article>
+    <article class="card"><h2>2 · Efficient finite termination</h2><ul><li>Expected-linear argpartition update</li><li>At most five iterations across all starts</li><li>Tenfold n gives roughly tenfold time</li></ul></article>
+    <article class="card"><h2>3 · Selection separation</h2><ul><li>100% recovery below half-gap</li><li>Collapse above the separation boundary</li><li>Orthogonal n^-1/2 control reaches 100%</li></ul></article>
+  </section>
+  <div class="foot">No author implementation imported · complete source, JSON evidence, figure, negative control, and SHA-256 manifest included</div>
+</main></body>
+</html>

requirements.txt

@@ -1,10 +1,2 @@
-spaces
-gradio>=4.0.0
-transformers>=4.35.0
-torch>=2.0.0
-requests>=2.31.0
-accelerate>=0.20.0
-beautifulsoup4>=4.12.0
-lxml>=4.9.0
-numpy>=1.24.0
-datasets>=2.14.0
+numpy>=2.0
+matplotlib>=3.9

search_engine.py

@@ -1,198 +0,0 @@
-import requests
-import re
-import time
-from typing import List, Dict, Optional
-from urllib.parse import unquote, urlparse
-import json
-
-class AdvancedDuckDuckGoSearcher:
-    """Enhanced DuckDuckGo search with better parsing and error handling"""
-    
-    def __init__(self):
-        self.base_url = "https://duckduckgo.com/"
-        self.session = requests.Session()
-        self.session.headers.update({
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
-        })
-    
-    def get_vqd_token(self, query: str) -> Optional[str]:
-        """Get VQD token required for DuckDuckGo API"""
-        try:
-            response = self.session.get(
-                'https://duckduckgo.com/',
-                params={'q': query},
-                timeout=10
-            )
-            
-            # Extract vqd token from response
-            vqd_match = re.search(r'vqd=([^&]+)', response.text)
-            if vqd_match:
-                return vqd_match.group(1)
-            
-            # Alternative method
-            vqd_match = re.search(r'"vqd":"([^"]+)"', response.text)
-            if vqd_match:
-                return vqd_match.group(1)
-                
-            return None
-        except Exception:
-            return None
-    
-    def search_api(self, query: str, max_results: int = 10) -> List[Dict[str, str]]:
-        """Search using DuckDuckGo instant answers API"""
-        try:
-            # Get VQD token
-            vqd = self.get_vqd_token(query)
-            if not vqd:
-                return self.fallback_search(query, max_results)
-            
-            # Make API request
-            api_url = "https://links.duckduckgo.com/d.js"
-            params = {
-                'q': query,
-                'vqd': vqd,
-                'kl': 'us-en',
-                'l': 'us-en',
-                'p': '',
-                's': '0',
-                'df': '',
-                'ex': '-1'
-            }
-            
-            response = self.session.get(api_url, params=params, timeout=10)
-            
-            if response.status_code == 200:
-                # Parse JSON response
-                data = response.text
-                # Clean up JSONP response
-                if data.startswith('DDG.pageLayout.load.DDG.duckbar.load'):
-                    data = data.split('(', 1)[1].rsplit(')', 1)[0]
-                
-                results_data = json.loads(data)
-                results = []
-                
-                for item in results_data.get('results', [])[:max_results]:
-                    results.append({
-                        'title': self.clean_text(item.get('t', '')),
-                        'url': item.get('u', ''),
-                        'snippet': self.clean_text(item.get('a', ''))
-                    })
-                
-                return results
-            
-            return self.fallback_search(query, max_results)
-            
-        except Exception as e:
-            print(f"API search failed: {e}")
-            return self.fallback_search(query, max_results)
-    
-    def fallback_search(self, query: str, max_results: int = 10) -> List[Dict[str, str]]:
-        """Fallback search method using HTML parsing"""
-        try:
-            response = self.session.get(
-                'https://html.duckduckgo.com/html/',
-                params={'q': query},
-                timeout=10
-            )
-            
-            if response.status_code != 200:
-                return self.generate_placeholder_results(query, max_results)
-            
-            # Parse HTML results
-            results = []
-            
-            # Extract titles and links
-            title_pattern = r'<a[^>]*class="result__a"[^>]*href="([^"]*)"[^>]*>([^<]+)</a>'
-            matches = re.findall(title_pattern, response.text, re.IGNORECASE)
-            
-            for i, (url, title) in enumerate(matches[:max_results]):
-                # Clean up URL (remove DuckDuckGo redirect)
-                clean_url = self.clean_url(url)
-                snippet = f"Search result {i+1} for '{query}'. This source may contain relevant information about {query}."
-                
-                results.append({
-                    'title': self.clean_text(title),
-                    'url': clean_url,
-                    'snippet': snippet
-                })
-            
-            # If no results found, generate placeholders
-            if not results:
-                return self.generate_placeholder_results(query, max_results)
-            
-            return results
-            
-        except Exception as e:
-            print(f"Fallback search failed: {e}")
-            return self.generate_placeholder_results(query, max_results)
-    
-    def clean_url(self, url: str) -> str:
-        """Clean DuckDuckGo redirect URLs"""
-        if 'uddg=' in url:
-            # Extract the actual URL from uddg parameter
-            match = re.search(r'uddg=([^&]+)', url)
-            if match:
-                return unquote(match.group(1))
-        return url
-    
-    def clean_text(self, text: str) -> str:
-        """Clean HTML entities and extra whitespace"""
-        # Remove HTML tags
-        text = re.sub(r'<[^>]+>', '', text)
-        # Decode HTML entities
-        text = text.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>')
-        text = text.replace('&quot;', '"').replace('&#x27;', "'")
-        # Clean whitespace
-        text = ' '.join(text.split())
-        return text.strip()
-    
-    def generate_placeholder_results(self, query: str, max_results: int) -> List[Dict[str, str]]:
-        """Generate placeholder results when search fails"""
-        topics = query.split()
-        results = []
-        
-        base_snippets = [
-            f"Comprehensive analysis of {query} including latest developments and key insights.",
-            f"Research findings on {query} from multiple authoritative sources and expert analysis.",
-            f"In-depth exploration of {query} covering fundamental concepts and recent advances.",
-            f"Expert perspective on {query} with detailed examination of current trends.",
-            f"Scientific review of {query} including methodology, findings, and implications."
-        ]
-        
-        for i in range(min(max_results, len(base_snippets))):
-            results.append({
-                'title': f"{query.title()} - Research Source {i+1}",
-                'url': f"https://example.com/research/{'-'.join(topics)}-{i+1}",
-                'snippet': base_snippets[i]
-            })
-        
-        return results
-    
-    def search(self, query: str, max_results: int = 10) -> List[Dict[str, str]]:
-        """Main search method with retry logic"""
-        if not query.strip():
-            return []
-        
-        # Try API search first
-        results = self.search_api(query, max_results)
-        
-        # Validate results
-        if len(results) < max_results // 2:
-            # If we got too few results, try fallback
-            fallback_results = self.fallback_search(query, max_results)
-            if len(fallback_results) > len(results):
-                results = fallback_results
-        
-        return results[:max_results]
-    
-    def batch_search(self, queries: List[str], max_results_per_query: int = 5) -> Dict[str, List[Dict[str, str]]]:
-        """Perform batch searches with rate limiting"""
-        results = {}
-        
-        for i, query in enumerate(queries):
-            if i > 0:
-                time.sleep(1)  # Rate limiting
-            
-            results[query] = self.search(query, max_results_per_query)
-        
-        return results

test_app.py

@@ -1,136 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for the Deep Research Agent
-"""
-
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-from search_engine import AdvancedDuckDuckGoSearcher
-import torch
-
-def test_search_engine():
-    """Test the search engine functionality"""
-    print("🔍 Testing search engine...")
-    
-    searcher = AdvancedDuckDuckGoSearcher()
-    
-    # Test basic search
-    test_query = "artificial intelligence 2024"
-    results = searcher.search(test_query, max_results=3)
-    
-    print(f"Query: {test_query}")
-    print(f"Results found: {len(results)}")
-    
-    for i, result in enumerate(results):
-        print(f"\n{i+1}. {result['title']}")
-        print(f"   URL: {result['url']}")
-        print(f"   Snippet: {result['snippet'][:100]}...")
-    
-    return len(results) > 0
-
-def test_model_loading():
-    """Test if model loading would work (without actually loading on CPU)"""
-    print("\n🤖 Testing model configuration...")
-    
-    try:
-        from transformers import AutoTokenizer
-        
-        model_name = "HuggingFaceTB/SmolLM3-3B"
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-        
-        print(f"✅ Tokenizer loaded successfully")
-        print(f"   Model: {model_name}")
-        print(f"   Vocab size: {tokenizer.vocab_size}")
-        print(f"   Pad token: {tokenizer.pad_token}")
-        
-        # Test tokenization
-        test_text = "This is a test for the research agent."
-        tokens = tokenizer.encode(test_text, return_tensors="pt")
-        print(f"   Test tokenization: {tokens.shape[1]} tokens")
-        
-        return True
-        
-    except Exception as e:
-        print(f"❌ Model loading test failed: {e}")
-        return False
-
-def test_gpu_availability():
-    """Test GPU availability for ZeroGPU"""
-    print("\n⚡ Testing GPU configuration...")
-    
-    if torch.cuda.is_available():
-        print(f"✅ CUDA available")
-        print(f"   Devices: {torch.cuda.device_count()}")
-        print(f"   Current device: {torch.cuda.current_device()}")
-        print(f"   Device name: {torch.cuda.get_device_name()}")
-    else:
-        print("ℹ️  CUDA not available (expected in local environment)")
-    
-    print(f"   PyTorch version: {torch.__version__}")
-    return True
-
-def test_gradio_imports():
-    """Test Gradio and related imports"""
-    print("\n🎨 Testing Gradio setup...")
-    
-    try:
-        import gradio as gr
-        print(f"✅ Gradio imported successfully (version: {gr.__version__})")
-        
-        import spaces
-        print("✅ Spaces module imported successfully")
-        
-        return True
-        
-    except Exception as e:
-        print(f"❌ Gradio test failed: {e}")
-        return False
-
-def main():
-    """Run all tests"""
-    print("🧪 Deep Research Agent - Test Suite")
-    print("=" * 50)
-    
-    tests = [
-        ("Search Engine", test_search_engine),
-        ("Model Configuration", test_model_loading),
-        ("GPU Setup", test_gpu_availability),
-        ("Gradio Imports", test_gradio_imports)
-    ]
-    
-    results = {}
-    
-    for test_name, test_func in tests:
-        try:
-            results[test_name] = test_func()
-        except Exception as e:
-            print(f"❌ {test_name} failed with exception: {e}")
-            results[test_name] = False
-    
-    print("\n" + "=" * 50)
-    print("📊 Test Results Summary:")
-    
-    passed = 0
-    for test_name, result in results.items():
-        status = "✅ PASS" if result else "❌ FAIL"
-        print(f"   {test_name}: {status}")
-        if result:
-            passed += 1
-    
-    print(f"\nOverall: {passed}/{len(tests)} tests passed")
-    
-    if passed == len(tests):
-        print("🎉 All tests passed! The app should work correctly.")
-    else:
-        print("⚠️  Some tests failed. Check the issues above.")
-    
-    return passed == len(tests)
-
-if __name__ == "__main__":
-    success = main()
-    sys.exit(0 if success else 1)