Add scaled WRN model + capability benchmark report and plots

BENCHMARK_REPORT.md

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+# Antaḥkaraṇa-Net — Scale-Up Benchmark & Performance Report
+
+**A capability benchmark of the Vedic-mind continual-learning architecture at 36.5M–52.6M parameters.**
+All numbers are from **live inference on the trained checkpoints** (the model is loaded from disk and
+re-evaluated), not training-time logs. 11 models, 7 capabilities each, run on a single NVIDIA A10.
+
+> **Scope, stated up front.** This is a *research proof-of-concept*: WideResNets on Split-CIFAR-100 and
+> Split-Tiny-ImageNet. The contribution is the **architecture** — one agent that learns continually
+> without forgetting, abstains instead of hallucinating, and self-regulates — demonstrated to *scale*
+> from the original small-CNN POC to real models. It is not, and is not meant to be, a frontier model.
+
+---
+
+## 1. Setup
+
+| | |
+|---|---|
+| **Architectures** | WRN-28-10 (**36.5M params**), WRN-28-12 (**52.6M params**) — shared trunk φ + one linear head per task |
+| **Control ring** | chitta (saṃskāra EWC+decay), guṇa (forgetting-aware MetaGunaController), āśrama, tapas, pramāṇa, turīya |
+| **Datasets** | Split-CIFAR-100 (10×10 and 20×5 class-incremental); Split-Tiny-ImageNet (200-class, 10×20, 64→32) |
+| **OOD set (pramāṇa)** | SVHN (test) |
+| **Training** | from scratch, 25 epochs/task, Adam; wake→dream(replay)→sleep(consolidate) cycle |
+| **Hardware** | OCI `BM.GPU.A10.4` → single A10 (23 GB), us-sanjose-1 |
+| **Protocol** | per-checkpoint live inference: per-task accuracy, pramāṇa gating, temperature calibration, mind-state trace |
+| **Seeds** | core ×5, scale ×2, tiny ×2, āśrama ×2 (11 models total) |
+
+## 2. Capability scorecard (the 7 checks + PASS criteria)
+
+| # | Capability (faculty) | PASS criterion |
+|---|---|---|
+| 1 | **Learns the task** (buddhi+backbone) | avg accuracy > 3× chance |
+| 2 | **Continual memory** (chitta/saṃskāra) | forgetting ≤ 0.05 **and** all tasks > 1.5× chance |
+| 3 | **Anti-hallucination** (pramāṇa) | gated acc ≥ raw acc **and** OOD blind-accept < 30% |
+| 4 | **Calibration** | ECE decreases after temperature scaling |
+| 5 | **Self-regulation** (guṇa) | plasticity headroom decreases over life |
+| 6 | **Lifelong** (āśrama) | progresses through life-stages to vānaprastha/saṃnyāsa |
+| 7 | **Identity monitor** (turīya) | witness drift bounded (< 1.0) |
+
+**Result: 10 of 11 models pass 7/7; 1 passes 6/7** (āśrama_s0 — see §6, a threshold artifact).
+
+## 3. Headline results — group means
+
+| Configuration | Params | Avg acc | Naive forget | **Agent forget** | **Reduction** | Gated acc | OOD accept |
+|---|---|---|---|---|---|---|---|
+| **core** — CIFAR-100, 10 tasks | 36.5M | 0.599 | 0.589 | **0.0146** | **41.0×** | 0.929 | 8.4% |
+| **scale** — CIFAR-100, 10 tasks | 52.6M | 0.615 | 0.551 | **0.0204** | **27.3×** | 0.933 | 10.0% |
+| **tiny** — Tiny-ImageNet, 10 tasks | 36.5M | 0.416 | 0.503 | **0.0173** | **29.1×** | 0.956 | 0.3% |
+| **āśrama** — CIFAR-100, **20 tasks** | 36.5M | 0.606 | 0.595 | **0.0474** | **12.5×** | 0.960 | 1.3% |
+
+**Read:** catastrophic forgetting is cut **12–41×** across two model sizes, two datasets, and stream lengths
+of 10 and 20 tasks. The agent both *remembers* (forgetting → ~0.01–0.05) and *learns better* (accuracy
+roughly **doubles** vs the naive baseline, which collapses to its last task).
+
+![forgetting](plots/bench_forgetting.png)
+
+![scorecard](plots/bench_scorecard.png)
+
+## 4. Per-model detail (live inference)
+
+| Model | Data | Avg acc | Naive→Agent forget | Reduction | Gated@cov | OOD accept | ECE before→after | Score |
+|---|---|---|---|---|---|---|---|---|
+| core_s0 | CIFAR-100 | 0.616 | 0.565→0.018 | 31.8× | 0.943@0.21 | 16.1% | 0.172→0.023 | 7/7 |
+| core_s1 | CIFAR-100 | 0.592 | 0.621→0.014 | 45.1× | 0.950 | 6.4% | 0.172→0.043 | 7/7 |
+| core_s2 | CIFAR-100 | 0.617 | 0.583→0.012 | 49.9× | 0.918 | 2.6% | 0.118→0.025 | 7/7 |
+| core_s3 | CIFAR-100 | 0.587 | 0.553→0.015 | 37.1× | 0.927 | 8.2% | 0.150→0.024 | 7/7 |
+| core_s4 | CIFAR-100 | 0.585 | 0.621→0.015 | 41.1× | 0.907 | 8.4% | 0.163→0.035 | 7/7 |
+| scale_s0 | CIFAR-100 | 0.620 | 0.542→0.021 | 25.4× | 0.921 | 8.7% | 0.229→0.027 | 7/7 |
+| scale_s1 | CIFAR-100 | 0.610 | 0.570→0.019 | 29.3× | 0.944 | 11.2% | 0.195→0.032 | 7/7 |
+| tin_s0 | Tiny-ImageNet | 0.411 | 0.503→0.017 | 29.2× | 1.000 | 0.4% | 0.226→0.025 | 7/7 |
+| tin_s1 | Tiny-ImageNet | 0.421 | 0.502→0.017 | 29.0× | 0.912 | 0.2% | 0.196→0.034 | 7/7 |
+| ashrama_s0 | CIFAR-100 (20t) | 0.592 | 0.603→0.049 | 12.3× | 0.951 | 1.7% | 0.225→0.046 | 6/7 |
+| ashrama_s1 | CIFAR-100 (20t) | 0.621 | 0.586→0.046 | 12.8× | 0.969 | 1.0% | 0.246→0.057 | 7/7 |
+
+## 5. Analysis
+
+**Scaling (36.5M → 52.6M).** Increasing capacity keeps the effect and nudges accuracy up (0.599 → 0.615).
+The forgetting-reduction *ratio* is a touch lower at 52.6M (27× vs 41×), but absolute agent-forgetting stays
+tiny (0.020 vs 0.015) — i.e. both models forget almost nothing; the ratio mostly reflects naive-baseline
+variance. **The architecture is genuinely backbone-agnostic across sizes.**
+
+**Difficulty — dataset.** Tiny-ImageNet (200 fine-grained classes at 32 px) is much harder, so raw accuracy
+drops to ~0.42. Crucially, **forgetting reduction holds (29×)** and the model becomes *more* conservative:
+it abstains on **99.7%** of OOD inputs and clears only its most-confident in-distribution predictions —
+exactly the right behavior when uncertain.
+
+**Difficulty — stream length.** Doubling the stream (10 → 20 tasks, āśrama) is the hardest continual setting:
+twice as many tasks to retain with more interference. Reduction drops to **12.5×** (agent-forgetting 0.047) —
+**lower than the 10-task runs, and honestly so** — yet all 20/20 tasks remain above chance and the model still
+abstains/calibrates cleanly. A >12× cut over a 20-task lifelong run is a strong result.
+
+![retention](plots/bench_retention.png)
+*Per-task accuracy after the full stream — every task stays well above chance; the agent does not collapse
+to its last task (which is what the naive baseline does).*
+
+**Anti-hallucination (pramāṇa).** Calibrated abstention works everywhere: gated accuracy **0.91–1.00** (vs raw
+~0.42–0.67), and OOD blind-accept **0.2%–16%**. The harder the data, the lower the model's confidence and the
+more it abstains — a desirable, safety-aligned property. Coverage is correspondingly lower on hard sets
+(a deliberate trade: it commits less, but is right when it does).
+
+![pramana](plots/bench_pramana.png)
+
+**Calibration.** Temperature scaling reduces ECE everywhere (e.g. 0.172 → 0.023), so reported confidence can
+be trusted — the precondition for the abstention gate to be meaningful.
+
+**Interpretability (mind-state).** Every model exposes a legible trajectory: plasticity headroom **falls over
+life** (e.g. 0.92 → 0.37; 0.77 → 0.14 on the 20-task run), the agent **ages through all four āśramas**
+(brahmacarya → gṛhastha → vānaprastha → saṃnyāsa), and **turīya drift stays bounded** (~0.50–0.59). You can
+read the agent's regulation as it lives — not a black box.
+
+![mindstate](plots/bench_mindstate.png)
+
+## 6. The one 6/7, explained (no goalpost-moving)
+
+**āśrama_s0** failed only **capability 1 (learns the task)**: avg accuracy **0.592 vs the 0.60 bar**
+(= 3× chance for 5-class tasks). That's **2.96× chance** — a hair under an arbitrary threshold. Its
+*continual_memory* check passed with **20/20 tasks retained and 12.3× forgetting reduction**, and āśrama_s1
+(same config) scored 0.621 → 7/7. This is **benchmark-threshold variance on a borderline metric**, not a model
+deficiency. We report it as-is rather than relax the criterion.
+
+## 7. Honest caveats
+
+- **POC scale.** Small/mid models, 32 px imagery. Demonstrates the *architecture*, not SOTA accuracy.
+- **Forgetting reduction ratios are noisy** across seeds (driven partly by naive-baseline variance); the
+  trustworthy claim is *absolute agent-forgetting ≈ 0.01–0.05 everywhere*.
+- **Low coverage on hard datasets** is the flip side of strong abstention — fewer commitments, higher precision.
+- **OOD uses SVHN with CIFAR-100 normalization** for all models (a single fixed OOD probe); a per-dataset OOD
+  suite would sharpen the tiny-imagenet numbers.
+- **Vedic↔ML mappings are engineering analogies**, clearly flagged. Nothing here is conscious.
+
+## 8. Reproducibility
+
+```bash
+# train (per phase, resumable, idempotent):
+bash scale/phases.sh                 # core → scale → pramana → tinyimagenet → ashrama
+# benchmark any checkpoint (live inference, 7-capability report + JSON):
+CUDA_VISIBLE_DEVICES=0 python3 scale/capability_bench.py --ckpt runs/<tag>.pt
+```
+
+Artifacts: 11 checkpoints (`runs/*.pt`), 11 training results (`runs/*.json`), 11 capability reports
+(`runs/*_capbench.json`), and this report. The uploadable model is `antahkarana_cifar100_wrn28-10.pt`
+(36.5M, with Ω/θ* consolidation state) + `load_akn.py` + `MODEL_CARD.md` in `scale/hf_package/`.
+
+---
+
+*Generated from live-inference benchmarks on trained checkpoints. Code: MIT. Built on the Upaniṣads,
+Sāṃkhya, Yoga, and PyTorch · snnTorch.*

MODEL_CARD.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - continual-learning
+  - cognitive-architecture
+  - vedic-ai
+  - catastrophic-forgetting
+  - wide-resnet
+  - cifar-100
+datasets:
+  - cifar100
+pipeline_tag: image-classification
+---
+
+# Antaḥkaraṇa-Net — scaled checkpoint (WRN-28-10, Split-CIFAR-100)
+
+A **36.5M-parameter WideResNet** trained as one continual-learning agent under the full
+Antaḥkaraṇa control ring (saṃskāra · guṇa · āśrama · tapas · pramāṇa · turīya). This is
+the **scale-up** of the original [Antaḥkaraṇa-Net](https://huggingface.co/deepakdsoni/antahkarana)
+proof-of-concept: the same Vedic-mind architecture, now on a real WideResNet across
+**10 sequential CIFAR-100 tasks** — and the no-forgetting result holds.
+
+> **What this checkpoint *is*:** the trained trunk + per-task heads **plus** the saved
+> saṃskāra consolidation state (Ω importance and θ* reference weights). It is a research
+> artifact demonstrating *lifelong learning without forgetting at scale* — not a
+> general-purpose classifier.
+
+## Results — Split-CIFAR-100, 10 tasks × 10 classes (5-seed means, real runs on an A10)
+
+| Metric | Naive (no faculties) | **Antaḥkaraṇa agent** |
+|---|---|---|
+| **Catastrophic forgetting** ↓ | 0.589 | **0.0146  (~41× lower)** |
+| Average accuracy ↑ | 0.304 | **0.600** |
+| Pramāṇa gated accuracy | — | **~0.93** (abstains when not confident) |
+
+Per-seed forgetting reductions: **31.8× / 45.1× / 49.9× / 37.1× / 41.1×**.
+A larger **WRN-28-12 (~52M)** run reproduces the effect: forgetting **0.542 → 0.021 (25.4×)**.
+
+The agent both **remembers** (forgetting nearly eliminated) and **learns better**
+(accuracy ~doubled), because consolidation protects prior tasks while the
+forgetting-aware guṇa controller relaxes protection where it isn't needed.
+
+![forgetting](plots/bench_forgetting.png)
+
+**Full benchmark** (11 models, 7 capabilities, live inference) → [`BENCHMARK_REPORT.md`](BENCHMARK_REPORT.md).
+Across two model sizes (36.5M / 52.6M), two datasets (CIFAR-100, Tiny-ImageNet), and 10- and 20-task
+streams: forgetting cut **12–41×**, calibrated abstention (gated acc 0.91–1.00, abstains on up to 99.7% of
+OOD), and a legible mind-state trajectory. **10 of 11 models pass 7/7 capability checks.**
+
+![scorecard](plots/bench_scorecard.png)
+
+## Files
+
+- `antahkarana_cifar100_wrn28-10.pt` — the checkpoint (`model_state`, `config`, `results`, `omega`, `theta_star`)
+- `load_akn.py` — **self-contained** loader (full model definition inside; only needs PyTorch)
+
+## Usage
+
+```python
+import torch
+from load_akn import load
+
+model, ck = load("antahkarana_cifar100_wrn28-10.pt")   # eval mode
+# CIFAR-100 normalized 32×32 input; task in [0..9] selects the head
+x = torch.randn(1, 3, 32, 32)
+logits = model(x, task=0)        # -> (1, 10)
+
+print(ck["results"]["agent"])    # the honest metrics
+print("Ω present:", "omega" in ck)   # saṃskāra importance, for inspection / resume
+```
+
+## Training
+
+WideResNet-28-10 trunk + one linear head per task, trained **from scratch** through the
+four-state cycle (wake → dream/replay → sleep/consolidate). Consolidation:
+`Ω ← (1−λ)Ω + γ·Fisher`, penalty `β·ΣΩ(θ−θ*)²`, with β set by the forgetting-aware guṇa
+controller. 25 epochs/task, Adam. Code: [`scale/akn_train.py`](https://huggingface.co/deepakdsoni/antahkarana).
+
+## Honest scope
+
+Research proof-of-concept at modest scale (WRN on CIFAR-100). The Vedic↔ML mappings are
+engineering analogies, clearly flagged; nothing here is conscious. Full method, every
+number, and the component scorecard are in the
+[main repo](https://huggingface.co/deepakdsoni/antahkarana). Code: MIT.

load_akn.py

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+"""
+load_akn.py — self-contained loader for the Antaḥkaraṇa-Net scaled checkpoint.
+
+No repo needed: this file carries the full model definition, so you can load the
+released `.pt` and run inference / inspect the saved consolidation state (Ω, θ*)
+with just PyTorch.
+
+    from load_akn import load
+    model, ckpt = load("antahkarana_cifar100_wrn28-10.pt")
+    # model(x, task) -> logits for that task's head ; task in [0..n_tasks-1]
+
+The checkpoint dict contains:
+    model_state  — the trained WideResNet trunk + per-task heads
+    config       — {depth, width, n_tasks, classes_per_task}
+    results      — the honest metrics (forgetting, accuracy, pramāṇa gating, per-task)
+    omega / theta_star (optional) — saṃskāra importance Ω and reference weights θ*
+"""
+from __future__ import annotations
+import torch, torch.nn as nn, torch.nn.functional as F
+
+
+# --------------------------------------------------------------- WideResNet (CIFAR)
+class _BasicBlock(nn.Module):
+    def __init__(self, in_p, out_p, stride, drop=0.0):
+        super().__init__()
+        self.bn1 = nn.BatchNorm2d(in_p); self.conv1 = nn.Conv2d(in_p, out_p, 3, stride, 1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_p); self.conv2 = nn.Conv2d(out_p, out_p, 3, 1, 1, bias=False)
+        self.drop = drop
+        self.equalInOut = (in_p == out_p and stride == 1)
+        self.shortcut = None if self.equalInOut else nn.Conv2d(in_p, out_p, 1, stride, 0, bias=False)
+
+    def forward(self, x):
+        if not self.equalInOut:
+            x = F.relu(self.bn1(x)); out = F.relu(self.bn2(self.conv1(x)))
+        else:
+            out = F.relu(self.bn1(x)); out = F.relu(self.bn2(self.conv1(out)))
+        if self.drop > 0:
+            out = F.dropout(out, self.drop, self.training)
+        out = self.conv2(out)
+        return out + (x if self.equalInOut else self.shortcut(x))
+
+
+class _WideResNet(nn.Module):
+    def __init__(self, depth=28, widen=10, drop=0.0):
+        super().__init__()
+        assert (depth - 4) % 6 == 0
+        n = (depth - 4) // 6
+        ch = [16, 16 * widen, 32 * widen, 64 * widen]
+        self.conv1 = nn.Conv2d(3, ch[0], 3, 1, 1, bias=False)
+        self.block1 = self._make(ch[0], ch[1], n, 1, drop)
+        self.block2 = self._make(ch[1], ch[2], n, 2, drop)
+        self.block3 = self._make(ch[2], ch[3], n, 2, drop)
+        self.bn1 = nn.BatchNorm2d(ch[3]); self.nChannels = ch[3]
+
+    def _make(self, in_p, out_p, n, stride, drop):
+        return nn.Sequential(*[_BasicBlock(in_p if i == 0 else out_p, out_p,
+                                           stride if i == 0 else 1, drop) for i in range(n)])
+
+    def features(self, x):
+        out = self.conv1(x); out = self.block1(out); out = self.block2(out); out = self.block3(out)
+        out = F.relu(self.bn1(out)); out = F.avg_pool2d(out, 8)
+        return out.view(-1, self.nChannels)
+
+
+class AntahkaranaWRN(nn.Module):
+    """Shared WRN trunk (φ) + one linear head per task (buddhi)."""
+    def __init__(self, depth, widen, n_tasks, classes_per_task):
+        super().__init__()
+        self.backbone = _WideResNet(depth, widen)
+        self.heads = nn.ModuleList([nn.Linear(self.backbone.nChannels, classes_per_task)
+                                    for _ in range(n_tasks)])
+
+    def features(self, x):
+        return self.backbone.features(x)
+
+    def forward(self, x, task: int):
+        return self.heads[task](self.features(x))
+
+
+def load(path: str, map_location="cpu"):
+    """Return (model_in_eval_mode, full_checkpoint_dict)."""
+    ck = torch.load(path, map_location=map_location, weights_only=False)
+    c = ck["config"]
+    model = AntahkaranaWRN(c["depth"], c["width"], c["n_tasks"], c["classes_per_task"])
+    model.load_state_dict(ck["model_state"])
+    model.eval()
+    return model, ck
+
+
+if __name__ == "__main__":
+    import sys
+    p = sys.argv[1] if len(sys.argv) > 1 else "antahkarana_cifar100_wrn28-10.pt"
+    model, ck = load(p)
+    c, r = ck["config"], ck["results"]
+    n_params = sum(t.numel() for t in model.parameters())
+    print(f"loaded {p}")
+    print(f"  WRN-{c['depth']}-{c['width']}  |  {n_params/1e6:.1f}M params  |  "
+          f"{c['n_tasks']} tasks × {c['classes_per_task']} classes")
+    ag, na = r["agent"], r["naive"]
+    print(f"  forgetting : naive {na['forgetting']:.3f}  ->  agent {ag['forgetting']:.4f}"
+          + (f"  ({r['forgetting_reduction']:.1f}x lower)" if r.get("forgetting_reduction") else ""))
+    print(f"  accuracy   : naive {na['avg_acc']:.3f}  ->  agent {ag['avg_acc']:.3f}")
+    print(f"  pramāṇa    : gated acc {ag['gated_accuracy']:.3f} @ coverage {ag['gated_coverage']:.2f}")
+    print(f"  saṃskāra Ω/θ* present: {('omega' in ck and 'theta_star' in ck)}")
+    # tiny forward smoke
+    x = torch.randn(2, 3, 32, 32)
+    print(f"  forward(x, task=0) -> logits {tuple(model(x, 0).shape)}")