Align dataset card with GitHub README

Sync the dataset card with the latest GitHub README while preserving Hugging Face metadata, using Hub-safe absolute links, and clarifying the code/data setup flow.

README.md

@@ -17,7 +17,6 @@ size_categories:
 - n<1K
 ---
 
-
 <h1 align="center">WildClawBench</h1>
 
 <p align="center">
@@ -29,11 +28,12 @@ size_categories:
 
 [![Tasks](https://img.shields.io/badge/Tasks-60-blue)]()
 [![Harnesses](https://img.shields.io/badge/Harnesses-4-purple)]()
-[![Models](https://img.shields.io/badge/Models-19-green)]()
-<br>
+[![Models](https://img.shields.io/badge/Models-28-green)]()
 [![Leaderboard](https://img.shields.io/badge/🏆_Leaderboard-WildClawBench-8c2416)](https://internlm.github.io/WildClawBench/)
+<br>
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-5865F2?logo=github&logoColor=white)](https://github.com/internlm/WildClawBench)
 [![arXiv](https://img.shields.io/badge/arXiv-2605.10912-b31b1b.svg)](https://arxiv.org/abs/2605.10912)
+[![HF Daily Paper](https://img.shields.io/badge/🤗_Daily_Paper-Featured-ffcc00)](https://huggingface.co/papers/2605.10912)
 [![HuggingFace](https://img.shields.io/badge/🤗_HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/internlm/WildClawBench)
 [![PDF Report](https://img.shields.io/badge/📄_Paper-PDF-red)](https://github.com/internlm/WildClawBench/blob/main/WildClawBench_report.pdf)
 
@@ -48,7 +48,7 @@ size_categories:
 
 We drop agents into a live [OpenClaw](https://github.com/openclaw/openclaw) environment — the same open-source personal AI assistant that real users rely on daily — and throw **60 original tasks** at them: clipping goal highlights from a football match, negotiating meeting times over multi-round emails, hunting down contradictions in search results, writing inference scripts for undocumented codebases, catching privacy leaks before they happen. Useful things. Hard things.
 
-Hard enough that **the strongest frontier model we tested still tops out around 62% overall** (technical report Main results table), and most models land well below that. That makes scores mean something.
+In the **technical report snapshot**, the strongest frontier model topped out at **62.2% overall**; the latest audited OpenClaw runs have since raised the best score to **67.2%**. Most models still land well below that. That makes scores mean something.
 
 ### Why WildClawBench?
 
@@ -71,6 +71,8 @@ Most agent benchmarks test isolated capabilities — calling a function, parsing
 
 ## News
 
+- **2026-07** We expanded the OpenClaw leaderboard with evaluations of the latest frontier models, including **GPT-5.6 Sol, Claude Fable 5, Kimi K3 and etc**.
+- **2026-06** ByteDance Seed's **[Seed2.1 release](https://seed.bytedance.com/en/blog/seed2-1-officially-released-advancing-ai-productivity)** includes WildClawBench in its agent evaluations. Thanks for the recognition!
 - **2026-05** We released a new version with **four agent harnesses** — OpenClaw, Claude Code, Codex CLI, and Hermes Agent — so the same 60-task suite can be evaluated under multiple scaffolds.
 - **2026-05** We published a **[technical report PDF](https://github.com/internlm/WildClawBench/blob/main/WildClawBench_report.pdf)**.
 - **2026-05** Tencent’s **[Hunyuan3 Preview](https://hunyuan.tencent.com/research/hy3)** page reports WildClawBench evaluation scores. Thanks for the recognition!
@@ -88,30 +90,42 @@ Full interactive leaderboard at [internlm.github.io/WildClawBench](https://inter
 
 ### Model leaderboard (OpenClaw harness)
 
-> **Overall score** follows the weighted Multimodal / Pure-text breakdown in that table. **Total time** and **total cost** are the paper’s Overall per-task averages (minutes / USD) multiplied by **60** for the full 60-task suite. 
-> Gemini 3.1 Pro was evaluated in low-effort mode; scores may not reflect peak capability.
+> **Overall score** follows the weighted Multimodal / Pure-text breakdown in that table. **Total time** and **total cost** are the paper’s Overall per-task averages (minutes / USD) multiplied by **60** for the full 60-task suite.
+
 
 | Rank | Model | Org | Overall Score | Total Time | Total Cost |
 |:----:|-------|-----|:-------------:|:----------:|:----------:|
-| 🥇 | **Claude Opus 4.7** | Anthropic | **62.2%** | 328 min | $77.40 |
-| 🥈 | GPT-5.5 | OpenAI | 58.2% | 262 min | $37.80 |
-| 🥉 | Claude Opus 4.6 | Anthropic | 51.6% | 508 min | $81.00 |
-| 4 | GPT-5.4 | OpenAI | 50.3% | 350 min | $19.80 |
-| 5 | GLM 5.1 | Zhipu AI | 48.2% | 515 min | $34.80 |
-| 6 | DeepSeek V4 Pro | DeepSeek | 43.7% | 605 min | $12.00 |
-| 7 | MiMo V2.5 Pro | Xiaomi | 43.0% | 451 min | $12.60 |
-| 8 | GLM 5 | Zhipu AI | 42.6% | 373 min | $11.40 |
-| 9 | Gemini 3.1 Pro | Google DeepMind | 40.8% | 240 min | $18.00 |
-| 10 | MiMo V2 Pro | Xiaomi | 40.2% | 458 min | $26.40 |
-| 11 | Qwen3.5 397B | Alibaba Cloud | 34.5% | 459 min | $22.20 |
-| 12 | DeepSeek V3.2 | DeepSeek | 34.0% | 549 min | $11.40 |
-| 13 | GLM 5 Turbo | Zhipu AI | 33.9% | 499 min | $15.00 |
-| 14 | MiniMax M2.7 | MiniMax | 33.8% | 551 min | $7.20 |
-| 15 | Kimi K2.5 | Moonshot AI | 30.8% | 406 min | $6.60 |
-| 16 | MiMo V2 Flash | Xiaomi | 30.8% | 433 min | $10.20 |
-| 17 | MiniMax M2.5 | MiniMax | 27.1% | 542 min | $9.60 |
-| 18 | Step 3.5 Flash | StepFun | 26.7% | 430 min | $6.60 |
-| 19 | Grok 4.20 Beta | xAI | 19.3% | 94 min | $9.60 |
+| 🥇 | **GPT-5.6 Sol** | OpenAI | **67.2%** | 222 min | $56.78 |
+| 🥈 | Claude Opus 4.8 | Anthropic | 64.7% | 400 min | $95.95 |
+| 🥉 | Claude Opus 4.7 | Anthropic | 62.2% | 328 min | $77.40 |
+| 4 | Claude Fable 5 | Anthropic | 62.0% | 324 min | $87.71 |
+| 5 | GPT-5.5 | OpenAI | 58.2% | 262 min | $37.80 |
+| 6 | Grok 4.5 | xAI | 57.5% | 359 min | $28.43 |
+| 7 | Muse Spark 1.1 | Meta | 54.8% | 367 min | $23.59 |
+| 8 | Kimi K3 | Moonshot AI | 54.5% | 488 min | $40.08 |
+| 9 | GLM 5.2 | Zhipu AI | 54.2% | 442 min | $17.10 |
+| 10 | Claude Opus 4.6 | Anthropic | 51.6% | 508 min | $81.00 |
+| 11 | GPT-5.4 | OpenAI | 50.3% | 350 min | $19.80 |
+| 12 | Hy3 | Tencent | 49.7% | 338 min | $2.13 |
+| 13 | GLM 5.1 | Zhipu AI | 48.2% | 515 min | $34.80 |
+| 14 | Kimi K2.7 Code | Moonshot AI | 46.9% | 674 min | $72.31 |
+| 15 | DeepSeek V4 Pro | DeepSeek | 43.7% | 605 min | $12.00 |
+| 16 | MiMo V2.5 Pro | Xiaomi | 43.0% | 451 min | $12.60 |
+| 17 | GLM 5 | Zhipu AI | 42.6% | 373 min | $11.40 |
+| 18 | Gemini 3.1 Pro | Google DeepMind | 40.8% | 240 min | $18.00 |
+| 19 | MiMo V2 Pro | Xiaomi | 40.2% | 458 min | $26.40 |
+| 20 | Qwen3.5 397B | Alibaba Cloud | 34.5% | 459 min | $22.20 |
+| 21 | DeepSeek V3.2 | DeepSeek | 34.0% | 549 min | $11.40 |
+| 22 | GLM 5 Turbo | Zhipu AI | 33.9% | 499 min | $15.00 |
+| 23 | MiniMax M2.7 | MiniMax | 33.8% | 551 min | $7.20 |
+| 24 | Kimi K2.5 | Moonshot AI | 30.8% | 406 min | $6.60 |
+| 25 | MiMo V2 Flash | Xiaomi | 30.8% | 433 min | $10.20 |
+| 26 | MiniMax M2.5 | MiniMax | 27.1% | 542 min | $9.60 |
+| 27 | Step 3.5 Flash | StepFun | 26.7% | 430 min | $6.60 |
+| 28 | Grok 4.20 Beta | xAI | 19.3% | 94 min | $9.60 |
+
+> Claude Opus 4.8 cost uses the dynamic base-tier rates for this evaluation: $5/M input, $25/M output, $0.5/M cache read, and $6.25/M cache write.
+> Kimi K2.7 Code cost uses the published rates for this evaluation: $6.5/M input, $27/M output, and $1.3/M cached input.
 
 ### Harness comparison
 
@@ -145,6 +159,13 @@ To create new tasks, see the annotated template at [`tasks/task0_template.md`](h
 
 ## Quick Start
 
+> This Hugging Face repository hosts the benchmark's large Docker images and task data. The evaluation code is maintained in the [GitHub repository](https://github.com/internlm/WildClawBench). Clone the code repository first, then download the data below into its root directory.
+
+```bash
+git clone https://github.com/internlm/WildClawBench.git
+cd WildClawBench
+```
+
 ### Install Docker
 
 <details>
@@ -414,6 +435,8 @@ For independent verification and side-by-side comparison, we have provided the c
 - MiniMax M2.7 Details: [MiniMax M2.7](https://drive.google.com/file/d/15K65XZxkUqKWj3rp-d-gZN0DEL1iu2Kf/view?usp=drive_link)
 - Claude Opus 4.6 Details: [Claude 4.6 Opus](https://drive.google.com/file/d/1qCPxy0-Z-LveiVAmPTVlrh3x2fe9qlU6/view?usp=drive_link)
 
+More models's (fable5, glm5.2, gpt5.6, grok4.5, hy3, kimi_k3, muse_spark, kimi-k2.7, interns2-preview-397b, claude-opus4.8) details in [internlm/WildClawBench-Trajectories](https://huggingface.co/datasets/internlm/WildClawBench-Trajectories)
+
 ## Personal OpenClaw Evaluation
 
 "Raising lobsters" has become a phenomenon — users gradually teach their OpenClaw agents new skills, customize personalities, and build up long-term memory through daily interaction. A natural question follows: **whose lobster is better?** Beyond bragging rights, there is real value in understanding which skill combinations, persona designs, and memory strategies actually improve agent performance on a given model. That's why we created the **Personal OpenClaw Leaderboard**. Submit your lobster's results and see how it stacks up!