Must-read papers of the week ▪️ AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses ▪️ Persistent Recursive Worlds Enable Autonomous Software Evolution ▪️ AgentRewind: Recoverable Execution for Long-Horizon LLM Agents ▪️ Demystifying Agent Skills: Why They Work-Until They Don’t ▪️ Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL ▪️ Stealing Reasoning Traces from Proprietary LLM APIs ▪️ Agent Safety Should Be a Runtime Contract ▪️ WorldClaw: Agentic 3D Open-World Generation at Scale ▪️ Second Thought: Reasoning in Parallel as LLM Agents Act and Observe ▪️ ScienceFlow: A Long-Horizon Agent for ML Research, Scientific Discovery and Beyond ▪️ OmniScientist: An Omni-Modal Omni-Discipline AI Scientist Find the full list of the most interesting papers + links here:

🧩 What autonomous AI agents are missing isn't a smarter model — it's on-the-ground know-how. That's the premise of this paper. Title: Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills URL: ❓ What's missing from autonomous research agents? 💡 The usual two-layer view — model plus execution harness — leaves out the operational knowledge of picking the right method, using package APIs correctly, and avoiding implementation pitfalls. This paper treats that as an explicit third layer. ❓ How do you actually get that knowledge? 💡 It distills GitHub repos and papers through a four-stage pipeline — Scope, Ground, Construct, Verify — into verified "skills." From 1,000 repos and 153 papers, they built a library of 5,353 skills. ❓ How much difference do skills actually make? 💡 With the same GPT-5.5 backbone and same harness, just adding skills lifts MLE-bench from 31.11% to 72.89%, with similar gains across PaperBench, FrontierCS, and PassNet — hard tasks see over 4x improvement. ❓ Isn't this just throwing more compute at the problem? 💡 No — improvement barely correlates with token counts or tool calls, and it beats a Claude Opus 4.8 setup while using fewer tokens. The knowledge itself is doing the work. #AIAgents# #MachineLearning#

Transfer a big model's smarts to a smaller one with no retraining — right at inference time. A fresh take on capability transfer. Title: AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses URL: ❓ How is this different from distillation? 💡 Distillation updates the target's weights during training. Here the weights are never touched: a strong builder model constructs an inference-time scaffold (harness) that helps a weaker model execute. Capability transfers through the inference environment. ❓ What does the scaffold actually do? 💡 Mainly three things: ・Offload unstable reasoning into deterministic code ・Route to different strategies by question type ・Enforce strict formatting so answers parse reliably ❓ How well does it work? 💡 On four Theory-of-Mind benchmarks, GPT-5.4-mini nearly doubled from 0.49 to 0.91, with all 11 builder configs beating baseline. Weaker targets gain the most, while already-strong targets can even regress. ❓ What decides success? 💡 Not probing more validation data, but the builder's own reasoning quality. A strong builder acts as a "compiler of task competence," encoding structure into procedures in one pass. #AIAgents# #TestTimeScaling#

Introducing Hyra-1.0, the first version of Hunyuan Research Agent. 💡💡💡 Built to recursively improve solutions for performance-driven research and engineering tasks. Explore our demos in AI4AI, AI4Science, and AI4Fun:

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