🧩 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#

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:

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:

AI is transforming productivity—but productivity is only part of the equation. Speaking at AI4 2026, @drfeifei highlighted a critical distinction: while AI tools can make people and businesses more productive, those gains do not automatically translate into shared prosperity. As AI continues to reshape jobs—creating new opportunities while changing or disrupting others—she argued that the conversation needs to go beyond how much more we can produce. It also needs to consider how the economic benefits of AI can be shared more broadly. Productivity ≠ Shared Prosperity. #ChinaAMC# #CNQQ# #AI4# #AI# #ArtificialIntelligence# #FeiFeiLi# #FutureOfAI# #FutureOfWork# #AIProductivity# Risk Disclaimer: Investment involves risk, including possible loss of principal. Any forecasts, projections, or opinions contained herein are for reference only and are not guaranteed to occur. The information in this material reflects prevailing market conditions and our judgment as of the release date, which are subject to change without further notice.

AI is powerful—but access to AI alone won't eliminate the skills gap. At AI4 2026, @AndrewYNg Ng argued that even as AI capabilities advance, there will continue to be meaningful differences between people who know how to use these tools effectively and those who don't. Rather than framing AI primarily as a threat, he offered a more optimistic message—especially for young people: learn the tools, build the skills, and use AI to expand what you can accomplish. The opportunity isn't simply AI replacing human capability. It's what people can achieve when they learn to work with AI. AI Won't Eliminate the Skills Gap. #ChinaAMC# #CNQQ# #AI4# #AI# #ArtificialIntelligence# #AndrewNg# #AISkills# #FutureOfWork# #FutureOfAI# Risk Disclaimer: Investment involves risk, including possible loss of principal. Any forecasts, projections, or opinions contained herein are for reference only and are not guaranteed to occur. The information in this material reflects prevailing market conditions and our judgment as of the release date, which are subject to change without further notice.

“Bring science, not science fiction, back to the AI debate.” At AI4 2026, @drfeifei pushed back against increasingly extreme narratives around artificial intelligence. As AI evolves rapidly, she argued that fear-mongering and unscientific rhetoric can make it harder for policymakers and the public to understand what the technology actually means. Instead, the AI conversation should be grounded in science, education, and healthy communication. For students, teachers, nurses, parents, and workers thinking about how AI could affect their jobs and their children's futures, understanding the technology matters. Her message is simple: AI is a rapidly evolving tool—and society needs a more rational conversation about how to use it. #ChinaAMC# #CNQQ# #AI4# #AI# #ArtificialIntelligence# #FeiFeiLi# #FutureOfAI# #AIInnovation# #AITools# Risk Disclaimer: Investment involves risk, including possible loss of principal. Any forecasts, projections, or opinions contained herein are for reference only and are not guaranteed to occur. The information in this material reflects prevailing market conditions and our judgment as of the release date, which are subject to change without further notice.

Day 1 at @Ai4Conferences showed that bigger models are only part of the AI story. From chips and compute to enterprise agents, governance, security and ROI, we heard from @googlecloud, @dataiku and @okta on what it takes to move AI into the real world froom @teslaownersSV #ChinaAMC# #CNQQ# #AI4# #AI# #ArtificialIntelligence# #AgenticAI# #EnterpriseAI# #FutureOfAI# Risk Disclaimer: Investment involves risk, including possible loss of principal. Any forecasts, projections, or opinions contained herein are for reference only and are not guaranteed to occur. The information in this material reflects prevailing market conditions and our judgment as of the release date, which are subject to change without further notice.