Using LLMs to build AI scientists is all the rage now (e.g., Google’s AI co-scientist [1] and Sakana’s Fully Automated Scientist [2]), but how much do we understand about their core scientific abilities?.We know how LLMs can be vastly useful (solving complex math problems) yet

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📢 We're thrilled to announce the CMU AI for Science Workshop on Sept 12 at CUC-MPW! . Featuring an amazing lineup of speakers: .- Akari Asai (AI2/CMU) .- Gabe Gomes (CMU) .- Chenglei Si (Stanford).- Keyon Vafa (Harvard) . Join us on campus, submit your poster & register here:.

RT @JiahaoQiu99: 🚀 Just released: "A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence"!.We provide the first compre….

Check out this cool video (made by @theryanliu) for our #icml25 paper, "Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse"🤗.

In "Mind Your Step (by Step): Chain‑of‑Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse", we connect human "overthinking" insights to LLM reasoning, offering a new lens on when thinking‑out‑loud backfires. 📄 Read the full paper:

RT @gaurav_ghosal: 1/So much of privacy research is designing post-hoc methods to make models mem. free. It’s time we turn that around with….

🧐Check out our poster 11 am today @ West-320!.

RT @xiangyue96: People are racing to push math reasoning performance in #LLMs—but have we really asked why? The common assumption is that i….

RT @gneubig: What will software development look like in 2026?. With coding agents rapidly improving, dev roles may look quite different. M….

RT @ChengleiSi: Are AI scientists already better than human researchers?. We recruited 43 PhD students to spend 3 months executing research….

I'm thrilled to share that I've moved to Pittsburgh and joined NeuLab at CMU as a research intern this summer, advised by @gneubig! I'll also start my PhD @LTIatCMU this fall. Feel free to reach out if you're interested in chatting about multi-agent systems, LLMs for scientific.

RT @EchoShao8899: 🚨 70 million US workers are about to face their biggest workplace transmission due to AI agents. But nobody asks them wha….

RT @AnthropicAI: New on the Anthropic Engineering blog: how we built Claude’s research capabilities using multiple agents working in parall….

RT @yikewang_: LLMs are helpful for scientific research — but will they continuously be helpful?. Introducing 🔍ScienceMeter: current knowle….

RT @ChengleiSi: This year, there have been various pieces of evidence that AI agents are starting to be able to conduct scientific research….

RT @JiahaoQiu99: The GAIA game is over, and Alita is the final answer. Alita takes the top spot in GAIA, outperforming OpenAI Deep Researc….

[1] [2] [3] (10/10).

Check out more details from our paper: Work done with amazing collaborators Howard Chen (@__howardchen), Dilip Arumugam (@Dilip_Arumugam), and Tom Griffiths (@cocosci_lab)! 🎉. Special thanks Danqi Chen (@danqi_chen) and people from @PrincetonPLI.

In summary, we show that LLMs still struggle to fully utilize passive observations to form hypotheses and allowing active intervention mitigates some of the failure modes. By evaluating their ability to reverse-engineer black-box systems, we offer a principled and controlled.

So, what makes active intervention so effective? It allows the LLM to strategically design its queries to test and iteratively refine its hypotheses about the black-box system. (7/n)