Frontier labs

Rob Fergus

Head of Meta FAIR

Why we track: FAIR direction

Career moves

No published career moves.

Papers

Paper2026-10-20 · Zenodo (CERN European Organization for Nuclear Research)

Release of Structural Data Used in AI-Based Enzyme Design Paper

Supplementary Data 2. Designed and predicted structures of all designs

Demis Hassabis · Pushmeet Kohli · Rob Fergus +35 more

What: The research team has released all structural data files covered in the paper on AI-driven enzyme design for novel chemical reactions.
Key result: The compressed file contains structures generated by the AI model, redesigned structures, and additional predicted structural data.
Why it matters: The released data allows related researchers to verify the results of AI-based enzyme design and conduct further research.AI summary

AI for science

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Paper2026-10-06 · arXiv (Cornell University)

A new method for evaluating the self-improvement capabilities of AI agents through experience

Agent Plasticity: Measuring Self-Improvement Through Experience

Rob Fergus +10 more

What: Introduced the concept of agent plasticity and studied evaluation methods to measure the self-improvement efficiency of AI agents that evolve on their own through experience.
Key result: Despite similar learning opportunities, state-of-the-art models showed significant differences in their improvement trajectories, and the agent with the best final performance did not necessarily improve the most efficiently.
Why it matters: Demonstrates that when evaluating the potential of artificial intelligence, it is important to measure not only fixed-point capabilities but also how effectively they improve through experience.AI summary

Agents & coding

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Paper2026-10-04 · arXiv (Cornell University)

Proposal of a method where multiple AI agents stack work in a Git repository to solve problems

GitSwarm: Decentralized Compounding Inference

Rob Fergus +13 more

What: Proposed and evaluated GitSwarm, a 'compounding inference' system where multiple identical AI agents work independently in a shared Git repository, continuously saving, referencing, and combining intermediate results.
Key result: Reported solving all 30 math proof problems in a single run, outperforming the strongest baseline under a fixed budget (65.1 points) with an average of 79.4 points on the programming benchmark ProgramBench, and improving starting architectures through iterative experiments across three neural network structure tasks.
Why it matters: Demonstrates that AI can accumulate intermediate achievements rather than discarding results after each attempt, reusing them for subsequent tasks, thereby suggesting a direction for long-horizon problem solving and AI design for scientific research. However, since these are results at the abstract level, actual industrial impact is difficult to judge from this material alone.AI summary

Agents & coding AI for science

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Paper2026-09-29 · arXiv (Cornell University)

Proposed meta-reasoning technique for AI agents to determine 'how to proceed' before performing tasks

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning

Rob Fergus +11 more

What: Proposed an 'agentic meta-reasoning' approach that systematically treats execution control decisions—such as how much to reuse previous task results, whether to start fresh, and when to stop—for AI agents performing long-running tasks.
Key result: On the ProgramBench program reconstruction benchmark, the GPT-5.5-based version recorded 71.5%, which is higher than Codex's 58.0%, and the Opus 4.8-based version recorded 67.2%, higher than Claude Code's 65.5%.
Why it matters: According to the abstract, this suggests that as agents perform longer tasks, allocating computational resources to structured execution control may become more important. However, since the results in the abstract were obtained under specific benchmarks and experimental conditions, it is difficult to conclude that they directly translate to real-world product performance.AI summary

Inference efficiency Agents & coding

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This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.