AI Coding Live News

Axiom released a Lean formalization of the entire Stacks Project on November 4 (official X post at 17:13 UTC, with over 2 million views). The roughly 7,500-page open-source algebraic geometry reference, started by Johan de Jong in 2005, becomes about 16 million lines of code, which Axiom calls the largest integrated, reusable formal math library ever built. Hierarchically coordinated AI agent swarms did the work and surfaced dozens of statements in the original that needed local fixes, with counterexamples. The library is open source and browsable side by side with the original text at stacks.axiommath.ai.

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YC Labs, a research project at Y Combinator, and multi-agent environment startup Olam Labs published "Agents in Games" on Oct 29. Fifty GPT-6.1 Sol agents told to "build a thriving civilization" in survival Minecraft spent three hours huddled in shelters rationing food, sending about 5,900 messages and inventing ration ledgers, permits and job titles. In nine Bed Wars games, 10 GPT-6.1 Sol agents beat 10 Claude Opus 5.5 agents 6–3; in seven 50-vs-50 Mindustry matches lasting about 9.5 hours, GPT won 6–1. The leader was almost always whichever agent was listed first in the directory, and stuck GPT agents fetched the game's public source code on their own. The Minecraft harness is open source. These are one lab's experimental observations, not a standardized benchmark.

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AI math company Axiom announced on Oct 27 that swarms of dozens of AI agents formalized the Classification of Finite Simple Groups (CFSG; the original proof runs about 15,000 pages across 500+ articles by about 100 mathematicians) in the Lean proof assistant in 24 days, producing about 43.1M lines of Lean and 2.05M declarations, with the repo public on GitHub. The work surfaced 34 noteworthy deviations from the published record, and AxiomProver produced a new alternative argument for the Unbalanced Group Theorem.

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Alibaba researchers introduce Cowork-Gym (arXiv:2610.17670), synthesizing 64,900 interactive human-agent coworking environments paired with a stateful user simulator that dynamically edits the shared workspace; post-training a 27B Qwen model yields #1 scores on PawBench and ClawEval, outperforming 100B+ parameter models. Concurrently, Harvard and MIT researchers open-source Market-Based Post-Training (MPT, arXiv:2610.22322), enabling decentralized agent swarms to jointly learn action policies and bidding-based self-orchestration without a central coordinator.

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As autonomous software engineering agents operate across multi-hour and multi-day horizons, context management becomes a decisive bottleneck. Prevailing paradigms treat context as an external append-only log, managed through rigid heuristic pruning outside the model's awareness. Researchers from the University of Washington, Meta FAIR, and Ai2 introduce Context Language Models (CLMs), where language models natively manage their own context by treating it as an editable file subject to unrestricted in-situ read/write operations. Zero-shot CLMs decisively outperform state-of-the-art context management: scoring 5% higher with 59% fewer FLOPs on 12-hour EdgeBench, and delivering a 65% improvement at matched compute on a 24-hour multi-repository agent-swarm task. Co-designed with SGLang, a novel Suffix Cache Reuse serving mechanism reduces inference compute by 35%, charting a scalable path for autonomous long-horizon multi-agent systems.

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