Agentic AI news, research notes, and industry analysis.
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7 stories in this tag.
YC Labs ran 50 GPT-6.1 Sol agents in a Minecraft survival world for three hours. They invented a ration ledger, a pseudo-government with written rules, a "sole fertilizer operator" role, and a protocol for named chest withdrawals — and the emergent risk aversion and role fixation turned out to mirror what the same team sees in coding agents.
Read more →Sakana AI and UC Berkeley's MASS paper frames recursive self-improvement not as a single model thinking harder, but as a cycle of evolutionary workflow search, self-evaluation, and LoRA fine-tuning on the model's own best multi-agent traces — with the workflow text deliberately withheld during training.
Read more →Stephen Chung, Wenyu Du, and William Wesley's Station system — an open-world multi-agent environment where AI agents autonomously choose research directions without central coordination — produced novel results on five open mathematical problems, including new infinite families of finite-field Kakeya sets and improved kissing configurations in dimension 11.
Read more →A new blind benchmark called Reconstruction asks frontier models to infer a paper's central research idea from its anonymized bibliography alone — no seed paper, no contemporaneous literature. Solo frontier models score 3–15%. A multi-agent Swiss-tournament pipeline reaches 23–42%. The gap between what the bibliography implies and what models can recover from it turns out to be surprisingly wide.
Read more →Sakana AI launched Fugu today: a multi-agent orchestration system packaged as a single OpenAI-compatible API. The underlying claim — that learned coordination beats any individual frontier model on hard tasks — is backed by two ICLR 2026 papers and benchmark numbers that hold up. The detail worth noticing: Fable 5 and Mythos are absent from the agent pool because they're export-controlled. Swappable orchestration isn't just a feature; it's a hedge.
Read more →Argus (arXiv 2605.16217, May 15) splits research agents into a Searcher that gathers evidence ReAct-style and an RL-trained Navigator that maintains an evidence graph, identifies missing pieces, and dispatches parallel Searchers purposefully. With 64 parallel Searchers and a 35B-A3B MoE backbone, Argus reaches 86.2 on BrowseComp — highest reported for any agent system — while keeping Navigator context under 21.5K tokens. The separation of search from orchestration turns out to matter more than raw parallelism.
Read more →Two independent developments this week point at the same underlying problem: individual model alignment doesn't compose into system-level good behavior. Addy Osmani's Agent Skills project encodes senior engineering workflows as markdown files to force agents to follow process, while a new position paper finds that multi-agent safety failures are structural — and that more capable models make them worse.
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