
PSI says Anthropic's Fable 5.2 solved a quantum conjecture
Scott Aaronson says he does not fully follow the proof but accepts it after Lean autoformalization.
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Scott Aaronson says he does not fully follow the proof but accepts it after Lean autoformalization.
Contrast sets resolve 73% of large belief-shift rejections to a specific alternative and resolve 67% more questions under the same experiment budget.
It scores 54.0 overall versus 47.9 for AI Scientist-v2, and authors report chemistry yield gains in collaborations.
Across five proposers and 12 tasks, limited information raised originality and later selection discarded half the candidates.
Higher monofact rates in training data increase scientific discoveries in LLMs.

He says OpenAI holds more proofs than the hundreds already released and spent on the order of hundreds of millions of dollars.
Of 12 models, only GPT-6-Astra found a law, at a 16.7% discovery rate; the other 11 scored 0%.
On five frontier models, recovering the exact symbolic form stays the main bottleneck.
Among 12 models, only GPT-6-Astra discovered laws, at a reported rate of 16.7 percent.
Across 30 simulated science experiments, even the strongest of six frontier agents succeeds less than half the time.
Bandit and MCTS estimators are recast as recommenders; experiments cover synthetic trees, arcade games, and scientific tasks.
TTME updates external memory instead of weights and cuts FLOPs by 82.8–84.6% and runtime by 47.7–58.4%.
The LLM framework beats knowledge, source, and experience baselines on molecular, protein, gene, and property tasks.

The Reasoning Atlas is on GitHub and a companion paper is on bioRxiv for labs to follow up.
The report says the model delegates work to subagents, checks its own progress, and produces inspectable research artifacts.
It adds subagent delegation, progress checks with strategy revision, and exploratory research beyond predefined questions.
A router and rule-constrained retriever co-adapt with reinforcement and contrastive learning on STaRK-QA.
A robustness pipeline flags potentially consequential analytical choices in 9.5% of fully reproduced findings for expert review.
Automated scores rose from 4.86 to 5.39 after two self-improvement rounds, with larger gains in falsifiability and data resolvability.
Authors report end-to-end validation on a real quantum-dot synthesis run, from one workstation to multi-robot labs.
Automated scores rose from 4.86 to 5.39 after two self-improvement rounds across four life-science domains.
SciExam for ENSO tests whether AI agents can build competitive climate models of El Nino from observations alone.
Station agents rediscover 62.7% of criteria from three ICLR papers, far above prior agent systems.
EvoDuet co-evolves web queries and solutions, raising LLM discovery scores on 21 scientific tasks.
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