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The author proposes an AI agent architecture inspired by human brain processes, using belief updates and decision loops, and seeks feedback from the AI community on its practicality and improvements.
This paper evaluates LLMs' ability to recognize unspoken beliefs (implicatures) and their updates through implicature cancellation, introducing the expert-annotated ImplicatureX dataset. Results show LLMs lag behind humans, especially in natural scenarios.
BayesBench evaluates how closely large language models' belief updates match Bayesian reasoning in multi-turn evidence accumulation tasks, finding that while scaling improves latent inference, models struggle to use that understanding for downstream predictions.
A study of 25,000 AI scientist trials finds the agents ignore evidence 68% of the time and rarely revise hypotheses, showing popular scaffolding fixes don’t instill true scientific reasoning.