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A learnable moderator can transform multi-agent debates into more efficient reasoning processes, outperforming traditional methods by reducing redundancy and enhancing evidence aggregation.
A new framework categorizes molecular LLM agents into four levels of autonomy, revealing critical gaps and risks in their deployment for scientific discovery.
Hallucinations in chemical reasoning models coexist with correct answers, revealing a complex relationship that challenges our understanding of model reliability.
A single structural edit can drastically impair LLM performance in molecular tasks, highlighting the fragility of their generalization capabilities.
