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Shanghai Jiao Tong University
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LLMs that ace one-shot optimization problems often stumble when faced with the messy reality of iterative model building, revision, and explanation in industrial settings.
Stop wasting idle compute: ProAct agents anticipate user needs and proactively gather information, slashing task completion time and hallucinations.
Decentralized debate among LLM agents doesn't just select the best solution for optimization modeling; it structurally enables agents to refine flawed candidates and even recover correct formulations through interaction.
LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
