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New system uses LLM agents to refine cognitive models from behavior

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel system that combines human expertise with large language model (LLM) agents to discover cognitive algorithms from behavioral data. This hybrid approach treats the discovery process as a program refinement problem, where human-crafted cognitive models, expressed as probabilistic programs, are iteratively improved by LLM agents. The system identifies discrepancies between models and observed behavior, proposes code modifications within specified constraints, and verifies structural integrity, ultimately enhancing model fit and capturing behavioral variability. AI

IMPACT This approach could enable more sophisticated and interpretable cognitive modeling, bridging the gap between traditional methods and scalable LLM-based generation.

RANK_REASON The item describes a new research paper published on arXiv detailing a novel methodology for discovering cognitive algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New system uses LLM agents to refine cognitive models from behavior

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The item describes a new research paper published on arXiv detailing a novel methodology for discovering cognitive algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Huiwen Alex Yang, Mark K. Ho, Bill D. Thompson ·

    Hypothesis-guided discovery of cognitive algorithms via program refinement

    arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Traditional approaches to cognitive modeling are interpretable and benefit from hum…

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