HQRE entropy regularization makes multi-agent LLM coordination well-posed, yielding unique equilibria, linear mirror convergence, bounded Bayesian regret, and DICE gains of 4.3–8.5 pp on reasoning/planning tasks.
arXiv preprint arXiv:2402.12091 , year=
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A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.
Reasoning-optimized LLMs achieve 88-89% accuracy on 16 feature model analysis operations applied to semi-formal textual blueprints, approaching solver-based FLAMA performance.
citing papers explorer
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DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
HQRE entropy regularization makes multi-agent LLM coordination well-posed, yielding unique equilibria, linear mirror convergence, bounded Bayesian regret, and DICE gains of 4.3–8.5 pp on reasoning/planning tasks.
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A Survey of Large Language Models for Perception and Measurement of Human Psychology
A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.
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Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis
Reasoning-optimized LLMs achieve 88-89% accuracy on 16 feature model analysis operations applied to semi-formal textual blueprints, approaching solver-based FLAMA performance.