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Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work together on complex problems. However, self-improvement methods typically optimize one system at a time, and multi-agent frameworks often have every model work toward a...
CogGym is introduced, a scalable, unified framework grounded in cognitive science for systematically comparing model and human behavior on matched experimental trials, and intends for CogGym to provide a living evaluation framework that continually incorporates new cognitive science experiments to characterize where mo...
Autopoietic Game Theory is introduced, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving and can incorporate exogenous pressures.
It is found that current LLM-based agents reach agreement reliably, and negotiate efficient contracts when environmental uncertainty is low, but under high uncertainty, they often fail to negotiate satisfiable, efficient, or mutually beneficial contracts.
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