Market-Bench is a new multi-agent benchmark showing that LLMs display large performance gaps in economic tasks, with only a few consistently growing capital while most break even despite similar ad quality.
Alympics: Language agents meet game theory
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A persona-driven multi-agent framework with a three-dimensional decision-theoretic evaluation shows that agent-persona alignment significantly impacts performance and coordination in O-RAN optimization challenges.
SOM uses a Structural Causal Model to create an explicit graph of opponent observation-to-action links, allowing LLMs to reason along those paths for more accurate and stable predictions in multi-agent settings.
The paper surveys LLM-based multi-agent systems, covering simulated domains, agent profiling and communication, mechanisms for capacity growth, and common benchmarks.
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Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition
Market-Bench is a new multi-agent benchmark showing that LLMs display large performance gaps in economic tasks, with only a few consistently growing capital while most break even despite similar ad quality.
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Decision-Theoretic Safety Assessment of Persona-Driven Multi-Agent Systems in O-RAN
A persona-driven multi-agent framework with a three-dimensional decision-theoretic evaluation shows that agent-persona alignment significantly impacts performance and coordination in O-RAN optimization challenges.
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SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model
SOM uses a Structural Causal Model to create an explicit graph of opponent observation-to-action links, allowing LLMs to reason along those paths for more accurate and stable predictions in multi-agent settings.
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Large Language Model based Multi-Agents: A Survey of Progress and Challenges
The paper surveys LLM-based multi-agent systems, covering simulated domains, agent profiling and communication, mechanisms for capacity growth, and common benchmarks.