EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
Equilibrium points in n-person games.Proceedings of the national academy of sciences, 36(1):48–49
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Context-ordinal Nash equilibria are defined via social choice aggregation of ordinal preferences, shown to exist under mild conditions, with regularization, approximation, regret notions, complexity results, and learning rules developed.
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.
citing papers explorer
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EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
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Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games
Context-ordinal Nash equilibria are defined via social choice aggregation of ordinal preferences, shown to exist under mild conditions, with regularization, approximation, regret notions, complexity results, and learning rules developed.
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Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.