Multi-agent LLM simulations with trait-conditioned agents and a reinforcement-learning orchestrator show heterogeneous teams and dynamic trait selection outperform static configurations in simulated legal argumentation.
2009.Argumentation in Artificial Intelligence
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.MA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Structured reasoning artifacts enable coordination in LLM multi-agent systems by preventing approval and welfare collapse under asymmetric information while keeping bad-approval rates low across audit regimes.
citing papers explorer
-
Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation
Multi-agent LLM simulations with trait-conditioned agents and a reinforcement-learning orchestrator show heterogeneous teams and dynamic trait selection outperform static configurations in simulated legal argumentation.
-
Toward Explanatory Equilibrium: Verifiable Reasoning as a Coordination Mechanism under Asymmetric Information
Structured reasoning artifacts enable coordination in LLM multi-agent systems by preventing approval and welfare collapse under asymmetric information while keeping bad-approval rates low across audit regimes.