Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
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Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.
citing papers explorer
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When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
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Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.