Geometry-aware FGW prior transfer plus PAC-Bayes residual adaptation reduces topology forgetting and raises average accuracy across continual multi-agent topology learning streams.
Adaptive graph pruning for multi-agent communication
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Conjunctive prompt attacks split adversarial elements across agents and routing paths in multi-agent LLM systems, evading isolated defenses and succeeding through topology-aware optimization.
FALAT improves failure attribution in LLM agent trajectories via dependency-guided search, achieving 46.0% step-level accuracy on algorithm-generated and 29.1% on hand-crafted trajectories in the Who&When benchmark.
PEAR is a permutation-equivariant adaptive routing protocol for multi-agent LLM debate that reconfigures sparse topologies each round to improve accuracy over fixed debate baselines.
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
citing papers explorer
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MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer
Geometry-aware FGW prior transfer plus PAC-Bayes residual adaptation reduces topology forgetting and raises average accuracy across continual multi-agent topology learning streams.
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Conjunctive Prompt Attacks in Multi-Agent LLM Systems
Conjunctive prompt attacks split adversarial elements across agents and routing paths in multi-agent LLM systems, evading isolated defenses and succeeding through topology-aware optimization.
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FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search
FALAT improves failure attribution in LLM agent trajectories via dependency-guided search, achieving 46.0% step-level accuracy on algorithm-generated and 29.1% on hand-crafted trajectories in the Who&When benchmark.
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PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate
PEAR is a permutation-equivariant adaptive routing protocol for multi-agent LLM debate that reconfigures sparse topologies each round to improve accuracy over fixed debate baselines.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.