ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
Agentsnet: Coordination and collaborative reasoning in multi-agent llms
5 Pith papers cite this work. Polarity classification is still indexing.
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DESBench reveals structural trade-offs among centralized, hierarchical, heterarchical, and holonic coordination in dynamic industrial scheduling that outcome metrics alone miss.
AgentCollabBench shows that multi-agent reliability is limited by communication topology, with converging-DAG nodes causing synthesis bottlenecks that discard constraints and explain 7-40% of information loss variance.
SAVeR adds self-auditing of internal beliefs in LLM agents via persona-based candidates and constraint-guided repairs, improving faithfulness on six benchmarks without hurting task performance.
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
citing papers explorer
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Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
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When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling
DESBench reveals structural trade-offs among centralized, hierarchical, heterarchical, and holonic coordination in dynamic industrial scheduling that outcome metrics alone miss.
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AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators
AgentCollabBench shows that multi-agent reliability is limited by communication topology, with converging-DAG nodes causing synthesis bottlenecks that discard constraints and explain 7-40% of information loss variance.
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Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
SAVeR adds self-auditing of internal beliefs in LLM agents via persona-based candidates and constraint-guided repairs, improving faithfulness on six benchmarks without hurting task performance.
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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.