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AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators

NeurIPS 2026 · Multi-agent systems and games › LLM agents · approach: Robust and efficient agent execution · 0 citations (OpenAlex)

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Introduces AgentCollabBench, a 900-task diagnostic benchmark measuring four behavioral failure modes (instruction decay, tracer durability, consensus pollution, cross-task leakage) in multi-agent LLM systems across communication topologies, evaluating four LLMs.

Problem

Outcome-based evaluations of multi-agent LLM systems miss process-level failures such as dropped constraints, lost information across hops, false-belief spread, and cross-task leakage.

Approach

A controlled-injection benchmark with four deterministic/LLM-judged metrics (IDR, RTD, CPR, CLC) stratified across domains, topologies, and complexity tiers, plus a finding that converging-DAG nodes drop constraints from minority parent branches.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: AgentCollabBench

Benchmarks: AgentCollabBench

Metrics: Instruction Decay Rate Radioactive Tracer Durability Consensus Pollution Rate Cross-task Leakage Containment Cohen's kappa Spearman correlation Kruskal-Wallis eta-squared

Baselines: Chatbot Arena leaderboard ranks

Builds on: MultiAgentBench GEMMAS MAST

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