CoMemBench：跨多智能体工作流拓扑的协作记忆边界基准测试
CoMemBench: Benchmarking Collaborative Memory Boundaries across Multi-Agent Workflow Topologies
浏览论文内容
中文总结 AI 辅助
CoMemBench提出首个基于执行的多智能体工作流拓扑协作记忆边界基准，通过800个复合工作流衡量共享与隔离权衡，揭示拓扑对系统性能的影响。
中文摘要 AI 辅助
多智能体工作流需要在智能体之间共享任务相关信息，而无关、过时、未经验证或不兼容的信息则必须保持隔离。我们将这种任务条件下的信息范围称为协作记忆边界。工作流拓扑决定了哪些中间产物适用于哪些下游工作者，以及它们何时失效，从而为共享和隔离提供了结构性压力维度。现有的记忆基准主要评估保留和检索能力，而多智能体基准则强调协调和端到端完成，导致拓扑条件下的记忆边界在很大程度上未被测量。我们引入了CoMemBench，一个基于执行的多智能体工作流拓扑协作记忆共享与隔离基准。它从源依赖图中构建了跨四个领域的800个复合工作流，包含节点级规范、可验证的产物交接、原生评估器以及匹配的隔离挑战。CoMemBench衡量工作流完成度、已验证节点进度、所需交接可靠性、隔离鲁棒性和令牌成本。实验揭示了共享与隔离之间的权衡：更广泛的上下文提高了信息可用性，但可能削弱隔离，而系统排名在拓扑和产物违规之间发生变化。
英文摘要
Multi-agent workflows require task-relevant information to be shared across agents, while irrelevant, stale, unverified, or incompatible information must remain isolated. We call this task-conditioned scope of information a collaborative memory boundary. Workflow topology determines which intermediate artifacts are applicable to which downstream workers and when they cease to be valid, thereby providing a structural stress dimension for sharing and isolation. Existing memory benchmarks primarily evaluate retention and retrieval, whereas multi-agent benchmarks emphasize coordination and end-to-end completion, leaving topology-conditioned memory boundaries largely unmeasured. We introduce CoMemBench, an execution-grounded benchmark for collaborative memory sharing and isolation across multi-agent workflow topologies. It constructs 800 composite workflows across four domains from source-grounded dependency graphs, with node-local specifications, verifiable artifact handoffs, native evaluators, and matched isolation challenges. CoMemBench measures workflow completion, verified node progress, required-handoff reliability, isolation robustness, and token cost. Experiments reveal a sharing-isolation trade-off: broader context improves information availability but can weaken isolation, while system rankings shift across topologies and artifact violations.
