2026-10-11 17:11 UTC

Follow-up evaluations on the new open-ended multi-agent benchmark will confirm whether communication and coordination, rather than individual task competence, cause the reported roughly 6% average return.

state: expiredheat: lowuncertainty: highcontradictsscott: mediummulti-agent-coordination agent-benchmarks long-horizon-agents communicationGoogle DeepMind

What is this?

Researchers introduced a long-horizon, open-ended environment that benchmarks language-model agents coordinating to explore, communicate, trade resources, craft tools, build structures, and fight mobs. Across 13 modern LLMs, agents reportedly averaged about 6% normalized return; ablations attribute the largest contribution to communication, while memory and reasoning help sustain multi-step plans. The supplied snippets associate the work with Edinburgh and Cambridge research pages but do not establish Google DeepMind as the organization behind it, and they do not describe separate follow-up evaluations beyond the paper’s own ablations.

Why it matters to Scott

The reported ablation provisionally challenges Scott’s load-bearing claim that durable external state, rather than better inter-agent coordination, is what makes multi-agent loops reliable. If independent follow-ups confirm communication as the dominant factor, it could change how he evaluates long-horizon architectures such as OpenClaw; however, the supplied evidence contains only the original paper’s ablations, not those follow-up results.
ip:concept.durability-beats-coordinationip:framework.long-running-agentsdev:project.openclaw
queries asked of Scott's wikis
  • multi-agent coordination versus individual agent competence
  • agent communication protocols and shared state
  • long-horizon agent memory and multi-step plans
  • benchmarks for agent teams and coordination failure
  • multi-agent exploration and task decomposition
  • production criteria for multi-agent versus single-agent systems

Measured heat

no measured readings yet — the hourly heat pass fills this in

How the heat travelled

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Evidence (7) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟠 reddit ⭐New LLM Coordination Benchmark - Benchmarking Open-Ended Multi-Agent Coordination in Language Agents [R]
MachineLearning
ktessera106
🟧 hnMulti-Agent LLMs Fail to Explore Each OtherAnon8421
🟧 hnShow HN: One person runs 200 AI agents in our agent-only MMOstatico10
🟧 hnFor coding agents, real-time collaboration beats the "wisdom of the crowd"ykev20
🟧 hnShow HN: AgentCouch – let your agents chat with other agentsspstoyanov73
🟧 hnCan AI agents conduct open-ended AI research?randomwalker20
🟧 hnCan AI agents conduct open-ended AI research?galsapir30

Interpretation history

Decision trace