CMU × Meta's HANDRAISER Cuts Multi-Agent Communication Cost 32.2% by Learning to Interrupt
lileics · x · 2026-09-23
A CMU × Meta FAIR paper accepted at CoLM 2026 flips the script on multi-agent communication efficiency: instead of compressing speaker messages, let listeners interrupt. Naive interruption makes LLMs overconfident and cut in too early, so HANDRAISER learns to predict the right moment based on estimated future reward and communication cost. On 2-agent pictionary, 3-agent scheduling, and 3-agent debate tasks, it cuts communication cost 32.2% on average (24.3–48.9% with Llama-3.1-8B listeners) with equal or better task performance, generalizing to unseen GPT-4o speakers without fine-tuning.
More from coding & agent
- TesterArmy raises $1.2M pre-seed to build AI agents that test coding-agent-built apps — fernandorojo · 2026-09-23
- Two personal AIs tried to schedule coffee: agent interop needs a protocol — signulll · 2026-09-23
- Devin's iOS beta "Cog" hits TestFlight: code from your phone — msg · 2026-09-23
- Dev credits Alchemy for letting agents debug real deployments, landing major perf wins — samgoodwin89 · 2026-09-23
- Hooking Codex up to iMessage lets you interrogate months of chat history — gregmushen · 2026-09-23
- Sentry founder David Cramer finds Claude Code's new sidebar confusing vs Codex — zeeg · 2026-09-23