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
- Pigment Agents now reads and posts in Slack, inheriting user permissions — mattturck · 2026-11-12
- Map of the agent economy: 50+ startups across payments, spend control, identity, memory — venturetwins · 2026-11-12
- OpenCode launches Browse, an on-device browser agent with local logins — Saul_Loveman · 2026-11-12
- VentureTwins maps the B2A economy: dozens of agent infra and app companies in first series installment — venturetwins · 2026-11-12
- Google releases 76-page 'Agents Companion' whitepaper on agent engineering — mdancho84 · 2026-11-12
- Spinning up containers for 20-line agent tools is backwards: Safescript compiles provable, 50ms-deploy apps — uriwa · 2026-11-12