GenCoord: Skill-Path Commitments under Private Information

2026-08-22

AI Summary

研究通过GenCoord方法解决多智能体在私有信息下协作的问题，利用Qwen3.5-0.8B模型生成可执行技能路径承诺，提高了任务成功率和效率。

Abstract

Suppose one embodied agent knows what must be built, while its teammate alone knows which transformation its workcell can perform. Neither local view determines who should act, what should be handed off, or how the joint task should continue. We introduce GenCoord, which turns the task consequence of such private facts into an executable skill-path commitment. A local Qwen3.5-0.8B model emits a multi-step SELF plan and peer REQ; bounded feedback conditions route revision when the deciding capability is peer-local. The resolved commitment is parsed, checked, canonically materialized, compiled to Mineflayer skills, and verified by handoff and terminal state. Counterfactual interventions that hold the world, call schedule, and executor unchanged make requester revision and receiver execution follow the injected task consequence in both directions. Across three independently trained seeds, correct capability feedback closes the paired local-information gap from 50% to 100%. Multi-step commitments improve held-out-template success by 6.9 points while reducing model decisions by 32%. At matched closed-loop quality on 128 held-out semantic clusters, Short DSL reduces peer traffic by 92.8% and median time-to-commitment by 68.2% relative to controlled free-form communication. These results identify executable task consequences as the coordination unit connecting distributed local reasoning to verified joint action.

Problem

• private information
• task consequence
• skill-path commitment
• local reasoning
• joint action

Innovation

• GenCoord
• Skill-Path Commitment
• Private Information
• Short DSL
• Multi-step Commitments