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Learning Self-Orchestration in Decentralized Agent Swarms

Paper recorded by Signals 4 on 2026-10-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.AI · 人工智能 · first seen 2026-10-16

Abstract

The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestra

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#296 most recent of 960 cs.AI papers we have recorded · ↑ newer: Twice Upon a Time: Complementary Representations for Time-Series Reaso · ↓ older: AVC-Reward: Event-Centric Audio-Video Consistency Evaluation
Cite this page: Learning Self-Orchestration in Decentralized Agent Swarms: the #296 most recent of 960 cs.AI papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-self-orchestration-in-decentralized-agent-swarms.html
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