pith:DS6RY4OG
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
Graph transformer convolutions on coordination graphs integrate receiver-sensitive teammate signals to overcome partial observation bottlenecks in cooperative multi-agent reinforcement learning.
arxiv:2605.08391 v2 · 2026-05-08 · cs.LG
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Claims
SACHI consistently matches or outperforms the best baseline on every task, and rigorous aggregate statistical analyses, including normalized metrics with bootstrap confidence intervals, Friedman ranking, and performance profiling, confirm that this advantage is statistically significant, robust across environments, and not attributable to increased model capacity.
That a coordination graph can be defined such that graph transformer convolutions reliably extract and deliver the precise receiver-sensitive, content-dependent signals needed to overcome the partial-observation information bottleneck without introducing new scalability or training instabilities.
SACHI uses graph transformer convolutions on inter-agent coordination graphs to enrich partial-observation agents with content-dependent teammate information, yielding statistically significant gains over baselines in five cooperative tasks.
Receipt and verification
| First computed | 2026-05-20T01:05:16.004300Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
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Canonical record JSON
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