pith:CZGDPYCF
STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning
STAR models inter-agent routing as a Markovian transition policy conditioned on typed failure states to learn specific recovery transitions from unsuccessful traces.
arxiv:2605.10057 v3 · 2026-05-11 · cs.AI · cs.MA
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Claims
Results prove that retaining unsuccessful traces during training enlarges the support of the routing policy on error states, enabling recovery transitions that success-only training cannot represent. Across three spatiotemporal benchmarks and eight backbone LLMs, STAR improves over multiple baselines with the clearest gains on queries whose execution deviates from the nominal routing path.
The framework assumes that failure states can be accurately and consistently typed into distinct categories (malformed outputs, missing dependencies, tool-query mismatches) during execution, allowing the routing matrix to condition recovery transitions on these types rather than collapsing them into a generic signal, as described in the central routing mechanism.
STAR presents a failure-aware routing framework using a state-conditioned transition policy and an agent routing matrix combining expert routes with learned recoveries from execution traces to improve multi-agent spatiotemporal reasoning.
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| First computed | 2026-05-20T00:00:42.355644Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
164c37e0454e6abbbaff73556cb4ae4d3cf8b933aa253aa863d9532a9550d0dd
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Canonical record JSON
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