Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
The paper discusses a new approach to workflow learning in multi-agent systems where agents hand off control through a shared artifact. It introduces an asynchronous decentralized Q-learning algorithm called IC-$Q$, which operates under interface constraints. The authors provide a finite-sample bound for this algorithm, demonstrating its effectiveness through various experiments.
- ▪The study focuses on workflow learning in multi-agent systems with interface constraints.
- ▪IC-$Q$ is a decentralized Q-learning algorithm designed for agents that do not observe joint trajectories.
- ▪The authors establish a finite-sample bound for neural IC-$Q$ and validate it through experiments.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19140 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary source text | contentText |
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| Cluster | s0dwdz2XLC9M |
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| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19140 (cs) [Submitted on 18 May 2026] Title:Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints Authors:Jiayu Li, Enpei Zhang, Dawei Zhou, Elynn Chen, Yujun Yan View a PDF of the paper titled Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints, by Jiayu Li and 4 other authors View PDF HTML (experimental) Abstract:We study workflow learning in a setting where specialized agents hand off control through a shared artifact, each agent observes only a local function of that artifact and its own private state, and no centralized learner accesses joint trajectories -- the operating regime of multi-agent LLM pipelines that span organizational, vendor, or trust boundaries.
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