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Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints

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arXiv:2605. 19140v1 Announce Type: new 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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Authors: Jiayu Li, Enpei Zhang, Dawei Zhou, Elynn Chen, Yujun Yan
Released: 2026-05-21 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: under, source, epochs, line, control, without, trained. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.19140

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