Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
📰 ArXiv cs.AI
Learn how to design provably convergent workflow learning systems under interface constraints for multi-agent LLM pipelines
Action Steps
- Formalize the workflow learning problem as an interface-constrained semi-Markov decision process (IC-SMDP)
- Define the local functions and private states for each specialized agent
- Design a decentralized learning algorithm that can handle the interface constraints
- Implement the algorithm using a suitable programming framework, such as Python or Julia
- Test and evaluate the convergence of the workflow learning system using simulation or real-world data
Who Needs to Know This
Research teams and engineers working on multi-agent systems and LLM pipelines will benefit from this knowledge to design more efficient and scalable workflows
Key Insight
💡 Interface constraints can be formalized as an IC-SMDP, enabling the design of provably convergent workflow learning systems
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💡 Design provably convergent workflow learning systems for multi-agent LLM pipelines under interface constraints! #LLM #MultiAgentSystems
Key Takeaways
Learn how to design provably convergent workflow learning systems under interface constraints for multi-agent LLM pipelines
Full Article
Title: Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
Abstract:
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. We formalize this regime as an interface-constrained semi-Markov decision process (IC-SMDP), w
Abstract:
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. We formalize this regime as an interface-constrained semi-Markov decision process (IC-SMDP), w
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