mlsanityData to 14 Oct 2026

XFlow: An Executable Protocol Programming System for Reliable Multi-Agent Workflows

arXiv 2606.14790 ↗First posted 11 Jun 2026cs.PL cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited. A central source of this limitation is the underspecified prompt--harness boundary. Current systems lack a principled way to decide which workflow commitments should remain in prompts and which should become harness structure. We present XFlow, an executable protocol programming system for reliable multi-agent workflows, and XPF (XFlow Protocol Format), its domain-specific protocol programming language. XFlow occupies a middle position between prompt-only orchestration and markup-like workflow descriptions. XPF remains readable as a literate protocol, but it is compiled and executed as a program. Its design keeps informal semantic work inside actors while moving selected commitments into harness structure that can be checked, preserved, and enforced. At runtime, XFlow stages uncertainty through lifecycle-governed symbols, which are typed state cells with validation and commit states. Actor outputs are mediated before they become shared state, instead of spreading through prompts, transcripts, or implicit memory. Our experiments cover Constrained Interaction, Long-Context Reasoning, and Agentic Software Engineering. They show that XFlow improves reliability by making constraints, evidence handling, and process requirements explicit and enforceable.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 38 references that are in the map. First posted on arXiv 11 Jun 2026.

Builds on

25 of 38 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026AgentSPEX: An Agent SPecification and EXecution Language14 Apr
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
  4. 2023ChatDev: Communicative Agents for Software Development16 Jul

Other references · 21

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  6. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  7. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  8. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  9. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  10. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  11. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  12. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviorscited by 598
9 more
  1. 2023Do Large Language Models Know What They Don't Know?cited by 243
  2. 2024Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedbackcited by 66
  3. 2024Reducing Tool Hallucination via Reliability Alignmentcited by 39
  4. 2025Alignment for Efficient Tool Calling of Large Language Modelscited by 17
  5. 2025Enhancing LLM Reliability via Explicit Knowledge Boundary Modelingcited by 13
  6. 2025Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaborationcited by 7
  7. 2026CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoningcited by 7
  8. 2025DiSRouter: Distributed Self-Routing for LLM Selectionscited by 5
  9. 2025Delusions of Large Language Modelscited by 4

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