Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs

• URL: http://arxiv.org/abs/2609.00621v1
• Date: Tue, 01 Sep 2026 03:04:08 GMT
• Title: Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs
• Abstract summary: prompt optimization can improve multi-agent systems, but the prompts being optimized often serve two entangled roles.<n>A prompt edit intended to improve content generation can inadvertently corrupt the protocol and cause the entire agent pipeline to fail.<n>We propose control-data flow separation, where execution-critical control is represented as typed program objects.
• Score: 12.541527720380897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt optimization can improve multi-agent LLM systems, but the prompts being optimized often serve two entangled roles: generating task-relevant content and specifying execution-critical protocols, such as message routing, output formatting, and termination signals, on which the underlying code relies. As a result, a prompt edit intended to improve content generation can inadvertently corrupt the protocol and cause the entire agent pipeline to fail. Our key observation is that these two roles have different representations: execution protocols are typically structured, while task-relevant content is usually expressed in unstructured language. Based on this, we propose control-data flow separation, where execution-critical control is represented as typed, validated program objects, while task-relevant language remains the optimizable data flow for agent communication. This design allows optimizers to improve multi-agent behavior without exposing the routing or formatting interface to prompt drift. Across synthetic reasoning, collaborative review generation, and insurance rating workflows, our framework empirically achieves 100% eventual protocol validity while consistently improving task performance.

Related papers

• Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
  We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
  arXiv  Detail & Related papers  (2026-09-04T23:32:11Z)
• FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production [55.71860113185132]
  We introduce FRAMEWORKERS, a task-centric and workspace-grounded multi-agent framework for open-ended video production.<n>A central Director formulates video creation as dynamic task management, continuously editing a Task Stack to determine which subtask to execute next and which sub-agent to invoke.<n>An Assistant serves as the execution layer, grounding each selected task in a shared Workspace, retrieving the required assets and context, invoking the assigned sub-agent, and persisting the resulting artifacts.<n>Experiments show that FRAMEWORKERS outperforms strong planners in routing accuracy, recovers reliably from runtime failures, generalizes to unseen sub
  arXiv  Detail & Related papers  (2026-08-30T14:28:50Z)
• The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows [72.9051182568928]
  We present ReASearch, a unified framework for reasoning-driven optimization.<n>With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML.<n>We observe that complex search behaviors emerge naturally from the agent's reasoning process.
  arXiv  Detail & Related papers  (2026-08-07T02:15:39Z)
• COVENANT: Natural-Language Workflow Compilation for Aligned Agent Execution [12.755574564434788]
  We propose COVENANT, a compiler-and-interpreter architecture for workflow-aligned agent execution.<n>Our key insight is to treat workflow instructions as source programs rather than prompts.<n>COVENANT converts the instructions into a workflow abstract syntax tree (WAST) and lowers it to a workflow control-flow graph (WCFG)
  arXiv  Detail & Related papers  (2026-07-28T07:59:50Z)
• Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs [52.26652574704317]
  Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes.<n>We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems.
  arXiv  Detail & Related papers  (2026-05-07T14:19:17Z)
• Agent Mentor: Framing Agent Knowledge through Semantic Trajectory Analysis [4.365760422569902]
  We introduce an analytics pipeline implemented as part of the Agent Mentor open-source library.<n>It monitors and incrementally adapts the system prompts defining another agent's behavior.<n>The pipeline improves performance by systematically injecting corrective instructions into the agent's knowledge.
  arXiv  Detail & Related papers  (2026-04-12T08:02:54Z)
• DLLM Agent: See Farther, Run Faster [94.74432470237817]
  Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties.<n>We study this in a controlled setting by instantiatingDLLM and AR backbones within the same agent workflow.<n>We find thatDLLM Agents are on average over 30% faster end to end than AR agents, with some cases exceeding 8x speedup.
  arXiv  Detail & Related papers  (2026-02-07T09:01:18Z)
• A Declarative Language for Building And Orchestrating LLM-Powered Agent Workflows [0.0]
  We present a declarative system that separates agent workflow specification from implementation.<n>Our results demonstrate 60% reduction in development time, and 3x improvement in deployment velocity compared to imperative implementations.<n>We show that complex involving product search, personalization, and cart management can be expressed in under 50 lines of DSL compared to 500+ lines of imperative code.
  arXiv  Detail & Related papers  (2025-12-22T05:03:37Z)
• SCOPE: Prompt Evolution for Enhancing Agent Effectiveness [53.75986399936395]
  Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts.<n>While agents have access to this context, their static prompts lack the mechanisms to manage it effectively.<n>We introduce textbfSCOPE (Self-evolving Context Optimization via Prompt Evolution)<n>We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles)
  arXiv  Detail & Related papers  (2025-12-17T12:25:05Z)
• FABRIC: Framework for Agent-Based Realistic Intelligence Creation [3.940391073007047]
  Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments.<n>We present a unified framework for synthesizing agentic data using only LLMs, without any human-in-the-loop supervision.
  arXiv  Detail & Related papers  (2025-10-20T18:20:22Z)
• Making Prompts First-Class Citizens for Adaptive LLM Pipelines [46.59482681672941]
  We describe our vision and an initial design for SPEAR, a language and runtime that fills the prompt management gap.<n> SPEAR defines a prompt algebra that governs how prompts are constructed and adapted within a pipeline.<n>By treating prompt logic as structured data, SPEAR enables optimizations such as operator fusion, prefix caching, and view reuse.
  arXiv  Detail & Related papers  (2025-08-07T03:49:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.