From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution

• URL: http://arxiv.org/abs/2604.11378v1
• Date: Mon, 13 Apr 2026 12:16:45 GMT
• Title: From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution
• Abstract summary: The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window.<n>This perspective places Agent Loops and graph based execution engines on a single semantic continuum.<n>We propose SGH, which lifts control flow from implicit context into an explicit static DAG.
• Score: 1.8222732878503212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window. This paradigm has three structural weaknesses: implicit dependencies between steps, unbounded recovery loops, and mutable execution history that complicates debugging. We characterize the Agent Loop as a single ready unit scheduler: at any moment, at most one executable unit is active, and the choice of which unit to activate comes from opaque LLM inference rather than an inspectable policy. This perspective places Agent Loops and graph based execution engines on a single semantic continuum. We propose SGH, Structured Graph Harness, which lifts control flow from implicit context into an explicit static DAG. SGH makes three commitments: execution plans are immutable within a plan version, planning execution and recovery are separated into three layers, and recovery follows a strict escalation protocol. These choices trade some expressiveness for controllability, verifiability, and implementability. Our contributions are fourfold: a scheduler unified framework that applies classical scheduling theory to LLM agent execution and identifies challenges introduced by non deterministic LLM nodes; a trade off analysis of controllability, expressiveness, and implementability across 70 surveyed systems; a formal specification including a node state machine with termination and soundness guarantees; and an attributable experimental framework with a seven group design for future validation. This is a position paper and design proposal. We provide a theoretical framework, design analysis, and experimental protocol, not a production implementation or empirical results.

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)
• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery [5.106901034158075]
  This paper presents a hierarchical, skill-based architecture for agentic orchestration.<n> Capabilities are organized as a rooted tree where internal nodes make routing decisions and leaf nodes execute deterministic tasks.<n>By replacing global memory with localized stack frames, the architecture prevents outputs from one execution branch from leaking into another.
  arXiv  Detail & Related papers  (2026-07-13T06:15:51Z)
• LLM-as-Code: Agentic Programming for Agent Harness [20.350593038902904]
  We argue that token explosion, control-flow hallucination, and unreliable completion are not implementation bugs.<n>A better prompt or a stronger model cannot guarantee the reliability of the LLM agent.<n>We propose Agentic Programming, in which the program governs all control flow, and the LLM is itself part of it.
  arXiv  Detail & Related papers  (2026-06-14T15:47:27Z)
• PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback [0.0]
  PRIMA operates as coordinated multi-agent research systems over multi-hour runs.<n>Main contributions are three operational patterns for surviving failure modes.<n>A Graph Isomorphism case study grounds the architectural claims in a generated artifact.
  arXiv  Detail & Related papers  (2026-05-23T23:27:46Z)
• PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
  Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
  arXiv  Detail & Related papers  (2026-05-12T04:59:47Z)
• KAIJU: An Executive Kernel for Intent-Gated Execution of LLM Agents [5.931389072124715]
  Tool-calling autonomous agents based on large language models using ReAct exhibit three limitations.<n>Recent work moves towards separating planning from execution but in each case model remains coupled to the execution mechanics.<n>We introduce a system-level abstraction for LLM agents which decouples the execution agent from the reasoning layer.
  arXiv  Detail & Related papers  (2026-03-31T21:38:28Z)
• SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning [104.01865949020304]
  Agentic multimodal large language models (MLLMs) achieve remarkable reasoning capabilities through iterative visual tool invocation.<n>However, the cascaded perception, reasoning, and tool-calling loops introduce significant sequential overhead.<n>This overhead, termed agentic depth, incurs prohibitive latency and seriously limits system-level throughput.<n>We propose SpecEyes, an agentic-level speculative acceleration framework that breaks this sequential bottleneck.
  arXiv  Detail & Related papers  (2026-03-24T17:45:47Z)
• SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems [2.2566909388480743]
  Large Language Models (LLMs) have recently begun to be integrated into various cyber-physical systems.<n>LLMs produce "hallucinations" - outputs that are coherent yet factually incorrect or contextually unsuitable.<n>We propose SafePilot, a novel hierarchical neuro-symbolic framework that provides end-to-end assurance for CPS according to attribute-based and temporal specifications.
  arXiv  Detail & Related papers  (2026-03-23T03:31:51Z)
• RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis [78.32151470154422]
  We introduce RAVEL, an agentic framework that enables the testers to autonomously plan and execute typical synthesis operations.<n>We present C3EBench, a benchmark comprising 1,258 samples derived from professional human writings.<n>By augmenting RAVEL with SOTA LLMs as operators, we find that such agentic text synthesis is dominated by the LLM's reasoning capability.
  arXiv  Detail & Related papers  (2026-02-28T14:47:34Z)
• Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction [21.08753833036094]
  We present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering.<n>AEC decomposes ZSEE into specialized subtasks--retrieval, planning, coding, and verification--each handled by a dedicated LLM agent.<n> Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines.
  arXiv  Detail & Related papers  (2025-11-17T08:17:15Z)
• Who Sees What? Structured Thought-Action Sequences for Epistemic Reasoning in LLMs [1.090218572228214]
  This study investigates the potential of structured examples to improve the performance of LLM-based agents within a ReAct framework.<n>We propose a structured solution-processing pipeline that generates three categories of examples: optimal goal paths (G-type), informative node paths (E-type) and step-by-step optimal decision sequences contrasting alternative actions (L-type)<n>While L-type examples slightly reduce clarification requests and overall action steps, they do not yield consistent improvements.
  arXiv  Detail & Related papers  (2025-08-20T09:36:53Z)
• ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection [4.101501114944147]
  ReflecSched is a framework that empowers the LLM beyond a direct scheduler.<n>It distills simulations across multiple planning horizons into a concise, natural-language summary.<n>This summary is then integrated into the prompt of a final decision-making module, guiding it to produce non-myopic actions.
  arXiv  Detail & Related papers  (2025-08-03T11:26:35Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language [81.73820295186727]
  We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
  arXiv  Detail & Related papers  (2021-09-16T20:36:21Z)

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.