Towards Structured, State-Aware, and Execution-Grounded Reasoning for Software Engineering Agents
- URL: http://arxiv.org/abs/2602.04640v1
- Date: Wed, 04 Feb 2026 15:07:53 GMT
- Title: Towards Structured, State-Aware, and Execution-Grounded Reasoning for Software Engineering Agents
- Abstract summary: We argue that we need to move beyond reactive behavior toward a structured, state-aware, and execution-grounded reasoning.<n>We outline how explicit structure, persistent and evolving state, and the integration of execution-grounded feedback can help SE agents perform more coherent and reliable reasoning.
- Score: 20.481392033796265
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software Engineering (SE) agents have shown promising abilities in supporting various SE tasks. Current SE agents remain fundamentally reactive, making decisions mainly based on conversation history and the most recent response. However, this reactive design provides no explicit structure or persistent state within the agent's memory, making long-horizon reasoning challenging. As a result, SE agents struggle to maintain a coherent understanding across reasoning steps, adapt their hypotheses as new evidence emerges, or incorporate execution feedback into the mental reasoning model of the system state. In this position paper, we argue that, to further advance SE agents, we need to move beyond reactive behavior toward a structured, state-aware, and execution-grounded reasoning. We outline how explicit structure, persistent and evolving state, and the integration of execution-grounded feedback can help SE agents perform more coherent and reliable reasoning in long-horizon tasks. We also provide an initial roadmap for developing next-generation SE agents that can more effectively perform real-world tasks.
Related papers
- Explaining AI Agents Through Execution Traces [1.8862116040247214]
We present a post-hoc XAI framework that transforms a lengthy agent's execution trace into a structured report and a faithful natural-language explanation.<n>Because it relies solely on execution traces, the framework applies across different agent architectures, environments, and tasks.
arXiv Detail & Related papers (2026-09-05T12:45:56Z) - Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution [50.50276752935017]
AgentRevive is a Markov state-aware framework for resilient multi-agent evolution.<n>Our approach dynamically manages agent collaboration through soft state transitions.<n>It significantly reduces token consumption through state-aware agent scheduling.
arXiv Detail & Related papers (2026-05-17T09:38:18Z) - The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
arXiv Detail & Related papers (2026-01-21T15:22:21Z) - The Path Ahead for Agentic AI: Challenges and Opportunities [4.52683540940001]
This chapter examines the emergence of agentic AI systems that operate autonomously in complex environments.<n>We trace the architectural progression from statistical models to transformer-based systems, identifying capabilities that enable agentic behavior.<n>Unlike existing surveys, we focus on the architectural transition from language understanding to autonomous action, emphasizing the technical gaps that must be resolved before deployment.
arXiv Detail & Related papers (2026-01-06T06:31:42Z) - Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning [4.226647687395254]
This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic based on multi-model consensus and reasoning-layer governance.<n>In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context.<n>A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions.
arXiv Detail & Related papers (2025-12-25T14:49:25Z) - Real-Time Reasoning Agents in Evolving Environments [52.21796134114843]
We introduce real-time reasoning as a new problem formulation for agents in evolving environments.<n>Our work establishes real-time reasoning as a critical testbed for developing practical agents.
arXiv Detail & Related papers (2025-11-07T00:51:02Z) - ProSEA: Problem Solving via Exploration Agents [1.1267979201719025]
We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through exploration and plan evolution.<n>ProSEA features a hierarchical architecture in which a Manager Agent orchestrates domain-specialized Expert Agents, decomposes tasks, and adaptively replans based on structured feedback from failed attempts.<n> Experiments on the challenging FinanceBench benchmark demonstrate that ProSEA, even without human feedback, outperforms state-of-the-art baselines and achieves robust performance across reasoning-heavy tasks.
arXiv Detail & Related papers (2025-10-08T18:23:33Z) - Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm [60.36837655498119]
We propose a Trajectory-based validated-by-Reproducing Agent-benchmark Complexity Evolution framework.<n>This framework takes an original task from an existing benchmark and encourages agents to evolve it into a new task with higher difficulty.<n>Experiments on the GAIA benchmark demonstrate that the TRACE framework consistently enhances task complexity while improving the reliability of correctness.
arXiv Detail & Related papers (2025-10-01T01:52:52Z) - WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback [78.55946306325914]
We identify key reasoning skills essential for effective web agents.<n>We reconstruct the agent's reasoning algorithms into chain-of-thought rationales.<n>Our approach yields significant improvements across multiple benchmarks.
arXiv Detail & Related papers (2025-05-26T14:03:37Z) - A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
arXiv Detail & Related papers (2025-04-07T21:01:25Z) - DANLI: Deliberative Agent for Following Natural Language Instructions [9.825482203664963]
We propose a neuro-symbolic deliberative agent that applies reasoning and planning based on its neural and symbolic representations acquired from past experience.
We show that our deliberative agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark.
arXiv Detail & Related papers (2022-10-22T15:57:01Z) - CausalCity: Complex Simulations with Agency for Causal Discovery and
Reasoning [68.74447489372037]
We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning.
A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios.
We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
arXiv Detail & Related papers (2021-06-25T00:21:41Z)
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.