UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development

• URL: http://arxiv.org/abs/2607.02186v1
• Date: Thu, 02 Jul 2026 13:56:57 GMT
• Title: UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development
• Abstract summary: UA-ChatDev is an uncertainty-aware multi-agent software development framework.<n>It integrates uncertainty quantification into agent interactions.<n>It consistently outperforms existing single-agent and multi-agent software development frameworks.
• Score: 0.3670422696827525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software development is a complex task that demands cooperation among agents with diverse roles. Large language models (LLMs) have enabled autonomous multi-agent software development frameworks that leverage role-based collaboration to automate requirements analysis, coding, testing, and refinement. However, existing approaches typically assume that intermediate agent outputs are equally reliable, leaving them vulnerable to hallucination propagation, where incorrect decisions generated in early development phases are transferred to downstream agents and negatively impact final software quality. To address this challenge, we propose UA-ChatDev, an uncertainty-aware multi-agent software development framework that integrates uncertainty quantification into agent interactions. It introduces a lightweight uncertainty estimation mechanism based on token-level log probabilities to assess the confidence of agent responses and employs phase-aware threshold calibration to selectively trigger retrieval-based verification when uncertainty exceeds acceptable levels. Extensive experiments on the SRDD benchmark demonstrate that UA-ChatDev consistently outperforms existing single-agent and multi-agent software development frameworks across completeness, executability, consistency, and overall quality metrics. Further ablation studies and communication analyses verify that uncertainty-aware interactions enhance code execution reliability.

Related papers

• From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents [75.69180947909148]
  We propose a trajectory-level UQ framework for large language model (LLM) agents.<n>RuPA represents an execution history as a directed trajectory graph in which reasoning states, tool interactions, and environment feedback are nodes connected by temporal and semantic dependency edges.<n>We evaluate RUPA on representative agent benchmarks, including $$-2, Terminal-Bench-2, and GAIA, using 6 open-source LLMs spanning multiple model families.
  arXiv  Detail & Related papers  (2026-08-17T01:40:14Z)
• Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems [11.562923882714095]
  This paper introduces a semantic-uncertainty-guided orchestration approach, HASSUM.<n>It estimates uncertainty using semantic entropy and semantic density, which measure trust at the level of answer semantics rather than output probabilities.<n>Because the approach operates independently of any particular agent architecture, it can be integrated into a broad range of hierarchical and collaborative multi-agent systems.
  arXiv  Detail & Related papers  (2026-08-11T03:55:53Z)
• Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management [0.7329200485567826]
  In software engineering, multi-agent AI systems are increasingly used to automate requirements analysis, architecture design, test generation, and traceability linking.<n>We propose a trust-aware coordination framework where a shared knowledge graph serves as both centralized semantic memory and a coordination surface.<n>We evaluate on an automotive software engineering case study measuring link prediction calibration, protocol effectiveness, threshold sensitivity, and the impact of traceability seeding.
  arXiv  Detail & Related papers  (2026-06-15T18:41:50Z)
• The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? [80.24951682268332]
  We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.<n>To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking.<n>We demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models.
  arXiv  Detail & Related papers  (2026-06-03T04:58:17Z)
• Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
  Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
  arXiv  Detail & Related papers  (2026-05-17T10:26:37Z)
• Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
  Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
  arXiv  Detail & Related papers  (2026-05-14T14:36:13Z)
• A Self-Healing Framework for Reliable LLM-Based Autonomous Agents [0.0]
  This paper proposes a reliability-aware self-healing framework for LLM-based software agents.<n>The proposed framework was implemented in a multi-agent workflow environment and evaluated using real-world task scenarios.
  arXiv  Detail & Related papers  (2026-05-07T13:10:41Z)
• Agentic Confidence Calibration [67.50096917021521]
  Holistic Trajectory (HTC) is a novel diagnostic framework for AI agents.<n>HTC consistently surpasses strong baselines in both calibration and discrimination.<n>HTC provides interpretability by revealing the signals behind failure.
  arXiv  Detail & Related papers  (2026-01-22T09:08:25Z)
• The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance [0.0]
  Current AI-based test generators produce invalid, redundant, or non-executable tests due to lack of execution aware feedback.<n>This paper introduces a closed-loop, self-correcting system in which a Test Generation Agent, an Execution and Analysis Agent, and a Review and Optimization Agent collaboratively generate, execute, analyze, and refine tests.
  arXiv  Detail & Related papers  (2026-01-05T18:20:14Z)
• Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives [2.7295959384567356]
  Co-Investigator AI is an agentic framework optimized to produce Suspicious Activity Reports (SARs) significantly faster and with greater accuracy than traditional methods.<n>We demonstrate its ability to streamline SAR drafting, align narratives with regulatory expectations, and enable compliance teams to focus on higher-order analytical work.
  arXiv  Detail & Related papers  (2025-09-10T08:16:04Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)

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.