BLAgent: Agentic RAG for File-Level Bug Localization

• URL: http://arxiv.org/abs/2605.17965v1
• Date: Mon, 18 May 2026 07:20:13 GMT
• Title: BLAgent: Agentic RAG for File-Level Bug Localization
• Abstract summary: BLAgent is a novel agentic RAG framework for file-level bug localization.<n> BLAgent attains over 78% Top-1 accuracy with open-source models.<n> BLAgent improves end-to-end repair success by over 20%.
• Score: 2.2917707112773593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bug localization remains a key bottleneck in downstream software maintenance tasks, including root cause analysis, triage, and automated program repair (APR), despite recent advances in large language model (LLM)-based repair systems. File-level bug localization is especially critical in hierarchical pipelines, where errors can propagate to downstream stages such as statement-level localization or patch generation. While Retrieval-Augmented Generation (RAG) offers a promising direction for grounding LLMs in repository context, existing RAG pipelines rely on static retrieval and lack the reasoning needed to identify faulty code accurately. In this work, we present BLAgent, a novel agentic RAG framework for file-level bug localization that integrates three key ideas: (i) code structure-aware repository encoding with path-augmented AST-based chunking, (ii) dual-perspective query transformation capturing both structural and behavioral signals, and (iii) two-phase agentic reranking combining symbolic inspection with evidence-grounded reasoning. Unlike prior graph-based or multi-hop agentic approaches, BLAgent performs bounded reasoning over a compact candidate set, balancing accuracy and cost. On SWE-bench Lite, BLAgent attains over 78% Top-1 accuracy with open-source models and over 86% with a closed-source model, while being over 18x cheaper than the strongest baseline using the same model. When integrated into an APR framework, it improves end-to-end repair success by over 20%.

Related papers

• DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection [16.007199928732614]
  We present DREA, a hypothesis-driven framework for repository-level vulnerability detection.<n>Goal-directed context acquisition is the primary source of detection improvement in this design.<n>To support evaluation, we construct RepoPairBench, a repository-grounded benchmark of validated Python vulnerability-fix pairs from real-world projects.
  arXiv  Detail & Related papers  (2026-07-15T04:49:05Z)
• AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs [15.215103223762364]
  AgentKGV integrates dynamic routing and iterative query rewriting to verify knowledge graphs reliably at industrial scale.<n>Two-stage training strategy transfers reasoning ability from a large teacher model into a small model for stable query rewriting and reasoning.<n>Our framework improves macro-F1 over single-turn RAG by 5.5 %p, and two-stage training does it further by 9.4 %p.
  arXiv  Detail & Related papers  (2026-07-10T04:22:34Z)
• TRAJEVAL: Decomposing Code Agent Trajectories for Fine-Grained Diagnosis [23.834704102474927]
  Code agents can resolve GitHub issues, yet when they fail, current evaluation provides no visibility into where or why.<n>We introduce TRAJEVAL, a diagnostic framework that decomposes agent trajectories into three interpretable stages.<n>We validate that these diagnostics are predictive, achieving model-level Pass@1 prediction within 0.87-2.1% MAE.
  arXiv  Detail & Related papers  (2026-03-25T05:27:03Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• TopoChunker: Topology-Aware Agentic Document Chunking Framework [5.304983617085637]
  TopoChunker is an agentic framework that maps heterogeneous documents onto a Structured Intermediate Representation (SIR)<n>To balance structural fidelity with computational cost, TopoChunker employs a dual-agent architecture.<n>It outperforms the strongest LLM-based baseline by 8.0% in absolute generation accuracy and achieves an 83.26% Recall@3, while simultaneously reducing token overhead by 23.5%.
  arXiv  Detail & Related papers  (2026-03-19T02:15:10Z)
• Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition [53.50448142467294]
  RAIM is a multi-design and architecture-aware framework for repository-level feature addition.<n>It shifts away from linear patching by generating multiple diverse implementation designs.<n>Experiments on the NoCode-bench Verified dataset demonstrate that RAIM establishes a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-03-02T12:50:40Z)
• SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair [22.745971570878435]
  We propose a Suggestion-Guided multi-Agent framework for repository-level software repair.<n> SGAgent introduces a suggestion phase to strengthen the transition from localization to repair.<n>Three specialized sub-agents collaborate to achieve automated end-to-end software repair.
  arXiv  Detail & Related papers  (2026-02-27T03:32:47Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization [14.474705451897691]
  We present T2L-Agent, a project-level, end-to-end framework that plans its own analysis and narrows scope from modules to exact vulnerable lines.<n>We benchmark T2L-ARVO, a diverse, expert-verified 50-case benchmark spanning five crash families and real-world projects.<n>On T2L-ARVO, T2L-Agent achieves up to 58.0% detection and 54.8% line-level localization, substantially outperforming baselines.
  arXiv  Detail & Related papers  (2025-09-30T22:27:18Z)
• Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
  Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
  arXiv  Detail & Related papers  (2025-09-29T18:20:27Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [39.11825182386288]
  FixAgent is an end-to-end framework for unified debug through multi-agent synergy. It significantly outperforms state-of-the-art repair methods, fixing 1.25$times$ to 2.56$times$ bugs on the repo-level benchmark, Defects4J.
  arXiv  Detail & Related papers  (2024-04-26T04:55:35Z)

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.