Rover: Context-aware Conflict Resolution with LLM

• URL: http://arxiv.org/abs/2605.17279v1
• Date: Sun, 17 May 2026 06:21:40 GMT
• Title: Rover: Context-aware Conflict Resolution with LLM
• Abstract summary: Code merging is a significant challenge, particularly in large-scale projects.<n>Existing solutions, including program analysis and machine learning, show promise but face critical limitations.<n>We introduce Rover, a novel conflict resolution system that integrates program analysis with large language models.
• Score: 10.069365211450997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code merging is a significant challenge, particularly in large-scale projects. Existing solutions, including program analysis and machine learning, show promise but face critical limitations. Program analysis lacks the ability to infer developers' intentions, relying on conservative strategies that offload unresolved conflicts for manual handling. Meanwhile, model-based approaches struggle with conflicts involving complex code dependencies due to insufficient contextual awareness. To address these gaps, we introduce Rover, a novel conflict resolution system that integrates program analysis with large language models (LLMs). To obtain context-aware prompts, we propose Multi-layer Code Property Graph (MtCPG), a new representation capturing inter-file dependencies and enabling contextual analysis for a given conflict. Using graph connectivity algorithms, Rover further clusters conflicting code and associated changes into meaningful "contexts" that guide the LLM in generating accurate resolutions. We compared Rover with standalone LLMs, machine learning baseline MergeGen, and suggestion provider tool WizardMerge with adjacent code as the contexts. Evaluation results show that Rover surpasses all of these approaches in terms of conflict resolution, achieving higher similarity to ground-truth resolutions at character, lexical, and semantic levels.

Related papers

• Large Language Models in Resolving Contextual Knowledge Conflicts [7.7973038442706715]
  We introduce a taxonomy of six types of contextual conflicts and contribute a comprehensive dataset ContextConflict.<n>The dataset contains 5,781 samples, covers both reasoning and summarization tasks, and includes both explicit contradictions and implicit conflicts that require multi-step reasoning.<n>We propose a training-free, label-free steering method that steers activations to encourage a more comprehensive incorporation of evidences for better conflict resolution.
  arXiv  Detail & Related papers  (2026-09-02T20:30:15Z)
• LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms [0.0]
  generative approaches based on Large Language Models (LLMs) and optimization approaches from Search-Based Software Engineering (SBSE)<n>This paper presents the first in-depth empirical study directly comparing these paradigms to identify their capabilities and limitations in real-world scenarios.
  arXiv  Detail & Related papers  (2026-05-15T21:34:32Z)
• Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding [51.242997961841276]
  Large language models accumulate parametric knowledge through pre-training.<n> knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts with external knowledge in the context.<n>Existing methods address knowledge conflicts through contrastive decoding, but in conflict-free scenarios, static approaches disrupt output distribution.<n>We propose Dynamic Cognitive Reconciliation Decoding (DCRD), to predict and context-memory conflicts.
  arXiv  Detail & Related papers  (2026-05-12T14:29:45Z)
• AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction [43.86866325776962]
  We propose a framework that enhances the understanding of complex documents by synergizing the capabilities of multiple models.<n> Specifically, we introduce a Query Complexity Router to allocate reasoning paths based on the analysis of query difficulty.
  arXiv  Detail & Related papers  (2026-03-05T02:29:33Z)
• CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering [53.7094431951084]
  Knowledge-based visual question answering (KB-VQA) demonstrates significant potential for handling knowledge-intensive tasks.<n>Conflicts arise between static parametric knowledge in vision language models and dynamically retrieved information.<n>We propose textbfCC-VQA as a training-free, conflict- and correlation-aware method for KB-VQA.
  arXiv  Detail & Related papers  (2026-02-27T11:56:26Z)
• Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning [78.86309644343295]
  Multimodal large language models (MLLMs) in long chain-of-thought reasoning often fail when different knowledge sources provide conflicting signals.<n>We formalize these failures under a unified notion of knowledge conflict, distinguishing input-level objective conflict from process-level effective conflict.<n>Our findings provide a mechanism-level view of multimodal reasoning under knowledge conflict and enable principled diagnosis and control of long-CoT failures.
  arXiv  Detail & Related papers  (2026-02-16T07:10:44Z)
• That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation [55.78914774437411]
  Large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt.<n>We propose a domain-agnostic framework for constructing and interpreting such conflicts.<n>We show that activation-level steering can achieve up to a textbf12.6% improvement in steering success over a random baseline.
  arXiv  Detail & Related papers  (2025-10-21T22:27:56Z)
• When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements [56.29265568399648]
  We argue that disagreements prevent premature consensus and expand the explored solution space.<n>Disagreements on task-critical steps can derail collaboration depending on the topology of solution paths.
  arXiv  Detail & Related papers  (2025-02-21T02:24:43Z)
• Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs [55.74117540987519]
  This paper explores the problem of commonsense level vision-knowledge conflict in Multimodal Large Language Models (MLLMs)<n>We introduce an automated framework, augmented with human-in-the-loop quality control, to generate inputs designed to simulate and evaluate these conflicts in MLLMs.<n>Using this framework, we have crafted a diagnostic benchmark consisting of 374 original images and 1,122 high-quality question-answer pairs.
  arXiv  Detail & Related papers  (2024-10-10T17:31:17Z)
• Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models [33.76903352835436]
  Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs. These models are prone to parametric knowledge conflicts, which arise from inconsistencies of represented knowledge between their vision and language components. We present a systematic approach to detect, interpret, and mitigate them.
  arXiv  Detail & Related papers  (2024-10-04T17:59:28Z)
• CONGRA: Benchmarking Automatic Conflict Resolution [3.9910625211670485]
  ConGra is a benchmarking scheme designed to evaluate the performance of software merging tools. We build a large-scale evaluation dataset based on 44,948 conflicts from 34 real-world projects.
  arXiv  Detail & Related papers  (2024-09-21T12:21:41Z)

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.