LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms

• URL: http://arxiv.org/abs/2605.16646v1
• Date: Fri, 15 May 2026 21:34:32 GMT
• Title: LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: The resolution of software merge conflicts is being reshaped by two competing paradigms: generative approaches based on Large Language Models (LLMs) and optimization approaches from Search-Based Software Engineering (SBSE). While tools from both paradigms have shown promise, their relative strengths, weaknesses, and trade-offs are not yet well understood. Objective: This paper presents the first in-depth empirical study directly comparing these paradigms to identify their capabilities and limitations in real-world scenarios. Method: We evaluated MergeGen, a state-of-the-art LLM-based tool, against SBCR, a novel SBSE approach employing a Random Restart Hill Climbing (RRHC) algorithm. The comparison used thousands of real-world conflicts from open-source projects written in Java, C#, JavaScript, and TypeScript. Results: Our findings reveal fundamental trade-offs. The LLM paradigm excels at resolving conflicts with imbalanced content by leveraging learned patterns. However, it struggles with non-English content and large inputs, which can lead to truncated or empty resolutions. Conversely, the SBSE paradigm demonstrates superior generalization across datasets and performs best on balanced conflicts, highlighting its potential as a robust, data-independent alternative. Conclusions: Neither paradigm is a silver bullet. Our findings highlight context-dependent strengths and motivate the development of hybrid systems that combine the complementary capabilities of LLM and SBSE approaches to create more robust and reliable merge conflict resolution tools.

Related papers

• CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection [56.64398465636452]
  generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability.<n>We propose textbfConflict-textbfOriented textbfREasoning (textbfCORE) framework, an effective paradigm that learns to endows multimodal large language models (MLLMs) with explicit conflict-capturing capability.<n> CORE achieves robust and generalizable conflict detection, effectively and rapidly adapting to unseen manipulation types with a few samples or in even zero-shot settings.
  arXiv  Detail & Related papers  (2026-06-02T02:53:48Z)
• Rover: Context-aware Conflict Resolution with LLM [10.069365211450997]
  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.
  arXiv  Detail & Related papers  (2026-05-17T06:21:40Z)
• 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)
• A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Bandits [2.969473917919491]
  Large language models (LLMs) have become powerful and widely used systems for language understanding and generation.<n>Multi-armed bandit (MAB) algorithms provide a principled framework for adaptive decision-making under uncertainty.<n>This survey explores the potential at the intersection of these two fields.
  arXiv  Detail & Related papers  (2026-01-19T10:53:57Z)
• 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)
• FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation [37.28571879699906]
  Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks.<n>This paper proposes FaithfulRAG, a novel framework that resolves knowledge conflicts by explicitly modeling discrepancies between the models parametric knowledge and retrieved context.
  arXiv  Detail & Related papers  (2025-06-10T16:02:54Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• ECon: On the Detection and Resolution of Evidence Conflicts [56.89209046429291]
  The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems. This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
  arXiv  Detail & Related papers  (2024-10-05T07:41:17Z)
• 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)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• 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)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)

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.