Can Large Language Models Resolve Real Java Merge Conflicts? An Evaluation with a Calibrated LLM-as-Judge

• URL: http://arxiv.org/abs/2607.27674v1
• Date: Thu, 30 Jul 2026 04:38:17 GMT
• Title: Can Large Language Models Resolve Real Java Merge Conflicts? An Evaluation with a Calibrated LLM-as-Judge
• Abstract summary: Large language models (LLMs) can produce a candidate resolution for almost any conflict.<n> measuring whether those resolutions are actually good at scale is hard.<n>We study both problems together on real Java merge conflicts from ConflictBench.
• Score: 6.965857844586889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Merge conflicts are a recurring cost of collaborative software development, and the traditional structured and semi-structured merge tools that address them frequently abstain: when their heuristics do not apply, they leave the conflict unresolved. Large language models (LLMs) can instead produce a candidate resolution for almost any conflict, but measuring whether those resolutions are actually good at scale is hard, because obtaining human desirability judgments for every model output does not scale. We study both problems together on real Java merge conflicts from ConflictBench. We first build an LLM solver as a generate-validate-retry agent that uses only inference-time signals (conflict markers, a Java parser, and duplicate-declaration checks) and never sees the developer's answer. We then evaluate its resolutions with a two-metric suite: (1) a developer-match LLM-as-judge implemented as a G-Eval metric and, crucially, calibrated against ConflictBench's human labels before use, and (2) a deterministic structural-validity check that uses no LLM. On a meta-evaluation of 292 human-labeled cases, the judge reaches 100% precision (zero false accepts) at 64.6% recall, so every acceptance is trustworthy and every downstream rate is a conservative lower bound. Under this validated judge, LLM solvers match the developer's own resolution on about 55% of true conflicts (conservative floor), and under a coverage-fair comparison the LLMs (55-59%) beat the strongest traditional tool (AutoMerge, 36.7%) by roughly 18-22 points; the edge comes almost entirely from coverage, not raw accuracy, since the tools abstain on 20-90% of conflicts while the LLM under forced resolution abstains on none. Finally, the LLM judge accepted 4 of the 5 resolutions that fail the deterministic structural check, evidence that structural correctness must not be delegated to an LLM.

Related papers

• When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals [0.0]
  LLM-as-judge is increasingly the default for evaluating AI systems in enterprise pipelines.<n>We show that consistency -- agreement among judges, or a model's own samples -- indicates correctness.<n>We ask when agreement is nonetheless a usable proxy, in a large-scale cross-runner study.
  arXiv  Detail & Related papers  (2026-07-09T02:46:51Z)
• LLM-as-a-Verifier: A General-Purpose Verification Framework [74.40111651545979]
  We introduce LLM-as-a-Verifier, a general-purpose verification framework.<n>It provides fine-grained feedback for agentic tasks without requiring additional training.<n>It achieves state-of-the-art performance on several benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T17:59:35Z)
• CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery [51.07538881798502]
  Large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference.<n>We propose CauTion, a framework that reliably integrates LLM domain knowledge into an ensemble of statistical causal discovery algorithms.<n>CauTion consistently outperforms both data-centric and LLM-augmented baselines.
  arXiv  Detail & Related papers  (2026-06-02T13:07:43Z)
• Merge-Bench: Resolve Merge Conflicts with Large Language Models [1.8162282886873073]
  This paper applies machine learning to the difficult and important task of version control merging.<n>We constructed a dataset, Merge-Bench, of 7938 real-world merge conflict hunks from 1439 GitHub repositories.<n>We trained a model, LLMergeJ, to resolve merge conflicts in Java programs.
  arXiv  Detail & Related papers  (2026-05-25T14:17:48Z)
• 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)
• OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation [53.88666485159289]
  We introduce OpenDeepThink, a population-based test-time compute framework that selects via pairwise Bradley-Terry comparison.<n>OpenDeepThink raises Gemini 3.1 Pro's effective Codeforces Elo by +405 points in eight sequential LLM-call rounds.<n>We release CF-73, a curated set of 73 expert-rated Codeforces problems with International Grandmaster annotation and 99% local-evaluation agreement against the official verdict.
  arXiv  Detail & Related papers  (2026-05-14T17:57:40Z)
• ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions [26.40258251641021]
  This dataset is a benchmark for evaluating Large Language Models' ability to detect and resolve conflicts within user instructions.<n>Most proprietary LLMs exhibit strong conflict detection capabilities, whereas among open-source models, only DeepSeek-R1 demonstrates similarly strong performance.<n>Despite their strong conflict detection abilities, LLMs rarely explicitly notify users about the conflicts or request clarification when faced with conflicting constraints.
  arXiv  Detail & Related papers  (2025-11-18T10:49:37Z)
• Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
  Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks.<n>They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct.<n>We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
  arXiv  Detail & Related papers  (2025-01-02T16:38:21Z)
• 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)
• WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia [59.96425443250666]
  Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs) In this work, we conduct a comprehensive evaluation of LLM-generated answers to questions based on contradictory passages from Wikipedia. We benchmark a diverse range of both closed and open-source LLMs under different QA scenarios, including RAG with a single passage, and RAG with 2 contradictory passages.
  arXiv  Detail & Related papers  (2024-06-19T20:13:42Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)

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.