{"paper":{"title":"LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Heleno de Souza Campos Junior, Leonardo Gresta Paulino Murta","submitted_at":"2026-05-15T21:34:32Z","abstract_excerpt":"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.\n  Objective: This paper presents the first in-depth empirical study directly comparing these paradigms to identify their capabilities and limitations in real-world scenarios.\n  Method: We evaluated MergeGen, a state-of-the-art LLM-ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16646","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16646/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.409170Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.528209Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"599f9567bbce53bc0a3b5238faed90065dd60c0d243e3127ad911b24fbdd611c"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}