LLM-based merge conflict resolution performs well on imbalanced conflicts but struggles with large or non-English inputs, while search-based methods show better generalization and strength on balanced conflicts.
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3 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Gleaner replaces slow graph-based trace analysis with bag-of-edges set operations plus log semantics and alarm-driven diversity to deliver faster, higher-fidelity sampling that improves RCA accuracy even at 1% rates.
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.
citing papers explorer
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LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms
LLM-based merge conflict resolution performs well on imbalanced conflicts but struggles with large or non-English inputs, while search-based methods show better generalization and strength on balanced conflicts.
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Gleaner: A Semantically-Rich and Efficient Online Sampler for Microservice Diagnostics
Gleaner replaces slow graph-based trace analysis with bag-of-edges set operations plus log semantics and alarm-driven diversity to deliver faster, higher-fidelity sampling that improves RCA accuracy even at 1% rates.
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Improving MPI Error Detection and Repair with Large Language Models and Bug References
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.