LogicLoc combines LLMs with Datalog to achieve accurate repo-level code localization without relying on keyword shortcuts in benchmarks.
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5 Pith papers cite this work. Polarity classification is still indexing.
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StackRepoQA shows LLMs reach only moderate accuracy on multi-file Java QA tasks, with gains from graph-based retrieval but frequent reliance on verbatim answer reproduction.
SWE-QA creates a new repository-level code QA benchmark with 576 pairs and an agentic LLM framework, showing promise but open challenges for models handling complex codebases.
SWE Atlas is a benchmark suite for coding agents that evaluates Codebase Q&A, Test Writing, and Refactoring using comprehensive protocols assessing both functional correctness and software engineering quality.
AOCI creates an incremental symbolic-semantic index per code unit that gives LLMs a complete, consistent repository view, outperforming baselines with zero defects on 19 industrial tasks while using far fewer tokens.
citing papers explorer
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Neurosymbolic Repo-level Code Localization
LogicLoc combines LLMs with Datalog to achieve accurate repo-level code localization without relying on keyword shortcuts in benchmarks.
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Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering
StackRepoQA shows LLMs reach only moderate accuracy on multi-file Java QA tasks, with gains from graph-based retrieval but frequent reliance on verbatim answer reproduction.
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SWE-QA: Can Language Models Answer Repository-level Code Questions?
SWE-QA creates a new repository-level code QA benchmark with 576 pairs and an agentic LLM framework, showing promise but open challenges for models handling complex codebases.
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SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution
SWE Atlas is a benchmark suite for coding agents that evaluates Codebase Q&A, Test Writing, and Refactoring using comprehensive protocols assessing both functional correctness and software engineering quality.
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AOCI: Symbolic-Semantic Indexing for Practical Repository-Scale Code Understanding with LLMs
AOCI creates an incremental symbolic-semantic index per code unit that gives LLMs a complete, consistent repository view, outperforming baselines with zero defects on 19 industrial tasks while using far fewer tokens.