title: MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use
summary: When a human requests an LLM to complete a coding task using functionality
  from a large code repository, how do we provide context from the repo to the
  LLM? One approach is to add the entire repo to the LLM's context window.
  However, most tasks involve only fraction of symbols from a repo, longer
  contexts are detrimental to the LLM's reasoning abilities, and context windows
  are not unlimited. Alternatively, we could emulate the human ability to
  navigate a large repo, pick out the right functionality, and form a plan to
  solve the task. We propose MutaGReP (Mutation-guided Grounded Repository Plan
  Search), an approach to search for plans that decompose a user request into
  natural language steps grounded in the codebase. MutaGReP performs neural tree
  search in plan space, exploring by mutating plans and using a symbol retriever
  for grounding. On the challenging LongCodeArena benchmark, our plans use less
  than 5% of the 128K context window for GPT-4o but rival the coding performance
  of GPT-4o with a context window filled with the repo. Plans produced by
  MutaGReP allow Qwen 2.5 Coder 32B and 72B to match the performance of GPT-4o
  with full repo context and enable progress on the hardest LongCodeArena tasks.
  Project page: zaidkhan.me/MutaGReP
paper_authors._id: 67be572f65ae638b17d35eae
paper_authors.name: Zaid Khan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:27:57.798Z
paper_authors._id: 6301c3e0a123c93a5fb295ff
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1661060051926-noauth.jpeg
paper_authors.fullname: Zaid Khan
paper_authors.type: user
paper_authors.user: codezakh
paper_authors._id: 67be572f65ae638b17d35eaf
paper_authors.name: Ali Farhadi
paper_authors._id: 67be572f65ae638b17d35eb0
paper_authors.name: Ranjay Krishna
paper_authors._id: 67be572f65ae638b17d35eb1
paper_authors.name: Luca Weihs
paper_authors._id: 67be572f65ae638b17d35eb2
paper_authors.name: Mohit Bansal
paper_authors._id: 67be572f65ae638b17d35eb3
paper_authors.name: Tanmay Gupta
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-25T23:50:08.896Z
paper_authors._id: 62a108242e30aaf94ec283bb
paper_authors.avatarUrl: /avatars/4da19ae99df6a3fa2d81336eb59cdaa7.svg
paper_authors.fullname: Tanmay Gupta
paper_authors.type: user
paper_authors.user: tanmayg
paper_title: MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use
paper_summary: When a human requests an LLM to complete a coding task using functionality
  from a large code repository, how do we provide context from the repo to the
  LLM? One approach is to add the entire repo to the LLM's context window.
  However, most tasks involve only fraction of symbols from a repo, longer
  contexts are detrimental to the LLM's reasoning abilities, and context windows
  are not unlimited. Alternatively, we could emulate the human ability to
  navigate a large repo, pick out the right functionality, and form a plan to
  solve the task. We propose MutaGReP (Mutation-guided Grounded Repository Plan
  Search), an approach to search for plans that decompose a user request into
  natural language steps grounded in the codebase. MutaGReP performs neural tree
  search in plan space, exploring by mutating plans and using a symbol retriever
  for grounding. On the challenging LongCodeArena benchmark, our plans use less
  than 5% of the 128K context window for GPT-4o but rival the coding performance
  of GPT-4o with a context window filled with the repo. Plans produced by
  MutaGReP allow Qwen 2.5 Coder 32B and 72B to match the performance of GPT-4o
  with full repo context and enable progress on the hardest LongCodeArena tasks.
  Project page: zaidkhan.me/MutaGReP
paper_ai_keywords: neural tree search plan space mutating plans symbol retriever LongCodeArena benchmark GPT-4o Qwen 2.5 Coder 32B Qwen 2.5 Coder 72B