Gistify! Codebase-Level Understanding via Runtime Execution
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is central. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full access to a codebase along with a specific entrypoint (e.g., a python command), and the generated file must replicate the output of the same command ran under the full codebase, while containing only the essential components necessary to execute the provided command. Success on Gistify requires both structural understanding of the codebase, accurate modeling of its execution flow as well as the ability to produce potentially large code patches. Our findings show that current state-of-the-art models struggle to reliably solve Gistify tasks, especially ones with long executions traces.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 35 of its 47 references that are in the map. First posted on arXiv 30 Oct 2025.
Builds on
35 of 47 references.
Closest ancestors · 5
Same direction, newest first
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Other references · 30
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Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
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- 2026SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests1 Jul 2026
- 2026RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations25 May 2026
- 2025CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents30 Nov 2025
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
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