paper-with-me

Papers

Gistify! Codebase-Level Understanding via Runtime Execution

2025-10-30 · Hyunji Lee, Minseon Kim, Chinmay Singh, Matheus Pereira, Atharv Sonwane, Isadora White, Elias Stengel-Eskin, Mohit Bansal, Zhengyan Shi, Alessandro Sordoni, Marc-Alexandre Côté, Xingdi Yuan, Lucas Caccia 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.

📄 PDF Abstract BibTeX arXiv:2510.26790

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Cerberus: Multi-Agent Reasoning and Coverage-Guided Exploration for Static Detection of Runtime Errors

2025-12-24 · Hridya Dhulipala, Xiaokai Rong, Tien N. Nguyen arxiv

In several software development scenarios, it is desirable to detect runtime errors and exceptions in code snippets without actual execution. A typical example is to detect runtime exceptions in online code snippets befo…

Evaluating LLMs on Real-World Software Performance Optimization

2026-06-24 · Ezgi Sarıkayak, Wenchao Gu, Hesham Ghonim, Chunyang Chen arxiv

Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinement, we still lack benchmarks that capture how optimization actually hap…

Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency

2024-06-18 · Leonidas Gee, Milan Gritta, Gerasimos Lampouras, Ignacio Iacobacci

Code Language Models have been trained to generate accurate solutions, typically with no regard for runtime. On the other hand, previous works that explored execution optimisation have observed corresponding drops in fun…

HumanEvalmbpp

tap: A File-Based Protocol for Heterogeneous LLM Agent Collaboration

2026-06-12 · Minseo Kim arxiv

Existing multi-agent software development systems have proposed many forms of agent collaboration, including role-based collaboration and automated code review. However, many systems assume a common runtime, a central co…

ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?

2026-02-23 · Ayush Nangia, Shikhar Mishra, Aman Gokrani, Paras Chopra arxiv

We introduce ISO-Bench, a benchmark for coding agents to test their capabilities on real-world inference optimization tasks. These tasks were taken from vLLM and SGLang, two of the most popular LLM serving frameworks. Ea…