Author name cluster

Carlos E. Jimenez

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICML Conference 2025 Conference Paper

EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities

Talor Abramovich
Meet Udeshi
Minghao Shao
Kilian Lieret
Haoran Xi
Kimberly Milner
Sofija Jancheska
John Yang 0002

Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited. We present EnIGMA, an LM agent for autonomously solving Capture The Flag (CTF) challenges. We introduce new tools and interfaces to improve the agent’s ability to find and exploit security vulnerabilities, focusing on interactive terminal programs. These novel Interactive Agent Tools enable LM agents, for the first time, to run interactive utilities, such as a debugger and a server connection tool, which are essential for solving these challenges. Empirical analysis on 390 CTF challenges across four benchmarks demonstrate that these new tools and interfaces substantially improve our agent’s performance, achieving state-of-the-art results on NYU CTF, Intercode-CTF, and CyBench. Finally, we analyze data leakage, developing new methods to quantify it and identifying a new phenomenon we term soliloquizing, where the model self-generates hallucinated observations without interacting with the environment.

Details

ICLR Conference 2025 Conference Paper

SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?

John Yang 0002
Carlos E. Jimenez
Alex L. Zhang
Kilian Lieret
Joyce Yang
Xindi Wu
Ori Press
Niklas Muennighoff

Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as images. This limited coverage motivates our inquiry into how existing systems might perform on unrepresented software engineering domains (e.g., front-end, game development, DevOps), which use different programming languages and paradigms. Therefore, we propose SWE-bench Multimodal (SWE-bench M), to evaluate systems on their ability to fix bugs in visual, user-facing JavaScript software. SWE-bench M features 617 task instances collected from 17 JavaScript libraries used for web interface design, diagramming, data visualization, syntax highlighting, and interactive mapping. Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. Lastly, we show that SWE-agent’s flexible language-agnostic features enable it to substantially outperform alternatives on SWE-bench M, resolving 12% of task instances compared to 6% for the next best system.

Details

NeurIPS Conference 2024 Conference Paper

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

John Yang
Carlos E. Jimenez
Alexander Wettig
Kilian Lieret
Shunyu Yao
Karthik Narasimhan
Ofir Press

Language model agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that language model agents represent a new category of end users with their own needs and abilities, and would benefit from specially built interfaces to the software they use. We investigate how the role of interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates language model agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12. 5% and 87. 7%, respectively, far exceeding the previous state-of-the-art achieved with non-interactive language models. Finally, we provide insight on how the design of the agent-computer interface can impact agents' behavior and performance.

PDF Details DOI

ICLR Conference 2024 Conference Paper

SWE-bench: Can Language Models Resolve Real-world Github Issues?

Carlos E. Jimenez
John Yang 0002
Alexander Wettig
Shunyu Yao 0006
Kexin Pei
Ofir Press
Karthik Narasimhan

Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.

Details