SWE-INTERACT：重新构想用户驱动的长时间编码会话的软件工程基准

Computer Science > Machine Learning

arXiv:2606.30573v1 (cs) [Submitted on 29 Jun 2026]

Title:SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions

Authors:Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal, Yunzhong He View a PDF of the paper titled SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions, by Mohit Raghavendra and 3 other authors View PDF

Abstract:We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically provide complete requirements upfront and evaluate agents on autonomous implementation. In contrast, SWE-Interact places agents in a realistic developer workflow: a carefully designed user simulator starts with vague or incomplete instructions, progressively reveals requirements, inspects the agent's workspace, and provides targeted feedback, revisions, and new constraints until the full task goal has been handed off. Grounded in large-scale studies of real coding-agent interactions, this setup tests whether agents can discover user intent, adapt to evolving requirements, and build on their own prior work. Across a suite of frontier and open-weight models, we find that strong performance on single-turn SWE tasks does not reliably transfer to multi-turn, user-driven workflows: the best-performing models solve roughly 50% of single-turn baseline tasks but only 25% of the corresponding SWE-Interact tasks. The strongest models in our evaluation, including Opus 4.8 and GPT 5.5, start strong even in the face of vague initial instructions, persevere until all the requirements are surfaced by the user, integrate them better and write clean code. However, they still suffer from over-agentic coding, forgetting requirements and technical mistakes. Weaker models start poorly under ambiguity, give up early, forget or ignore instructions and rework their code more. Overall, SWE-Interact measures an orthogonal, real-world capability axis for frontier model development: interactive goal discovery and iterative refinement with a user in the loop.

Comments:	-
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2606.30573 [cs.LG]
	(or arXiv:2606.30573v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2606.30573 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Mohit Raghavendra [view email]
[v1] Mon, 29 Jun 2026 17:17:45 UTC (865 KB)
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