想知道编程智能体在真实对话开发中能撑多久吗？SWE-Interact测试了Opus 4.8和GPT 5.5在模糊需求下逐步迭代的能力，结果比单轮任务差一半。
SWE-Interact是一个新测试平台，用于评估编码代理在多轮交互、用户驱动的软件工程任务中的表现。现有SWE基准（如SWE-bench）通常预先提供完整需求，而SWE-Interact通过用户模拟器逐步揭示需求、提供反馈和约束。在单轮任务中，最强模型（如Opus 4.8和GPT 5.5）解决率约50%，但在SWE-Interact多轮任务中仅解决约25%。该基准测量了模型在交互目标发现和迭代细化中的正交能力。
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
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.