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SWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

Abstract

Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a coding agent and evaluate whether the agent successfully solves the task. However, in real-world use cases, prompts are often not as well-formulated or structured as existing GitHub issues. Additionally, a coding agent session is not limited to a sin

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#214 most recent of 960 cs.AI papers we have recorded · ↑ newer: From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 · ↓ older: Which Differences Matter? Controlled Tests of Rule Transfer, Joint Rel
Cite this page: SWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation: the #214 most recent of 960 cs.AI papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/swe-dive-multi-turn-coding-agent-evaluation-with-dynamic-user-simulation.html
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