arXiv · 2609.29754
SWE-PolyVision: Benchmarking Cross-Image Abductive Reasoning for Repository-Level Software Engineering
Abstract
Current multimodal software-engineering benchmarks expose images as additional context, but do not test whether an agent can integrate evidence distributed across images into a verified repository-level repair. We present SWE-PolyVision, an executable benchmark of 92 real tasks from 36 open-source organizations, with 48 public tasks and 44 private holdouts. The release contains 402 static images and 6 videos, with at least two visual inputs per task. Each task pairs a fixed pre-fix repository with an isolated verifier and is evaluated under the supported conditions among three access modes: Text-only, Native Vision, and Tool-mediated Vision. Across eleven coding models, visual access changes which tasks are solved, but effects depend on both model and task. Two trace-linked Native Vision cases illustrate how complementary visual and textual clues can lead to source-localized, verified repairs; controlled interventions show that this conversion is not yet stable across inputs. SWE-PolyVision thus separates the availability of multi-image evidence from its successful use in repository-level repair, without treating patch success alone as proof of explicit reasoning.
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Jiajun Wu, Leixin Sun, Zihan Tan, Yitao Liu, Zhenjun Zhao, Danyang Zhang, Shuo Li, Jiaru Qian, Yuxin Wu, Shanghaoran Quan, Chuangxin Zhao, Yangxu Liao, Yang Liu, Bin Chong, Guancheng Wan. 2026-10-15. SWE-PolyVision: Benchmarking Cross-Image Abductive Reasoning for Repository-Level Software Engineering. https://arxiv.org/abs/2609.29754
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