Multi-image evidence can help models repair software, but unreliably

SWE-PolyVision tests whether coding models can combine clues across multiple visuals and text to produce repository-level repairs that pass isolated verification.

Independent
Jiajun Wu · Leixin Sun · Zihan Tan · Yitao Liu · Shuo Li · Jiaru Qian · +6 more
Research Digest··2 min read
The authors introduce an executable benchmark containing 92 software-repair tasks, each with at least two visual inputs and a fixed pre-repair repository.

The authors assembled 92 tasks from 36 open-source organizations, split into 48 public tasks and 44 private holdouts.

Why this paper

Independent · Part of Live Software Adaptation, now 4 papers

In one line

SWE-PolyVision benchmarks cross-image reasoning for repository-level repair and finds visual access effects are model- and task-dependent.

What we could check

  • ·No code link found
  • ·No weights link found
  • ·No dataset link found
  • ·No compute details found
  • ·No stated limitations found
  • ·No benchmark numbers found

Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.

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