// AI news · 2026-11-01 · Towards Data Science
Image Labels Outperform Prompt Tweaks for Vision Models
Vision-language models perform better when image annotations are added than when prompts are modified.
Published 2026-11-01 22:00 (Beijing time)Source Towards Data ScienceAuto-summarised, fact-checked against source
01Summary
Adding explicit labels for start and end points significantly improves accuracy, especially in grid-based puzzles. Generic annotations like row/column numbers or grid lines offer little benefit and sometimes degrade performance. The most effective modifications resolve specific visual misreadings and link image content directly to task language.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- Start/end labels boost Qwen 3.5 397B accuracy from 14.4% to 32.6%
- Row/column numbers offer little benefit and can degrade model performance
- Cell coordinates or explicit symbols improve path start/end detection
- Models misidentify start/end points on original images; annotations fix this issue
03Source
Towards Data Science · published 2026-11-01 22:00 Beijing time
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