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Vision-Language Models Swayed by Textual Cues in Robot Progress Judging

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper reveals that vision-language models (VLMs) used to judge robot progress can be easily swayed by accompanying text. When provided with a single log line indicating progress, even if the robot is actually failing, VLMs like Gemini, Qwen, and GPT incorrectly assessed progress 10-35% more often. This effect was significantly reduced when the logs were flagged as potentially inaccurate, and specific "task complete" claims had a more pronounced impact on Qwen than Gemini. AI

IMPACT Highlights potential vulnerabilities in VLM-based evaluation systems, suggesting a need for more robust methods to prevent manipulation.

RANK_REASON Academic paper detailing a new finding about VLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision-Language Models Swayed by Textual Cues in Robot Progress Judging

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Academic paper detailing a new finding about VLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weiyan Zhao, Sunny Yang ·

    Execution Logs Sway VLM Judges of Robot Progress

    arXiv:2610.34151v1 Announce Type: new Abstract: Vision-language models now grade robot videos, and text placed beside the frames can change their verdicts. On clips where the robot is losing progress, we add one log line saying the robot is making progress. Across Gemini, Qwen an…

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