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New V-ICAL benchmark reveals significant limitations in video-based learning for multimodal agents

By PulseAugur Editorial · [1 sources] ·

A new benchmark called V-ICAL has been introduced to evaluate how well multimodal agents can learn from video demonstrations in interactive environments. This benchmark, comprising 342 tasks across 37 environments, assesses agents' ability to translate video examples into executable policies and adapt to novel situations. Current state-of-the-art agents, including Seed-2.1-Pro, Gemini-3.1-Pro, and GPT-5.6, show significant limitations, with the top performer achieving only a 54.4 score, indicating a critical gap in video-based in-context learning capabilities for these agents. AI

IMPACT Highlights a critical gap in multimodal agent capabilities, necessitating advancements in video-based in-context learning.

RANK_REASON The cluster describes a new academic benchmark and evaluation of existing models, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

New V-ICAL benchmark reveals significant limitations in video-based learning for multimodal agents

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The cluster describes a new academic benchmark and evaluation of existing models, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ziqian Fan, Shibo Xu, Junjie Li, Xiangyu Zhao, Shengyuan Ding, Yifan Yang, Zhenjie Yang, Haodong Duan, Yue Zhou, Zhihang Zhong, Xue Yang ·

    V-ICAL Bench: Evaluating Video In-Context Learning for Multimodal Agents in Interactive Environments

    arXiv:2609.15683v1 Announce Type: new Abstract: While In-Context Learning (ICL) enables models to adapt from exemplars without parameter updates, multimodal ICL remains largely underexplored, particularly regarding video demonstrations in interactive environments. For multimodal …

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