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Researchers develop Glance-or-Gaze to improve LMM visual search with adaptive focus

Researchers have introduced Glance-or-Gaze (GoG), a new framework designed to improve Large Multimodal Models (LMMs) in handling knowledge-intensive visual queries. Unlike previous methods that retrieve information indiscriminately, GoG employs a Selective Gaze mechanism to adaptively focus on relevant image regions or global context. The framework is trained using a dual-stage approach, combining supervised fine-tuning with complexity-adaptive reinforcement learning to enhance iterative reasoning and performance on complex visual tasks. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel adaptive search mechanism for LMMs, potentially improving efficiency and accuracy in complex visual query tasks.

RANK_REASON This is a research paper detailing a new framework for LMMs.

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Hongbo Bai, Yujin Zhou, Yile Wu, Chi-Min Chan, Pengcheng Wen, Kunhao Pan, Sirui Han, Yike Guo ·

    Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning

    arXiv:2601.13942v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries involving long-tail entities or evolving information due to static parametric knowledge.…