Summary
This paper addresses a limitation in self-evolving large multimodal models (LMMs): their multi-role self-play and self-consistency reward schemes optimize for answer agreement without ensuring the decoder actually attends to visual content, relying instead on statistical language priors. The authors identify and term this failure mode 'visual under-conditioning', where the decoder produces self-consistent outputs that are insufficiently grounded in visual tokens. The work, listed on arXiv as 2606.27373 by Shravan Venkatraman, Ritesh Thawkar, and Omkar Thawakar, falls within computer vision research. It is presented here as part of zhichai.net's automated paper digest covering recent arXiv submissions in the CV field.
Paper Overview
Field: Computer Vision (CV)
Authors: Shravan Venkatraman, Ritesh Thawkar, Omkar Thawakar
Published: 2026-06-27
arXiv: 2606.27373
Abstract
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and self-consistency reward schemes in existing self-evolving LMMs optimize answer agreement without ensuring the decoder attends to visual content, relying instead on statistical language priors to produce self-consistent outputs. This leads to a persistent failure mode the authors term visual under-conditioning, where the decoder relies insufficiently on visual tokens.
*Note: The source post's abstract is truncated; the full paper details are available on arXiv.*
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*Auto-collected on 2026-06-27*
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