BiPS — Bi-directional Perceptual Shaping for Multimodal Reasoning

This model card describes BiPS (Bi-directional Perceptual Shaping), a training-time framework proposed in “See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning” [CVPR 2026].

• Paper: https://arxiv.org/abs/2512.22120
• Code: https://github.com/zss02/BiPS

What is BiPS?

Many VLMs fail on multimodal reasoning because they look at the wrong visual evidence (especially for charts, thin lines, intersections, and small regions). BiPS improves question-conditioned visual grounding by turning “where-to-look” supervision into training signals—without requiring extra tools at inference time.

Key idea

BiPS trains a VLM with two complementary view transformations:

• Evidence-Preserving View: keep only the visual evidence needed to answer, reduce distractions.
  → enforce consistency between predictions from the original image and the preserved view.

• Evidence-Ablated View: remove the key evidence so the image no longer supports the answer.
  → enforce separation so the model cannot rely on shortcuts.

These constraints are typically implemented with KL-based objectives and can be integrated into GRPO training.

Why it matters

• Better fine-grained evidence alignment
• Less “guessing” from language priors
• No additional inference overhead (views are used only during training)

How to use

BiPS is mainly a training recipe. To apply it:

1. Follow the official repo to set up dependencies and scripts.
2. Train your base VLM with BiPS-generated preserve/ablate views.
3. Use the resulting checkpoint as a standard VLM at inference time (no extra steps).

Citation

@article{zhang2025bips,
  title={See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning},
  author={Zhang, Shuoshuo and Zhang, Yizhen and Fu, Jingjing and Song, Lei and Bian, Jiang and Yang, Yujiu and Wang, Rui},
  journal={arXiv preprint arXiv:2512.22120},
  year={2025}
}