Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking

• URL: http://arxiv.org/abs/2512.12218v2
• Date: Fri, 19 Dec 2025 08:16:24 GMT
• Title: Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
• Authors: Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy,
• Abstract summary: Reasoning-augmented vision language models generate explicit chains of thought that promise greater capability and transparency.<n>Models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction.<n>We introduce the visual faithfulness of reasoning chains as a distinct evaluation dimension, focusing on whether the perception steps of a reasoning chain are grounded in the image.
• Score: 11.763473690046721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning-augmented vision language models (VLMs) generate explicit chains of thought that promise greater capability and transparency but also introduce new failure modes: models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction. Standard evaluations that only measure final-answer accuracy cannot distinguish these behaviors. We introduce the visual faithfulness of reasoning chains as a distinct evaluation dimension, focusing on whether the perception steps of a reasoning chain are grounded in the image. We propose a training- and reference-free framework that decomposes chains into perception versus reasoning steps and uses off-the-shelf VLM judges for step-level faithfulness, additionally verifying this approach through a human meta-evaluation. Building on this metric, we present a lightweight self-reflection procedure that detects and locally regenerates unfaithful perception steps without any training. Across multiple reasoning-trained VLMs and perception-heavy benchmarks, our method reduces Unfaithful Perception Rate while preserving final-answer accuracy, improving the reliability of multimodal reasoning.

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