Perceptual Flow Network for Visually Grounded Reasoning

2026-05-04

AI Summary

This work addresses the limitations of current large vision-language models, which suffer from language bias and hallucination due to optimization objectives that inadequately constrain visual grounding, and rely on geometric priors that prioritize precision over reasoning utility. To overcome these issues, the authors propose Perception Flow Network (PFlowNet), a framework that decouples perception and reasoning to establish a self-conditioned generation process. PFlowNet eschews rigid alignment with expert priors and instead employs variational reinforcement learning to drive perception toward reasoning goals. It further integrates a multi-dimensional reward mechanism with neighborhood geometric shaping to enhance both interpretability and reasoning efficiency. The model achieves state-of-the-art performance at the time of evaluation, attaining 90.6% on V* Bench and 67.0% on MME-RealWorld-lite.

Abstract

Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories, leading to language bias and hallucination. To mitigate this, current methods introduce geometric priors from visual experts as additional supervision. However, we observe that such supervision is typically suboptimal: it is biased toward geometric precision and offers limited reasoning utility. To bridge this gap, we propose Perceptual Flow Network (PFlowNet), which eschews rigid alignment with the expert priors and achieves interpretable yet more effective visual reasoning. Specifically, PFlowNet decouples perception from reasoning to establish a self-conditioned generation process. Based on this, it integrates multi-dimensional rewards with vicinal geometric shaping via variational reinforcement learning, thereby facilitating reasoning-oriented perceptual behaviors while preserving visual reliability. PFlowNet delivers a provable performance guarantee and competitive empirical results, particularly setting new SOTA records on V* Bench (90.6%) and MME-RealWorld-lite (67.0%).

Problem

• visually grounded reasoning
• language bias
• hallucination
• geometric priors
• visual reasoning

Innovation

• Perceptual Flow Network
• visually grounded reasoning
• variational reinforcement learning
• decoupled perception-reasoning
• geometric shaping