Perceptual Flow Network for Visually Grounded Reasoning
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| Authors | Yangfu Li et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2605.02730 |
| Download | |
| HF Page | View on Hugging Face |
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%).
Engineering Breakdown
Plain English
This paper proposes Perceptual Flow Network (PFlowNet), a new training approach for vision-language models that addresses hallucination and language bias by decoupling perception from reasoning rather than forcing alignment with geometric priors. The key finding is that rigid geometric supervision from visual experts actually hurts reasoning quality, and a self-conditioned generation process with multi-dimensional rewards produces better visual grounding with fewer false outputs.
Key Engineering Insight
Separating the perception step from the reasoning step in vision-language models is more effective than the current industry standard of adding geometric constraints as auxiliary losses. This suggests the optimization landscape is fundamentally different when you let the model learn what to perceive versus how to reason about it.
Why It Matters for Engineers
Vision-language hallucination (generating plausible-sounding text that doesn't match the image) is a critical production problem. This work suggests that common mitigation strategies—adding geometric loss terms—may actually be counterproductive, pointing toward a different architectural approach that could significantly reduce hallucination in deployed systems without expensive external vision experts.
Research Context
Prior work bolted on geometric priors from vision experts as auxiliary supervision to constrain LVLMs toward visual faithfulness. PFlowNet reframes this as a fundamental design choice: instead of post-hoc constraints, it architecturally separates perception from reasoning, enabling self-conditioned generation. This advances beyond treating hallucination as a loss-function problem and positions it as an architectural one.
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