Unleashing Perception-Time Scaling to Multimodal Reasoning Models

• URL: http://arxiv.org/abs/2510.08964v1
• Date: Fri, 10 Oct 2025 03:17:52 GMT
• Title: Unleashing Perception-Time Scaling to Multimodal Reasoning Models
• Authors: Yifan Li, Zhenghao Chen, Ziheng Wu, Kun Zhou, Ruipu Luo, Can Zhang, Zhentao He, Yufei Zhan, Wayne Xin Zhao, Minghui Qiu,
• Abstract summary: Recent advances in inference-time scaling have substantially enhanced the reasoning capabilities of Large Vision-Language Models (LVLMs)<n>Inspired by this success, similar strategies have been applied to multimodal reasoning, yet their impact on visual perception remains unclear.<n>We propose Perception-Time Scaling (PTS), a novel paradigm that encourages token-rich perception and decomposes complex perception problems into intermediate tractable sub-problems.
• Score: 60.578179197783754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of Large Vision-Language Models (LVLMs). Inspired by this success, similar strategies have been applied to multimodal reasoning, yet their impact on visual perception remains unclear. To investigate this gap, we introduce DisTANCE, a perception-centric benchmark for visual estimation tasks. Evaluation results show that LVLMs exhibit limited estimation precision, and inference-time scaling offers only marginal gains. We attribute this to the fast perception paradigm of current LVLMs, where visual understanding is treated as a one-shot output without modeling the underlying perceptual process. To address this, we propose Perception-Time Scaling (PTS), a novel paradigm that encourages token-rich perception and decomposes complex perception problems into intermediate tractable sub-problems, thereby enabling perception to align with and benefit from inference-time scaling. Combined with reinforcement learning techniques, PTS significantly improves perception accuracy, raising high-precision performance on DisTANCE from 8.0% to 64.7%, and generalizes well to out-of-domain tasks. Surprisingly, even though PTS data are purely synthetic, combining them with math reasoning data yields consistent gains in both reasoning and real-world perception benchmarks. Further analysis reveals that PTS introduces more perception-related tokens and increases the model's attention to image tokens. Our code and data will be publicly released.

Related papers

• Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models [13.301879353093398]
  We study how reduced large language model (LLM) capacity affects multimodal capabilities.<n>LLM downscaling disproportionately affects visual capabilities, rather than abilities inherited from the LLM.<n>We introduce visual extraction tuning, which explicitly trains the model to extract instruction-relevant visual details consistently across tasks.
  arXiv  Detail & Related papers  (2025-11-21T18:43:01Z)
• Cognitive Inception: Agentic Reasoning against Visual Deceptions by Injecting Skepticism [81.39177645864757]
  We propose textbfInception, a fully reasoning-based agentic reasoning framework to conduct authenticity verification by injecting skepticism.<n>To the best of our knowledge, this is the first fully reasoning-based framework against AIGC visual deceptions.
  arXiv  Detail & Related papers  (2025-11-21T05:13:30Z)
• Spotlight on Token Perception for Multimodal Reinforcement Learning [65.97597482517425]
  Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs)<n>In this paper, we undertake a pioneering exploration of multimodal RLVR through the novel perspective of token perception.<n>We propose Visually-Perceptive Policy Optimization (VPPO), a novel policy gradient algorithm that explicitly leverages token perception to refine the learning signal.
  arXiv  Detail & Related papers  (2025-10-10T11:25:33Z)
• Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models [63.69856480318313]
  AGILE formulates jigsaw solving as an interactive process, enabling the model to progressively engage with the environment.<n>We show that AGILE substantially boosts performance on jigsaw tasks of varying complexity.<n>We also demonstrate strong generalization across 9 general vision tasks, achieving an average improvement of 3.1%.
  arXiv  Detail & Related papers  (2025-10-01T17:58:05Z)
• VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception [50.446538409259524]
  Visual Test-Time Scaling (VTTS) is a novel approach to enhance MLLMs' reasoning via iterative inference during inference.<n>VTTS mimics humans' attention by focusing on high-confidence hierarchical-temporal regions, guided by updated textual predictions.<n>Our newly introduced Videochat-R1.5 model has achieved remarkable improvements, with an average increase of over 5%.
  arXiv  Detail & Related papers  (2025-09-25T12:46:46Z)
• Visual hallucination detection in large vision-language models via evidential conflict [24.465497252040294]
  Dempster-Shafer theory (DST)-based visual hallucination detection method for LVLMs through uncertainty estimation.<n>We propose to the best of our knowledge, the first Dempster-Shafer theory (DST)-based visual hallucination detection method for LVLMs through uncertainty estimation.
  arXiv  Detail & Related papers  (2025-06-24T11:03:10Z)
• Caption This, Reason That: VLMs Caught in the Middle [3.4820139118440676]
  Vision-Language Models (VLMs) have shown remarkable progress in visual understanding in recent years.<n>They still lag behind human capabilities in specific visual tasks such as counting or relational reasoning.<n>We analyze VLM performance along core cognitive axes: Perception, Attention, and Memory.
  arXiv  Detail & Related papers  (2025-05-24T14:25:48Z)
• Next Token Perception Score: Analytical Assessment of your LLM Perception Skills [12.093755170926762]
  Next Token Perception Score (NTPS) is a score derived under a linear setting that measures the overlap between autoregressive and perception feature subspaces.<n>We show that NTPS increases following low-rank adaptation (LoRA) fine-tuning, especially in large models.<n>Our results offer both theoretical insights and practical tools for analytically assessing perception skills.
  arXiv  Detail & Related papers  (2025-05-22T17:18:51Z)
• DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual Grounding [61.26026947423187]
  Human experts excel at fine-grained visual discrimination by leveraging domain knowledge to refine perceptual features.<n>Current Multimodal Large Language Models (MLLMs) struggle to integrate reasoning into visual perception.<n>We propose DeepPerception, an MLLM enhanced with cognitive visual perception capabilities.
  arXiv  Detail & Related papers  (2025-03-17T04:06:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.