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Slow Perception: Let's Perceive Geometric Figures Step-by-step

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arxiv 2412.20631 v2 pith:UQLRFPPT submitted 2024-12-30 cs.CV

classification cs.CV
keywords perceptiongeometricvisualcomplexlinemodelslowaccurately
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly understand the complex inherent logic and spatial relationships within geometric shapes. We believe accurate copying (strong perception) is the first step to visual o1. Accordingly, we introduce the concept of "slow perception" (SP), which guides the model to gradually perceive basic point-line combinations, as our humans, reconstruct complex geometric structures progressively. There are two-fold stages in SP: a) perception decomposition. Perception is not instantaneous. In this stage, complex geometric figures are broken down into basic simple units to unify geometry representation. b) perception flow, which acknowledges that accurately tracing a line is not an easy task. This stage aims to avoid "long visual jumps" in regressing line segments by using a proposed "perceptual ruler" to trace each line stroke-by-stroke. Surprisingly, such a human-like perception manner enjoys an inference time scaling law -- the slower, the better. Researchers strive to speed up the model's perception in the past, but we slow it down again, allowing the model to read the image step-by-step and carefully.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MentalThink: Shaping Thoughts in Mental SVG World

    cs.AI 2026-07 conditional novelty 7.0 of 10

    MLLMs that generate and render SVG sketches as multi-turn intermediate reasoning steps reach 55.1% on VSIBench and 76.0% on MindCube, far above the Qwen2.5-VL-7B backbone.

  2. AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A stochastic scheduling framework that modulates slow-to-fast reasoning in large reasoning models at test time, improving accuracy while reducing token usage.

  3. RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios

    cs.CV 2025-11 conditional novelty 5.0 of 10

    A new 9,121-case benchmark of road-marking tasks shows most multimodal LLMs perform near or below simple rule-based baselines in fine-grained urban spatial reasoning.

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