BLINK-Twice: You see, but do you observe? A Reasoning Benchmark on Visual Perception

• URL: http://arxiv.org/abs/2510.09361v1
• Date: Fri, 10 Oct 2025 13:14:13 GMT
• Title: BLINK-Twice: You see, but do you observe? A Reasoning Benchmark on Visual Perception
• Authors: Junyan Ye, Dongzhi Jiang, Jun He, Baichuan Zhou, Zilong Huang, Zhiyuan Yan, Hongsheng Li, Conghui He, Weijia Li,
• Abstract summary: We introduce BLINK-Twice, a vision-centric reasoning benchmark grounded in challenging perceptual tasks.<n>Instead of relying on external knowledge, our tasks require models to reason from visual content alone.<n>Compared to prior perception benchmarks, it moves beyond shallow perception and requires fine-grained observation and analytical reasoning.
• Score: 67.89135437537179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Multimodal Large Language Models (MLLMs) have made rapid progress, particularly in enhancing their reasoning capabilities. However, existing reasoning benchmarks still primarily assess language-based reasoning, often treating visual input as replaceable context. To address this gap, we introduce BLINK-Twice, a vision-centric reasoning benchmark grounded in challenging perceptual tasks. Instead of relying on external knowledge, our tasks require models to reason from visual content alone, shifting the focus from language-based to image-grounded reasoning. Compared to prior perception benchmarks, it moves beyond shallow perception ("see") and requires fine-grained observation and analytical reasoning ("observe"). BLINK-Twice integrates three core components: seven types of visual challenges for testing visual reasoning, natural adversarial image pairs that enforce reliance on visual content, and annotated reasoning chains for fine-grained evaluation of the reasoning process rather than final answers alone. We evaluate 20 leading MLLMs, including 12 foundation models and 8 reasoning-enhanced models. BLINK-Twice poses a significant challenge to current models. While existing reasoning strategies in the language space-such as chain-of-thought or self-criticism can improve performance, they often result in unstable and redundant reasoning. We observe that repeated image observation improves performance across models, and active visual interaction, as demonstrated by models like o3, highlights the need for a new paradigm for vision reasoning. The dataset is publicly available at https://github.com/PicoTrex/BLINK-Twice

Related papers

• Video-R2: Reinforcing Consistent and Grounded Reasoning in Multimodal Language Models [56.851611990473174]
  Reasoning over dynamic visual content remains a central challenge for large language models.<n>We propose a reinforcement learning approach that enhances both temporal precision and reasoning consistency.<n>The resulting model, Video R2, achieves consistently higher TAC, VAS, and accuracy across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-11-28T18:59:58Z)
• ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation [79.17352367219736]
  ROVER tests the use of one modality to guide, verify, or refine outputs in the other.<n>ROVER is a human-annotated benchmark that explicitly targets reciprocal cross-modal reasoning.
  arXiv  Detail & Related papers  (2025-11-03T02:27:46Z)
• Self-Rewarding Vision-Language Model via Reasoning Decomposition [49.784411666601905]
  Vision-Language Models (VLMs) often suffer from visual hallucinations, saying things that are not actually in the image, and language shortcuts.<n>We introduce Vision-SR1, a self-rewarding method that improves visual reasoning without relying on external visual supervisions.<n>Our experiments demonstrate that Vision-SR1 improves visual reasoning, mitigates visual hallucinations, and reduces reliance on language shortcuts.
  arXiv  Detail & Related papers  (2025-08-27T08:01:03Z)
• VLMs have Tunnel Vision: Evaluating Nonlocal Visual Reasoning in Leading VLMs [18.349695067647012]
  Visual Language Models excel at complex visual tasks such as VQA and chart understanding, yet recent work suggests they struggle with simple tests.<n>We present an evaluation that tests vision-language models' capacity for nonlocal visual reasoning.<n>Our findings show that despite gains in raw visual acuity, current models lack core visual reasoning capabilities.
  arXiv  Detail & Related papers  (2025-07-04T23:15:52Z)
• Seeing is Not Reasoning: MVPBench for Graph-based Evaluation of Multi-path Visual Physical CoT [24.085953089267772]
  We show how OpenAI o3 and GPT-4o fail to grasp basic physical laws, spatial interactions, and causal effects in complex scenes.<n>We introduce MVPBench, a benchmark designed to rigorously evaluate visual physical reasoning through the lens of visual chain-of-thought (CoT)<n> Experimental results reveal a concerning trend: even cutting-edge MLLMs exhibit poor visual reasoning accuracy and weak image-text alignment in physical domains.
  arXiv  Detail & Related papers  (2025-05-30T03:48:59Z)
• v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning [27.688428439248607]
  We introduce v1, a lightweight extension that enables active visual referencing through a simple point-and-copy approach.<n>This allows the model to identify relevant image patches and copy their embeddings back into the reasoning stream.<n>Our pointing strategy lets the MLLM directly select image patches using their semantic representations as keys, keeping perceptual evidence embedded in the same space as the model's reasoning.
  arXiv  Detail & Related papers  (2025-05-24T19:30:47Z)
• Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps [56.76175383189738]
  We introduce ReasonMap, a benchmark designed to assess the fine-grained visual understanding and spatial reasoning abilities of MLLMs.<n>ReasonMap encompasses high-resolution transit maps from 30 cities across 13 countries and includes 1,008 question-answer pairs spanning two question types and three templates.<n> Comprehensive evaluations of 15 popular MLLMs, including both base and reasoning variants, reveal a counterintuitive pattern.
  arXiv  Detail & Related papers  (2025-05-24T12:33:52Z)
• Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning [53.790502697674754]
  We propose Take-along Visual Conditioning (TVC), a strategy that shifts image input to critical reasoning stages.<n>TVC helps the model retain attention to the visual components throughout the reasoning.<n>Our approach achieves state-of-the-art performance on average across five mathematical reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-03-17T16:45:12Z)
• Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios [69.00444996464662]
  We propose RIV-CoT, a Retrieval-Based Interleaved Visual Chain-of-Thought method that enables vision-language models to reason using visual crops corresponding to relevant entities.<n>Our experiments demonstrate that RIV-CoT improves answer accuracy by 3.1% and reasoning accuracy by 4.6% over vanilla CoT prompting.
  arXiv  Detail & Related papers  (2025-01-08T18:31:16Z)

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.