Core Viewpoint - The article discusses the transition of Visual Language Models (VLM) from "perception" to "cognition," highlighting the introduction of "Pixel-Space Reasoning" which allows models to interact with visual information directly at the pixel level, enhancing their understanding and reasoning capabilities [1][2][3]. Group 1: Key Developments in VLM - The current mainstream VLMs are limited by their reliance on text tokens, which can lead to loss of critical information in high-resolution images and dynamic video scenes [2][4]. - "Pixel-Space Reasoning" enables models to perform visual operations directly, allowing for a more human-like interaction with visual data [3][6]. - This new reasoning paradigm shifts the focus from text-mediated understanding to native visual operations, enhancing the model's ability to capture spatial relationships and dynamic details [6][7]. Group 2: Overcoming Learning Challenges - The research team identified a "cognitive inertia" challenge where the model's established text reasoning capabilities hinder the development of new pixel operation skills, creating a "learning trap" [8][9]. - To address this, a reinforcement learning framework was designed that combines intrinsic curiosity incentives with extrinsic correctness rewards, encouraging the model to explore visual operations [9][12]. - The framework includes constraints to ensure a minimum rate of pixel-space reasoning and to balance exploration with computational efficiency [10][11]. Group 3: Performance Validation - The Pixel-Reasoner, based on the Qwen2.5-VL-7B model, achieved impressive results across four visual reasoning benchmarks, outperforming models like GPT-4o and Gemini-2.5-Pro [13][19]. - Specifically, it achieved an accuracy of 84.3% on the V* Bench, significantly higher than its competitors [13]. - The model demonstrated a 73.8% accuracy on TallyQA-Complex, showcasing its ability to differentiate between similar objects in images [19][20]. Group 4: Future Implications - The research indicates that pixel-space reasoning is not a replacement for text reasoning but rather a complementary pathway for VLMs, enabling a dual-track understanding of the world [21]. - As multi-modal reasoning capabilities evolve, the industry is moving towards a future where machines can "see more clearly and think more deeply" [21].