“The benchmark demands a lot of model's visual reasoning: they must mentally rotate pieces, count coordinates properly, keep track of each piece's starred square, and determine the relationship between different pieces on the board.”

“FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS.”

“State-of-the-art video models achieve only about 20% POC@1.0 and exhibit a significant outcome-hacking.”

“ViReLoc plans routes between two given ground images.”

“SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5.”

“FIGR improves the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME, highlighting the effectiveness of figure-guided multimodal reasoning in enhancing the stability and reliability of complex reasoning.”

“OmniAgent achieves state-of-the-art performance, surpassing leading open-source and proprietary models by substantial margins of 10% - 20% accuracy.”

“ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions.”

“Models excel at extracting explicit text, but struggle with deep chemical logic and precise structural recognition.”

“PathFound integrates pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement.”

“UniMAGE achieves state-of-the-art performance among open-source models, generating logically coherent video scripts and visually consistent keyframe images.”

“REVEALER achieves state-of-the-art performance across four benchmarks and demonstrates superior inference efficiency.”

“Even advanced search-augmented models like GPT-5.1 (w/ Search) achieve only 15.24% accuracy.”

“The Active Cognition-based Reasoning (ACR) module performs human-like perception of the target via a cognitive task chain and actively reasons about contextually relevant objects, thereby extending VLM cognition through a dynamically updated OLT.”

“The paper highlights that VPTracker 'significantly enhances tracking stability and target disambiguation under challenging scenarios, opening a new avenue for integrating MLLMs into visual tracking.'”

“SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks; among open-source models of comparable size, SR-MCR-7B achieves state-of-the-art performance with an average accuracy of 81.4%.”

“Models learn discriminative and interpretable features from as few as one or five training examples.”

“BiPS boosts Qwen2.5-VL-7B by 8.2% on average and shows strong out-of-domain generalization to unseen datasets and image types.”

“iSHIFT matches state-of-the-art performance on multiple benchmark datasets.”

“Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process.”

“Look at GPT image gen capabilities👍🏽 AGI next month?”

“The research focuses on few-shot fine-grained visual categorization.”

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“LogicLens addresses text-centric forgery analysis.”

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“VisRes Bench is a benchmark for evaluating the visual reasoning capabilities of VLMs.”

“To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs.”

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“Cube Bench is a benchmark for spatial visual reasoning in MLLMs.”

“The paper focuses on dynamic spatial understanding, hinting at the consideration of time as a dimension.”

“The research focuses on grounding generative reasoning and situated feedback using structured visualization design knowledge.”

“The research focuses on achieving high-fidelity visual consistency.”

“The article's focus is on vocabulary-free fine-grained recognition.”

“The article likely discusses a method to extend the visual context available to MLLMs.”

“The paper originates from ArXiv, indicating it is likely a pre-print research publication.”

“The research is sourced from ArXiv.”

“CodeDance is a Dynamic Tool-integrated MLLM for Executable Visual Reasoning.”

“The paper focuses on advancing multi-turn reasoning for 'thinking with images'.”

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“R4 likely involves leveraging retrieval-augmented techniques to process and reason about visual information across both spatial and temporal dimensions.”

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“The article's key focus is on 'vision-centric reasoning' and its associated framework.”

“ViRC enhances Visual Interleaved Mathematical CoT with Reason Chunking.”

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“ChartAgent is a chart understanding framework.”

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“JointAVBench is a benchmark for joint audio-visual reasoning evaluation.”

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“The article likely discusses visual faithfulness within the context of 'slow thinking' in AI.”