数据来源：ArXiv Domain LLM Domain Papers1. ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM InferenceWeight-only post-training quantization (PTQ) compresses the weights of Large Language Models (LLMs) into low-precision representations to reduce memory footprint and accelerate inference. However, the presence of outliers in weights and activations often leads to large quantization errors and severe accuracy degradation, especially in recent reasoning LLMs where errors accumulat ...

数据来源：ArXiv Domain LLM Domain Papers1. ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM InferenceWeight-only post-training quantization (PTQ) compresses the weights of Large Language Models (LLMs) into low-precision representations to reduce memory footprint and accelerate inference. However, the presence of outliers in weights and activations often leads to large quantization errors and severe accuracy degradation, especially in recent reasoning LLMs where errors accumulat ...

数据来源：ArXiv Domain LLM Domain Papers1. Optimizing Mixture of Block AttentionMixture of Block Attention (MoBA) (Lu et al., 2025) is a promising building block for efficiently processing long contexts in LLMs by enabling queries to sparsely attend to a small subset of key-value blocks, drastically reducing computational cost. However, the design principles governing MoBA’s performance are poorly understood, and it lacks an efficient GPU implementation, hindering its practical adoption. In this pa ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Spatial Intelligence with Multimodal Foundation ModelsDespite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and g ...

数据来源：ArXiv Domain LLM Domain Papers1. ARC Is a Vision Problem!The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although the puzzle-like tasks in ARC are inherently visual, existing research has rarely approached the problem from a vision-centric perspective ...

数据来源：ArXiv Domain LLM Domain Papers1. Dataset Distillation for Pre-Trained Self-Supervised Vision ModelsThe task of dataset distillation aims to find a small set of synthetic images such that training a model on them reproduces the performance of the same model trained on a much larger dataset of real samples. Existing distillation methods focus on synthesizing datasets that enable training randomly initialized models. In contrast, state-of-the-art vision approaches are increasingly building o ...

数据来源：ArXiv Domain LLM Domain Papers1. Dataset Distillation for Pre-Trained Self-Supervised Vision ModelsThe task of dataset distillation aims to find a small set of synthetic images such that training a model on them reproduces the performance of the same model trained on a much larger dataset of real samples. Existing distillation methods focus on synthesizing datasets that enable training randomly initialized models. In contrast, state-of-the-art vision approaches are increasingly building o ...

数据来源：ArXiv Domain LLM Domain Papers1. Dataset Distillation for Pre-Trained Self-Supervised Vision ModelsThe task of dataset distillation aims to find a small set of synthetic images such that training a model on them reproduces the performance of the same model trained on a much larger dataset of real samples. Existing distillation methods focus on synthesizing datasets that enable training randomly initialized models. In contrast, state-of-the-art vision approaches are increasingly building o ...

数据来源：ArXiv Domain LLM Domain Papers1. The Loss of Control Playbook: Degrees, Dynamics, and PreparednessThis research report addresses the absence of an actionable definition for Loss of Control (LoC) in AI systems by developing a novel taxonomy and preparedness framework. Despite increasing policy and research attention, existing LoC definitions vary significantly in scope and timeline, hindering effective LoC assessment and mitigation. To address this issue, we draw from an extensive literatu ...

数据来源：ArXiv Domain LLM Domain Papers1. MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging ModalitiesTraditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detection (OVOD) addresses this limitation but remains underexplored in medical imaging due to dataset scarcity and weak text-image alignment. To bridge this gap, we introduce MedROV, the first ...

数据来源：ArXiv Domain LLM Domain Papers1. Revisiting Generalization Across Difficulty Levels: It’s Not So EasyWe investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLM ...

数据来源：ArXiv Domain LLM Domain Papers1. Revisiting Generalization Across Difficulty Levels: It’s Not So EasyWe investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLM ...

数据来源：ArXiv Domain LLM Domain Papers1. Revisiting Generalization Across Difficulty Levels: It’s Not So EasyWe investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLM ...

数据来源：ArXiv Domain LLM Domain Papers1. Training AI Co-Scientists Using Rubric RewardsAI co-scientists are emerging as a tool to assist human researchers in achieving their research goals. A crucial feature of these AI co-scientists is the ability to generate a research plan given a set of aims and constraints. The plan may be used by researchers for brainstorming, or may even be implemented after further refinement. However, language models currently struggle to generate research plans that fol ...

数据来源：ArXiv Domain LLM Domain Papers1. SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and TimeWe present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time. To achieve this, we introduce an e ...