数据来源：HuggingFace Papers Latest Papers1. Pixels, Patterns, but No Poetry: To See The World like HumansAchieving human-like perception and reasoning in Multimodal Large Language Models (MLLMs) remains a central challenge in artificial intelligence. While recent research has primarily focused on enhancing reasoning capabilities in MLLMs, a fundamental question persists: Can Multimodal Large Language Models truly perceive the world as humans do? This paper shifts focus from reasoning to perception ...

数据来源：HuggingFace Papers Latest Papers1. Beyond Context Limits: Subconscious Threads for Long-Horizon ReasoningTo break the context limits of large language models (LLMs) that bottleneck reasoning accuracy and efficiency, we propose the Thread Inference Model (TIM), a family of LLMs trained for recursive and decompositional problem solving, and TIMRUN, an inference runtime enabling long-horizon structured reasoning beyond context limits. Together, TIM hosted on TIMRUN supports virtually unlimit ...

数据来源：HuggingFace Papers Latest Papers1. The Geometry of LLM Quantization: GPTQ as Babai’s Nearest Plane AlgorithmQuantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto more affordable accelerators. GPTQ emerged as one of the standard methods for one-shot post-training quantization at LLM scale. Yet, its inner workings are described as a sequence of ad-hoc algebraic updates that obscure any geometric mean ...

数据来源：HuggingFace Papers Latest Papers1. MOVE: Motion-Guided Few-Shot Video Object SegmentationThis work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion unde ...

数据来源：HuggingFace Papers Latest Papers1. Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual SegmentationReferring audio-visual segmentation (RAVS) has recently seen significant advancements, yet challenges remain in integrating multimodal information and deeply understanding and reasoning about audiovisual content. To extend the boundaries of RAVS and facilitate future research in this field, we propose Omnimodal Referring Audio-Visual Segmentation (OmniAVS), a new dataset co ...

数据来源：HuggingFace Papers Latest Papers1. Deep Researcher with Test-Time DiffusionDeep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time scaling algorithms. Drawing inspiration from the iterative nature of human research, which involves cycles of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep Researcher (TTD-DR). This novel ...

数据来源：HuggingFace Papers Latest Papers1. Seed-Prover: Deep and Broad Reasoning for Automated Theorem ProvingLLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective train ...

数据来源：HuggingFace Papers Latest Papers1. Seed-Prover: Deep and Broad Reasoning for Automated Theorem ProvingLLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective train ...

数据来源：HuggingFace Papers Latest Papers1. Seed-Prover: Deep and Broad Reasoning for Automated Theorem ProvingLLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective train ...

数据来源：HuggingFace Papers Latest Papers1. Seed-Prover: Deep and Broad Reasoning for Automated Theorem ProvingLLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving due to the lack of clear supervision signals when solely using natural language. Dedicated domain-specific languages like Lean provide clear supervision via formal verification of proofs, enabling effective train ...

数据来源：HuggingFace Papers Latest Papers1. On the Generalization of SFT: A Reinforcement Learning Perspective with Reward RectificationWe present a simple yet theoretically motivated improvement to Supervised Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited generalization compared to reinforcement learning (RL). Through mathematical analysis, we reveal that standard SFT gradients implicitly encode a problematic reward structure that may severely restrict the generaliza ...

数据来源：HuggingFace Papers Latest Papers1. Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed InferenceWe present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel generation, discrete diffusion models provide a notable speedup to mitigate the inherent latency of token-by-token decoding, as demonstrated recently (e.g., Mercury Coder, Gemini Diffusion). Seed Diffus ...

数据来源：HuggingFace Papers Latest Papers1. Beyond Fixed: Variable-Length Denoising for Diffusion Large Language ModelsDiffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length. This static length all ...

数据来源：HuggingFace Papers Latest Papers1. Qwen-Image Technical ReportWe present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to ...

数据来源：HuggingFace Papers Latest Papers1. ReasonRank: Empowering Passage Ranking with Strong Reasoning AbilityLarge Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large Reasoning Models, many studies have demonstrated that step-by-step reasoning during test-time helps improve listwise ranking performance. However, due to the scarcity of reasoning-intensive training data, existing rerankers perform poorly in many c ...