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 ...

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 ...

数据来源：ArXiv Domain LLM Domain Papers1. Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning ModelsMeta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available (“few-shot” learning). Increased task diversity is often believed to enhance meta-learning by providing richer information across tasks. However, recent work by Kumar et al. (2022) shows t ...

数据来源：ArXiv Domain LLM Domain Papers1. VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-ReflectionWe present VDC-Agent, a self-evolving framework for Video Detailed Captioning that requires neither human annotations nor larger teacher models. The agent forms a closed loop of caption generation, principle-guided scoring (score and textual suggestions), and prompt refinement. When caption quality regresses, a self-reflection path leverages the previous chain-of-thought ...

数据来源：ArXiv Domain LLM Domain Papers1. EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion DetectionSpeech emotion recognition (SER) systems are constrained by existing datasets that typically cover only 6-10 basic emotions, lack scale and diversity, and face ethical challenges when collecting sensitive emotional states. We introduce EMONET-VOICE, a comprehensive resource addressing these limitations through two components: (1) EmoNet-Voice Big, a 5,000-hour multilingual ...

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 ...

数据来源：ArXiv Domain LLM Domain Papers1. Automated Semantic Rules Detection (ASRD) for Emergent Communication InterpretationThe field of emergent communication within multi-agent systems examines how autonomous agents can independently develop communication strategies, without explicit programming, and adapt them to varied environments. However, few studies have focused on the interpretability of emergent languages. The research exposed in this paper proposes an Automated Semantic Rules Detection ...

数据来源：ArXiv Domain LLM Domain Papers1. Optimal Lower Bounds for Online MulticalibrationWe prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner’s predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibration error using just three disjoint binary groups. This matches the upper bounds of Noarov et al. (2025) up to log ...

数据来源：ArXiv Domain LLM Domain Papers1. Optimal Lower Bounds for Online MulticalibrationWe prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner’s predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibration error using just three disjoint binary groups. This matches the upper bounds of Noarov et al. (2025) up to log ...

数据来源：ArXiv Domain LLM Domain Papers1. Optimal Lower Bounds for Online MulticalibrationWe prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner’s predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibration error using just three disjoint binary groups. This matches the upper bounds of Noarov et al. (2025) up to log ...

数据来源：ArXiv Domain LLM Domain Papers1. Manifold limit for the training of shallow graph convolutional neural networksWe study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates that of the Laplace-Beltrami operator of the underlying smooth manifold, and shallow ...

数据来源：ArXiv Domain LLM Domain Papers1. MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-HeadWhile the Transformer architecture dominates many fields, its quadratic self-attention complexity hinders its use in large-scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re-introducing computational overhead through extra modules (e.g., depthwise separable convolution) that de ...

数据来源：ArXiv Domain LLM Domain Papers1. Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review SystemIn this work, we explore the Large Language Model (LLM) agent reviewer dynamics in an Elo-ranked review system using real-world conference paper submissions. Multiple LLM agent reviewers with different personas are engage in multi round review interactions moderated by an Area Chair. We compare a baseline setting with conditions that incorporate Elo ratings and reviewer memory. Our simulation ...

数据来源：ArXiv Domain LLM Domain Papers1. Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent PlanningVision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning fra ...

数据来源：ArXiv Domain LLM Domain Papers1. MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite MatchingTool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing reinforcement learning methods typically rely on outcome- or trajectory-level rewards, assigning uniform advantages to all steps within a trajectory. This coarse-grained credit assignment fails to ...