Orionnnn/hub-stats huggingface dataset:
OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification
Authors: name: Zijian Wu, name: Lingkai Kong, name: Wenwei Zhang, name: Songyang Gao, name: Yuzhe Gu, name: Zhongrui Cai, name: Tianyou Ma, name: Yuhong Liu, name: Zhi Wang, name: Runyuan Ma, name: Guangyu Wang, name: Wei Li, name: Conghui He, name: Dahua Lin, name: Kai Chen
Published: 2025-12-11 15:47:38

Large language models (LLMs) have achieved significant progress in solving complex reasoning tasks by Reinforcement Learning with Verifiable Rewards (RLVR). This advancement is also inseparable from the oversight automated by reliable verifiers. However, current outcome-based verifiers (OVs) are unable to inspect the unreliable intermediate steps in the long reasoning chains of thought (CoTs). Meanwhile, current process-based verifiers (PVs) have difficulties in reliably detecting errors in the complex long CoTs, limited by the scarcity of high-quality annotations due to the prohibitive costs of human annotations. Therefore, we propose the Outcome-based Process Verifier (OPV), which verifies the rationale process of summarized outcomes from long CoTs to achieve both accurate and efficient verification and enable large-scale annotation. To empower the proposed verifier, we adopt an iterative active learning framework with expert annotations to progressively improve the verification capability of OPV with fewer annotation costs. Specifically, in each iteration, the most uncertain cases of the current best OPV are annotated and then subsequently used to train a new OPV through Rejection Fine-Tuning (RFT) and RLVR for the next round. Extensive experiments demonstrate OPV's superior performance and broad applicability. It achieves new state-of-the-art results on our held-out OPV-Bench, outperforming much larger open-source models such as Qwen3-Max-Preview with an F1 score of 83.1 compared to 76.3. Furthermore, OPV effectively detects false positives within synthetic dataset, closely align with expert assessment. When collaborating with policy models, OPV consistently yields performance gains, e.g., raising the accuracy of DeepSeek-R1-Distill-Qwen-32B from 55.2% to 73.3% on AIME2025 as the compute budget scales.
Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task
Authors: name: Sunqi Fan, name: Jiashuo Cui, name: Meng-Hao Guo, name: Shuojin Yang
Published: 2025-12-11 07:17:57

Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios. However, existing Multimodal Large Language Models (MLLMs) struggle with simultaneously modeling spatial relationships within video frames and understanding the causal dynamics of temporal evolution on complex and reasoning-intensive VideoQA task. In this work, we equip MLLM with a comprehensive and extensible Video Toolkit, to enhance MLLM's spatiotemporal reasoning capabilities and ensure the harmony between the quantity and diversity of tools. To better control the tool invocation sequence and avoid toolchain shortcut issues, we propose a Spatiotemporal Reasoning Framework (STAR) that strategically schedules temporal and spatial tools, thereby progressively localizing the key area in the video. Our STAR framework enhances GPT-4o using lightweight tools, achieving an 8.2% gain on VideoMME and 4.6% on LongVideoBench. We believe that our proposed Video Toolkit and STAR framework make an important step towards building autonomous and intelligent video analysis assistants. The code is publicly available at https://github.com/fansunqi/VideoTool.
Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation
Authors: name: Yiwen Tang, name: Zoey Guo, name: Kaixin Zhu, name: Ray Zhang, name: Qizhi Chen, name: Dongzhi Jiang, name: Junli Liu, name: Bohan Zeng, name: Haoming Song, name: Delin Qu, name: Tianyi Bai, name: Dan Xu, name: Wentao Zhang, name: Bin Zhao
Published: 2025-12-11 18:59:52

Reinforcement learning (RL), earlier proven to be effective in large language and multi-modal models, has been successfully extended to enhance 2D image generation recently. However, applying RL to 3D generation remains largely unexplored due to the higher spatial complexity of 3D objects, which require globally consistent geometry and fine-grained local textures. This makes 3D generation significantly sensitive to reward designs and RL algorithms. To address these challenges, we conduct the first systematic study of RL for text-to-3D autoregressive generation across several dimensions. (1) Reward designs: We evaluate reward dimensions and model choices, showing that alignment with human preference is crucial, and that general multi-modal models provide robust signal for 3D attributes. (2) RL algorithms: We study GRPO variants, highlighting the effectiveness of token-level optimization, and further investigate the scaling of training data and iterations. (3) Text-to-3D Benchmarks: Since existing benchmarks fail to measure implicit reasoning abilities in 3D generation models, we introduce MME-3DR. (4) Advanced RL paradigms: Motivated by the natural hierarchy of 3D generation, we propose Hi-GRPO, which optimizes the global-to-local hierarchical 3D generation through dedicated reward ensembles. Based on these insights, we develop AR3D-R1, the first RL-enhanced text-to-3D model, expert from coarse shape to texture refinement. We hope this study provides insights into RL-driven reasoning for 3D generation. Code is released at https://github.com/Ivan-Tang-3D/3DGen-R1.
MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
Authors: name: Kehong Gong, name: Zhengyu Wen, name: Weixia He, name: Mingxi Xu, name: Qi Wang, name: Ning Zhang, name: Zhengyu Li, name: Dongze Lian, name: Wei Zhao, name: Xiaoyu He, name: Mingyuan Zhang
Published: 2025-12-11 18:09:48

Motion capture now underpins content creation far beyond digital humans, yet most existing pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation such as BVH that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware inverse kinematics. The system contains three learnable modules and a lightweight IK stage: (1) a Reference Prompt Encoder that extracts per-joint queries from the asset's skeleton, mesh, and rendered images; (2) a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the gap between video and joint space; and (3) a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1038 motion clips, each providing a standardized skeleton-mesh-render triad. Experiments on both in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits meaningful cross-species retargeting across heterogeneous rigs, enabling scalable, prompt-driven 3D motion capture for arbitrary assets. Project page: https://animotionlab.github.io/MoCapAnything/
Stronger Normalization-Free Transformers
Authors: name: Mingzhi Chen, name: Taiming Lu, name: Jiachen Zhu, name: Mingjie Sun, name: Zhuang Liu
Published: 2025-12-11 18:58:49

Although normalization layers have long been viewed as indispensable components of deep learning architectures, the recent introduction of Dynamic Tanh (DyT) has demonstrated that alternatives are possible. The point-wise function DyT constrains extreme values for stable convergence and reaches normalization-level performance; this work seeks further for function designs that can surpass it. We first study how the intrinsic properties of point-wise functions influence training and performance. Building on these findings, we conduct a large-scale search for a more effective function design. Through this exploration, we introduce Derf(x) = erf(αx + s), where erf(x) is the rescaled Gaussian cumulative distribution function, and identify it as the most performant design. Derf outperforms LayerNorm, RMSNorm, and DyT across a wide range of domains, including vision (image recognition and generation), speech representation, and DNA sequence modeling. Our findings suggest that the performance gains of Derf largely stem from its improved generalization rather than stronger fitting capacity. Its simplicity and stronger performance make Derf a practical choice for normalization-free Transformer architectures.
Confucius Code Agent: An Open-sourced AI Software Engineer at Industrial Scale
Authors: name: Zhaodong Wang, name: Zhenting Qi, name: Sherman Wong, name: Nathan Hu, name: Samuel Lin, name: Jun Ge, name: Erwin Gao, name: Yining Yang, name: Ben Maurer, name: Wenlin Chen, name: David Recordon, name: Yilun Du, name: Minlan Yu, name: Ying Zhang
Published: 2025-12-11 08:05:58

Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.
Evaluating Gemini Robotics Policies in a Veo World Simulator
Authors: name: Gemini Robotics Team, name: Coline Devin, name: Yilun Du, name: Debidatta Dwibedi, name: Ruiqi Gao, name: Abhishek Jindal, name: Thomas Kipf, name: Sean Kirmani, name: Fangchen Liu, name: Anirudha Majumdar, name: Andrew Marmon, name: Carolina Parada, name: Yulia Rubanova, name: Dhruv Shah, name: Vikas Sindhwani, name: Jie Tan, name: Fei Xia, name: Ted Xiao, name: Sherry Yang, name: Wenhao Yu, name: Allan Zhou
Published: 2025-12-11 14:22:14

Generative world models hold significant potential for simulating interactions with visuomotor policies in varied environments. Frontier video models can enable generation of realistic observations and environment interactions in a scalable and general manner. However, the use of video models in robotics has been limited primarily to in-distribution evaluations, i.e., scenarios that are similar to ones used to train the policy or fine-tune the base video model. In this report, we demonstrate that video models can be used for the entire spectrum of policy evaluation use cases in robotics: from assessing nominal performance to out-of-distribution (OOD) generalization, and probing physical and semantic safety. We introduce a generative evaluation system built upon a frontier video foundation model (Veo). The system is optimized to support robot action conditioning and multi-view consistency, while integrating generative image-editing and multi-view completion to synthesize realistic variations of real-world scenes along multiple axes of generalization. We demonstrate that the system preserves the base capabilities of the video model to enable accurate simulation of scenes that have been edited to include novel interaction objects, novel visual backgrounds, and novel distractor objects. This fidelity enables accurately predicting the relative performance of different policies in both nominal and OOD conditions, determining the relative impact of different axes of generalization on policy performance, and performing red teaming of policies to expose behaviors that violate physical or semantic safety constraints. We validate these capabilities through 1600+ real-world evaluations of eight Gemini Robotics policy checkpoints and five tasks for a bimanual manipulator.
The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
Authors: name: Aileen Cheng, name: Alon Jacovi, name: Amir Globerson, name: Ben Golan, name: Charles Kwong, name: Chris Alberti, name: Connie Tao, name: Eyal Ben-David, name: Gaurav Singh Tomar, name: Lukas Haas, name: Yonatan Bitton, name: Adam Bloniarz, name: Aijun Bai, name: Andrew Wang, name: Anfal Siddiqui, name: Arturo Bajuelos Castillo, name: Aviel Atias, name: Chang Liu, name: Corey Fry, name: Daniel Balle, name: Deepanway Ghosal, name: Doron Kukliansky, name: Dror Marcus, name: Elena Gribovskaya, name: Eran Ofek, name: Honglei Zhuang, name: Itay Laish, name: Jan Ackermann, name: Lily Wang, name: Meg Risdal, name: Megan Barnes, name: Michael Fink, name: Mohamed Amin, name: Moran Ambar, name: Natan Potikha, name: Nikita Gupta, name: Nitzan Katz, name: Noam Velan, name: Ofir Roval, name: Ori Ram, name: Polina Zablotskaia, name: Prathamesh Bang, name: Priyanka Agrawal, name: Rakesh Ghiya, name: Sanjay Ganapathy, name: Simon Baumgartner, name: Sofia Erell, name: Sushant Prakash, name: Thibault Sellam, name: Vikram Rao, name: Xuanhui Wang, name: Yaroslav Akulov, name: Yulong Yang, name: Zhen Yang, name: Zhixin Lai, name: Zhongru Wu, name: Anca Dragan, name: Avinatan Hassidim, name: Fernando Pereira, name: Slav Petrov, name: Srinivasan Venkatachary, name: Tulsee Doshi, name: Yossi Matias, name: Sasha Goldshtein, name: Dipanjan Das
Published: 2025-12-11 16:35:14

We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually accurate text across diverse scenarios. The suite provides a holistic measure of factuality by aggregating the performance of models on four distinct sub-leaderboards: (1) FACTS Multimodal, which measures the factuality of responses to image-based questions; (2) FACTS Parametric, which assesses models' world knowledge by answering closed-book factoid questions from internal parameters; (3) FACTS Search, which evaluates factuality in information-seeking scenarios, where the model must use a search API; and (4) FACTS Grounding (v2), which evaluates whether long-form responses are grounded in provided documents, featuring significantly improved judge models. Each sub-leaderboard employs automated judge models to score model responses, and the final suite score is an average of the four components, designed to provide a robust and balanced assessment of a model's overall factuality. The FACTS Leaderboard Suite will be actively maintained, containing both public and private splits to allow for external participation while guarding its integrity. It can be found at https://www.kaggle.com/benchmarks/google/facts .
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
Authors: name: Xiang Chen, name: Yuling Shi, name: Qizhen Lan, name: Yuchao Qiu, name: Xiaodong Gu
Published: 2025-12-09 18:04:41

LLM agents are widely deployed in complex interactive tasks, yet privacy constraints often preclude centralized optimization and co-evolution across dynamic environments. While Federated Learning (FL) has proven effective on static datasets, its extension to the open-ended self-evolution of agents remains underexplored. Directly applying standard FL is challenging: heterogeneous tasks and sparse, trajectory-level rewards introduce severe gradient conflicts, destabilizing the global optimization process. To bridge this gap, we propose Fed-SE, a Federated Self-Evolution framework for LLM agents. Fed-SE establishes a local evolution-global aggregation paradigm. Locally, agents employ parameter-efficient fine-tuning on filtered, high-return trajectories to achieve stable gradient updates. Globally, Fed-SE aggregates updates within a low-rank subspace that disentangles environment-specific dynamics, effectively reducing negative transfer across clients. Experiments across five heterogeneous environments demonstrate that Fed-SE improves average task success rates by approximately 18% over federated baselines, validating its effectiveness in robust cross-environment knowledge transfer in privacy-constrained deployments.
MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
Authors: name: Yixin Wan, name: Lei Ke, name: Wenhao Yu, name: Kai-Wei Chang, name: Dong Yu
Published: 2025-12-11 04:53:58

We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility. Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion transformations extracted and verified from continuous videos. This new task is not only scientifically challenging but also practically significant, powering downstream applications such as frame-controlled video synthesis and animation.
  To evaluate model performance on the novel task, we introduce MotionEdit-Bench, a benchmark that challenges models on motion-centric edits and measures model performance with generative, discriminative, and preference-based metrics. Benchmark results reveal that motion editing remains highly challenging for existing state-of-the-art diffusion-based editing models. To address this gap, we propose MotionNFT (Motion-guided Negative-aware Fine Tuning), a post-training framework that computes motion alignment rewards based on how well the motion flow between input and model-edited images matches the ground-truth motion, guiding models toward accurate motion transformations. Extensive experiments on FLUX.1 Kontext and Qwen-Image-Edit show that MotionNFT consistently improves editing quality and motion fidelity of both base models on the motion editing task without sacrificing general editing ability, demonstrating its effectiveness.
Towards a Science of Scaling Agent Systems
Authors: name: Yubin Kim, name: Ken Gu, name: Chanwoo Park, name: Chunjong Park, name: Samuel Schmidgall, name: A. Ali Heydari, name: Yao Yan, name: Zhihan Zhang, name: Yuchen Zhuang, name: Mark Malhotra, name: Paul Pu Liang, name: Hae Won Park, name: Yuzhe Yang, name: Xuhai Xu, name: Yilun Du, name: Shwetak Patel, name: Tim Althoff, name: Daniel McDuff, name: Xin Liu
Published: 2025-12-09 06:52:21

Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench. Using five canonical architectures (Single, Independent, Centralized, Decentralized, Hybrid) instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations with standardized tools and token budgets. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated R^2=0.513. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: coordination yields diminishing or negative returns (beta=-0.408, p<0.001) once single-agent baselines exceed ~45%. (3) topology-dependent error amplification: independent agents amplify errors 17.2x through unchecked propagation, while centralized coordination contains this to 4.4x. Centralized coordination improves performance by 80.9% on parallelizable tasks like financial reasoning, while decentralized coordination excels on dynamic web navigation (+9.2% vs. +0.2%). Yet for sequential reasoning tasks, all multi-agent variants degraded performance by 39-70%. The framework predicts the optimal coordination strategy for 87% of held-out configurations, providing a predictive principle of agentic scaling based on measurable task properties.
GimbalDiffusion: Gravity-Aware Camera Control for Video Generation
Authors: name: Frédéric Fortier-Chouinard, name: Yannick Hold-Geoffroy, name: Valentin Deschaintre, name: Matheus Gadelha, name: Jean-François Lalonde
Published: 2025-12-09 20:54:35

Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive. Existing approaches typically encode camera trajectories through relative or ambiguous representations, limiting explicit geometric control. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing precise and interpretable control over camera parameters without requiring an initial reference frame. We leverage panoramic 360-degree videos to construct a wide variety of camera trajectories, well beyond the predominantly straight, forward-facing trajectories seen in conventional video data. To further enhance camera guidance, we introduce null-pitch conditioning, an annotation strategy that reduces the model's reliance on text content when conflicting with camera specifications (e.g., generating grass while the camera points towards the sky). Finally, we establish a benchmark for camera-aware video generation by rebalancing SpatialVID-HQ for comprehensive evaluation under wide camera pitch variation. Together, these contributions advance the controllability and robustness of text-to-video models, enabling precise, gravity-aligned camera manipulation within generative frameworks.