Orionnnn/hub-stats huggingface dataset:
StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors
Authors: name: Guibao Shen, name: Yihua Du, name: Wenhang Ge, name: Jing He, name: Chirui Chang, name: Donghao Zhou, name: Zhen Yang, name: Luozhou Wang, name: Xin Tao, name: Ying-Cong Chen
Published: 2025-12-18 18:59:50

The rapid growth of stereoscopic displays, including VR headsets and 3D cinemas, has led to increasing demand for high-quality stereo video content. However, producing 3D videos remains costly and complex, while automatic Monocular-to-Stereo conversion is hindered by the limitations of the multi-stage ``Depth-Warp-Inpaint'' (DWI) pipeline. This paradigm suffers from error propagation, depth ambiguity, and format inconsistency between parallel and converged stereo configurations. To address these challenges, we introduce UniStereo, the first large-scale unified dataset for stereo video conversion, covering both stereo formats to enable fair benchmarking and robust model training. Building upon this dataset, we propose StereoPilot, an efficient feed-forward model that directly synthesizes the target view without relying on explicit depth maps or iterative diffusion sampling. Equipped with a learnable domain switcher and a cycle consistency loss, StereoPilot adapts seamlessly to different stereo formats and achieves improved consistency. Extensive experiments demonstrate that StereoPilot significantly outperforms state-of-the-art methods in both visual fidelity and computational efficiency. Project page: https://hit-perfect.github.io/StereoPilot/.
Exploration v.s. Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
Authors: name: Peter Chen, name: Xiaopeng Li, name: Ziniu Li, name: Wotao Yin, name: Xi Chen, name: Tianyi Lin
Published: 2025-12-18 18:59:27

This paper examines the exploration-exploitation trade-off in reinforcement learning with verifiable rewards (RLVR), a framework for improving the reasoning of Large Language Models (LLMs). Recent studies suggest that RLVR can elicit strong mathematical reasoning in LLMs through two seemingly paradoxical mechanisms: spurious rewards, which suppress exploitation by rewarding outcomes unrelated to the ground truth, and entropy minimization, which suppresses exploration by pushing the model toward more confident and deterministic outputs, highlighting a puzzling dynamic: both discouraging exploitation and discouraging exploration improve reasoning performance, yet the underlying principles that reconcile these effects remain poorly understood. We focus on two fundamental questions: (i) how policy entropy relates to performance, and (ii) whether spurious rewards yield gains, potentially through the interplay of clipping bias and model contamination. Our results show that clipping bias under spurious rewards reduces policy entropy, leading to more confident and deterministic outputs, while entropy minimization alone is insufficient for improvement. We further propose a reward-misalignment model explaining why spurious rewards can enhance performance beyond contaminated settings. Our findings clarify the mechanisms behind spurious-reward benefits and provide principles for more effective RLVR training.
Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
Authors: name: Xin Lin, name: Meixi Song, name: Dizhe Zhang, name: Wenxuan Lu, name: Haodong Li, name: Bo Du, name: Ming-Hsuan Yang, name: Truong Nguyen, name: Lu Qi
Published: 2025-12-18 18:59:29

In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: https://insta360-research-team.github.io/DAP_website/ {https://insta360-research-team.github.io/DAP\_website/}
DeContext as Defense: Safe Image Editing in Diffusion Transformers
Authors: name: Linghui Shen, name: Mingyue Cui, name: Xingyi Yang
Published: 2025-12-18 15:01:44

In-context diffusion models allow users to modify images with remarkable ease and realism. However, the same power raises serious privacy concerns: personal images can be easily manipulated for identity impersonation, misinformation, or other malicious uses, all without the owner's consent. While prior work has explored input perturbations to protect against misuse in personalized text-to-image generation, the robustness of modern, large-scale in-context DiT-based models remains largely unexamined. In this paper, we propose DeContext, a new method to safeguard input images from unauthorized in-context editing. Our key insight is that contextual information from the source image propagates to the output primarily through multimodal attention layers. By injecting small, targeted perturbations that weaken these cross-attention pathways, DeContext breaks this flow, effectively decouples the link between input and output. This simple defense is both efficient and robust. We further show that early denoising steps and specific transformer blocks dominate context propagation, which allows us to concentrate perturbations where they matter most. Experiments on Flux Kontext and Step1X-Edit show that DeContext consistently blocks unwanted image edits while preserving visual quality. These results highlight the effectiveness of attention-based perturbations as a powerful defense against image manipulation.
TabReX : Tabular Referenceless eXplainable Evaluation
Authors: name: Tejas Anvekar, name: Juhna Park, name: Aparna Garimella, name: Vivek Gupta
Published: 2025-12-17 19:20:20

Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely on fixed references that limit generalization. We present TabReX, a reference-less, property-driven framework for evaluating tabular generation via graph-based reasoning. TabReX converts both source text and generated tables into canonical knowledge graphs, aligns them through an LLM-guided matching process, and computes interpretable, rubric-aware scores that quantify structural and factual fidelity. The resulting metric provides controllable trade-offs between sensitivity and specificity, yielding human-aligned judgments and cell-level error traces. To systematically asses metric robustness, we introduce TabReX-Bench, a large-scale benchmark spanning six domains and twelve planner-driven perturbation types across three difficulty tiers. Empirical results show that TabReX achieves the highest correlation with expert rankings, remains stable under harder perturbations, and enables fine-grained model-vs-prompt analysis establishing a new paradigm for trustworthy, explainable evaluation of structured generation systems.
Adaptation of Agentic AI
Authors: name: Pengcheng Jiang, name: Jiacheng Lin, name: Zhiyi Shi, name: Zifeng Wang, name: Luxi He, name: Yichen Wu, name: Ming Zhong, name: Peiyang Song, name: Qizheng Zhang, name: Heng Wang, name: Xueqiang Xu, name: Hanwen Xu, name: Pengrui Han, name: Dylan Zhang, name: Jiashuo Sun, name: Chaoqi Yang, name: Kun Qian, name: Tian Wang, name: Changran Hu, name: Manling Li, name: Quanzheng Li, name: Hao Peng, name: Sheng Wang, name: Jingbo Shang, name: Chao Zhang, name: Jiaxuan You, name: Liyuan Liu, name: Pan Lu, name: Yu Zhang, name: Heng Ji, name: Yejin Choi, name: Dawn Song, name: Jimeng Sun, name: Jiawei Han
Published: 2025-12-18 08:38:51

Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Authors: name: Tiwei Bie, name: Maosong Cao, name: Kun Chen, name: Lun Du, name: Mingliang Gong, name: Zhuochen Gong, name: Yanmei Gu, name: Jiaqi Hu, name: Zenan Huang, name: Zhenzhong Lan, name: Chengxi Li, name: Chongxuan Li, name: Jianguo Li, name: Zehuan Li, name: Huabin Liu, name: Ling Liu, name: Guoshan Lu, name: Xiaocheng Lu, name: Yuxin Ma, name: Jianfeng Tan, name: Lanning Wei, name: Ji-Rong Wen, name: Yipeng Xing, name: Xiaolu Zhang, name: Junbo Zhao, name: Da Zheng, name: Jun Zhou, name: Junlin Zhou, name: Zhanchao Zhou, name: Liwang Zhu, name: Yihong Zhuang
Published: 2025-12-10 09:26:18

This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (AR) models -- establishing a new paradigm for frontier-scale deployment. Instead of costly training from scratch, LLaDA2.0 upholds knowledge inheritance, progressive adaption and efficiency-aware design principle, and seamless converts a pre-trained AR model into dLLM with a novel 3-phase block-level WSD based training scheme: progressive increasing block-size in block diffusion (warm-up), large-scale full-sequence diffusion (stable) and reverting back to compact-size block diffusion (decay). Along with post-training alignment with SFT and DPO, we obtain LLaDA2.0-mini (16B) and LLaDA2.0-flash (100B), two instruction-tuned Mixture-of-Experts (MoE) variants optimized for practical deployment. By preserving the advantages of parallel decoding, these models deliver superior performance and efficiency at the frontier scale. Both models were open-sourced.
Kling-Omni Technical Report
Authors: name: Kling Team, name: Jialu Chen, name: Yuanzheng Ci, name: Xiangyu Du, name: Zipeng Feng, name: Kun Gai, name: Sainan Guo, name: Feng Han, name: Jingbin He, name: Kang He, name: Xiao Hu, name: Xiaohua Hu, name: Boyuan Jiang, name: Fangyuan Kong, name: Hang Li, name: Jie Li, name: Qingyu Li, name: Shen Li, name: Xiaohan Li, name: Yan Li, name: Jiajun Liang, name: Borui Liao, name: Yiqiao Liao, name: Weihong Lin, name: Quande Liu, name: Xiaokun Liu, name: Yilun Liu, name: Yuliang Liu, name: Shun Lu, name: Hangyu Mao, name: Yunyao Mao, name: Haodong Ouyang, name: Wenyu Qin, name: Wanqi Shi, name: Xiaoyu Shi, name: Lianghao Su, name: Haozhi Sun, name: Peiqin Sun, name: Pengfei Wan, name: Chao Wang, name: Chenyu Wang, name: Meng Wang, name: Qiulin Wang, name: Runqi Wang, name: Xintao Wang, name: Xuebo Wang, name: Zekun Wang, name: Min Wei, name: Tiancheng Wen, name: Guohao Wu, name: Xiaoshi Wu, name: Zhenhua Wu, name: Da Xie, name: Yingtong Xiong, name: Yulong Xu, name: Sile Yang, name: Zikang Yang, name: Weicai Ye, name: Ziyang Yuan, name: Shenglong Zhang, name: Shuaiyu Zhang, name: Yuanxing Zhang, name: Yufan Zhang, name: Wenzheng Zhao, name: Ruiliang Zhou, name: Yan Zhou, name: Guosheng Zhu, name: Yongjie Zhu
Published: 2025-12-18 17:08:12

We present Kling-Omni, a generalist generative framework designed to synthesize high-fidelity videos directly from multimodal visual language inputs. Adopting an end-to-end perspective, Kling-Omni bridges the functional separation among diverse video generation, editing, and intelligent reasoning tasks, integrating them into a holistic system. Unlike disjointed pipeline approaches, Kling-Omni supports a diverse range of user inputs, including text instructions, reference images, and video contexts, processing them into a unified multimodal representation to deliver cinematic-quality and highly-intelligent video content creation. To support these capabilities, we constructed a comprehensive data system that serves as the foundation for multimodal video creation. The framework is further empowered by efficient large-scale pre-training strategies and infrastructure optimizations for inference. Comprehensive evaluations reveal that Kling-Omni demonstrates exceptional capabilities in in-context generation, reasoning-based editing, and multimodal instruction following. Moving beyond a content creation tool, we believe Kling-Omni is a pivotal advancement toward multimodal world simulators capable of perceiving, reasoning, generating and interacting with the dynamic and complex worlds.
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Authors: name: Tianshuai Hu, name: Xiaolu Liu, name: Song Wang, name: Yiyao Zhu, name: Ao Liang, name: Lingdong Kong, name: Guoyang Zhao, name: Zeying Gong, name: Jun Cen, name: Zhiyu Huang, name: Xiaoshuai Hao, name: Linfeng Li, name: Hang Song, name: Xiangtai Li, name: Jun Ma, name: Shaojie Shen, name: Jianke Zhu, name: Dacheng Tao, name: Ziwei Liu, name: Junwei Liang
Published: 2025-12-18 16:57:44

Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-tailed scenarios. Their cascaded design further propagates perception errors, degrading downstream planning and control. Vision-Action (VA) models address some limitations by learning direct mappings from visual inputs to actions, but they remain opaque, sensitive to distribution shifts, and lack structured reasoning or instruction-following capabilities. Recent progress in Large Language Models (LLMs) and multimodal learning has motivated the emergence of Vision-Language-Action (VLA) frameworks, which integrate perception with language-grounded decision making. By unifying visual understanding, linguistic reasoning, and actionable outputs, VLAs offer a pathway toward more interpretable, generalizable, and human-aligned driving policies. This work provides a structured characterization of the emerging VLA landscape for autonomous driving. We trace the evolution from early VA approaches to modern VLA frameworks and organize existing methods into two principal paradigms: End-to-End VLA, which integrates perception, reasoning, and planning within a single model, and Dual-System VLA, which separates slow deliberation (via VLMs) from fast, safety-critical execution (via planners). Within these paradigms, we further distinguish subclasses such as textual vs. numerical action generators and explicit vs. implicit guidance mechanisms. We also summarize representative datasets and benchmarks for evaluating VLA-based driving systems and highlight key challenges and open directions, including robustness, interpretability, and instruction fidelity. Overall, this work aims to establish a coherent foundation for advancing human-compatible autonomous driving systems.
SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
Authors: name: Wentao Guo, name: Mayank Mishra, name: Xinle Cheng, name: Ion Stoica, name: Tri Dao
Published: 2025-12-16 04:39:10

Mixture of Experts (MoE) models have emerged as the de facto architecture for scaling up language models without significantly increasing the computational cost. Recent MoE models demonstrate a clear trend towards high expert granularity (smaller expert intermediate dimension) and higher sparsity (constant number of activated experts with higher number of total experts), which improve model quality per FLOP. However, fine-grained MoEs suffer from increased activation memory footprint and reduced hardware efficiency due to higher IO costs, while sparser MoEs suffer from wasted computations due to padding in Grouped GEMM kernels. In response, we propose a memory-efficient algorithm to compute the forward and backward passes of MoEs with minimal activation caching for the backward pass. We also design GPU kernels that overlap memory IO with computation benefiting all MoE architectures. Finally, we propose a novel "token rounding" method that minimizes the wasted compute due to padding in Grouped GEMM kernels. As a result, our method SonicMoE reduces activation memory by 45% and achieves a 1.86x compute throughput improvement on Hopper GPUs compared to ScatterMoE's BF16 MoE kernel for a fine-grained 7B MoE. Concretely, SonicMoE on 64 H100s achieves a training throughput of 213 billion tokens per day comparable to ScatterMoE's 225 billion tokens per day on 96 H100s for a 7B MoE model training with FSDP-2 using the lm-engine codebase. Under high MoE sparsity settings, our tile-aware token rounding algorithm yields an additional 1.16x speedup on kernel execution time compared to vanilla top-K routing while maintaining similar downstream performance. We open-source all our kernels to enable faster MoE model training.
FrontierCS: Evolving Challenges for Evolving Intelligence
Authors: name: Qiuyang Mang, name: Wenhao Chai, name: Zhifei Li, name: Huanzhi Mao, name: Shang Zhou, name: Alexander Du, name: Hanchen Li, name: Shu Liu, name: Edwin Chen, name: Yichuan Wang, name: Xieting Chu, name: Zerui Cheng, name: Yuan Xu, name: Tian Xia, name: Zirui Wang, name: Tianneng Shi, name: Jianzhu Yao, name: Yilong Zhao, name: Qizheng Zhang, name: Charlie Ruan, name: Zeyu Shen, name: Kaiyuan Liu, name: Runyuan He, name: Dong Xing, name: Zerui Li, name: Zirong Zeng, name: Yige Jiang, name: Lufeng Cheng, name: Ziyi Zhao, name: Youran Sun, name: Wesley Zheng, name: Meiyuwang Zhang, name: Ruyi Ji, name: Xuechang Tu, name: Zihan Zheng, name: Zexing Chen, name: Kangyang Zhou, name: Zhaozi Wang, name: Jingbang Chen, name: Aleksandra Korolova, name: Peter Henderson, name: Pramod Viswanath, name: Vijay Ganesh, name: Saining Xie, name: Zhuang Liu, name: Dawn Song, name: Sewon Min, name: Ion Stoica, name: Joseph E. Gonzalez, name: Jingbo Shang, name: Alvin Cheung
Published: 2025-12-17 18:52:45

We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competitive programming participants and problem setters. Unlike existing benchmarks that focus on tasks with known optimal solutions, FrontierCS targets problems where the optimal solution is unknown, but the quality of a solution can be objectively evaluated. Models solve these tasks by implementing executable programs rather than outputting a direct answer. FrontierCS includes algorithmic problems, which are often NP-hard variants of competitive programming problems with objective partial scoring, and research problems with the same property. For each problem we provide an expert reference solution and an automatic evaluator. Combining open-ended design, measurable progress, and expert curation, FrontierCS provides a benchmark at the frontier of computer-science difficulty. Empirically, we find that frontier reasoning models still lag far behind human experts on both the algorithmic and research tracks, that increasing reasoning budgets alone does not close this gap, and that models often over-optimize for generating merely workable code instead of discovering high-quality algorithms and system designs.
Puzzle Curriculum GRPO for Vision-Centric Reasoning
Authors: name: Ahmadreza Jeddi, name: Hakki Can Karaimer, name: Hue Nguyen, name: Zhongling Wang, name: Ke Zhao, name: Javad Rajabi, name: Ran Zhang, name: Raghav Goyal, name: Babak Taati, name: Radek Grzeszczuk
Published: 2025-12-16 22:17:25

Recent reinforcement learning (RL) approaches like outcome-supervised GRPO have advanced chain-of-thought reasoning in Vision Language Models (VLMs), yet key issues linger: (i) reliance on costly and noisy hand-curated annotations or external verifiers; (ii) flat and sparse reward schemes in GRPO; and (iii) logical inconsistency between a chain's reasoning and its final answer. We present Puzzle Curriculum GRPO (PC-GRPO), a supervision-free recipe for RL with Verifiable Rewards (RLVR) that strengthens visual reasoning in VLMs without annotations or external verifiers. PC-GRPO replaces labels with three self-supervised puzzle environments: PatchFit, Rotation (with binary rewards) and Jigsaw (with graded partial credit mitigating reward sparsity). To counter flat rewards and vanishing group-relative advantages, we introduce a difficulty-aware curriculum that dynamically weights samples and peaks at medium difficulty. We further monitor Reasoning-Answer Consistency (RAC) during post-training: mirroring reports for vanilla GRPO in LLMs, RAC typically rises early then degrades; our curriculum delays this decline, and consistency-enforcing reward schemes further boost RAC. RAC correlates with downstream accuracy. Across diverse benchmarks and on Qwen-7B and Qwen-3B backbones, PC-GRPO improves reasoning quality, training stability, and end-task accuracy, offering a practical path to scalable, verifiable, and interpretable RL post-training for VLMs.
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
Authors: name: Jonas Golde, name: Patrick Haller, name: Alan Akbik
Published: 2025-12-15 20:36:39

Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appeared as by-products of broader experiments rather than as systematic, reusable resources. We introduce FiNERweb, a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. Building on FineWeb-Edu, our approach trains regression models to identify NER-relevant passages and annotates them with multilingual LLMs, resulting in about 225k passages with 235k distinct entity labels. Our experiments show that the regression model achieves more than 84 F1, and that models trained on FiNERweb obtain comparable or improved performance in zero shot transfer settings on English, Thai, and Swahili, despite being trained on 19x less data than strong baselines. In addition, we assess annotation quality using LLM-as-a-judge and observe consistently high scores for both faithfulness (3.99 out of 5) and completeness (4.05 out of 5), indicating reliable and informative annotations. Further, we release the dataset with both English labels and translated label sets in the respective target languages because we observe that the performance of current state-of-the-art models drops by 0.02 to 0.09 F1 when evaluated using target language labels instead of English ones. We release FiNERweb together with all accompanying artifacts to the research community in order to facilitate more effective student-teacher training for multilingual named entity recognition.