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Yuxuan Zhang, Yubo Wang, Yipeng Zhu +18 authors
ClawBench presents a comprehensive evaluation framework with 153 real-world tasks across 144 platforms to test AI agents' ability to automate everyday online activities requiring complex multi-step workflows and document processing.
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Hao Liang, Zhengyang Zhao, Meiyi Qiang +22 authors
DataFlex is a unified framework for dynamic data-centric training of large language models that supports sample selection, domain mixture adjustment, and sample reweighting while maintaining compatibility with standard training workflows and enabling efficient large-scale deployment.
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16 authors
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness.
Jiaru Zou, Rui Pan, Ruizhong Qiu +8 authors
RecursiveMAS extends recursive scaling principles from single models to multi-agent systems, enabling collaborative reasoning through iterative latent-space computations with improved efficiency and accuracy.
Ziyu Ma, Shidong Yang, Yuxiang Ji +5 authors
SkillClaw enables collective skill evolution in multi-user LLM agent systems by aggregating user interactions to autonomously update and improve reusable skills across the ecosystem.
DataFlow Team, Bohan Zeng, Daili Hua +38 authors
OpenWorldLib presents a standardized framework for advanced world models that integrate perception, interaction, and long-term memory capabilities for comprehensive world understanding and prediction.
Mengting Chen, Zhengrui Chen, Yongchao Du +16 authors
A commercial-scale virtual try-on system achieves high success rates, photorealistic results, and real-time performance through integrated system design and multi-stage training.
Team Seedance, De Chen, Liyang Chen +168 authors
Seedance 2.0 is a multi-modal audio-video generation model that supports text, image, audio, and video inputs with improved generation quality and speed.
Yuxuan Zhang, EunJeong Hwang, Huaisong Zhang +8 authors
Vision-language models face challenges in video understanding due to text-based biases in benchmarks and datasets, which are addressed through VidGround, a method that uses only visually grounded questions for post-training to improve performance.
Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +39 authors
World models are categorized into three capability levels and four law regimes to better understand and develop predictive environment models for AI agents across diverse domains.
Chenxu Yang, Chuanyu Qin, Qingyi Si +7 authors
RLSD combines reinforcement learning with verifiable rewards and self-distillation to achieve stable training with fine-grained updates and reliable policy direction from environmental feedback.
Jian Yang, Wei Zhang, Jiajun Wu +22 authors
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-quality code reasoning and performance.
Qihan Ren, Peng Wang, Ruikun Cai +8 authors
Supervised finetuning and reinforcement learning exhibit conditional cross-domain generalization in reasoning tasks, influenced by optimization dynamics, data quality, and model capability, with asymmetric outcomes between reasoning improvement and safety degradation.
Team HY-World, Chenjie Cao, Xuhui Zuo +42 authors
HY-World 2.0 is a multi-modal world model framework that generates high-fidelity 3D Gaussian Splatting scenes from diverse inputs using specialized modules for panorama generation, trajectory planning, world expansion, and composition, along with an enhanced rendering platform for interactive 3D exploration.
Ali Slim, Haydar Hamieh, Jawad Kotaich +5 authors
QuanBench+ evaluates large language models on quantum code generation across multiple frameworks using functional testing and repair-based feedback, revealing significant progress but persistent dependence on framework-specific knowledge.
Tencent Robotics X, HY Vision Team, Xumin Yu +19 authors
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities.
Weijie Wang, Xiaoxuan He, Youping Gu +9 authors
World-R1 framework improves video generation by incorporating 3D constraints through reinforcement learning and specialized text datasets while maintaining visual quality and scalability.
Bowen Ye, Rang Li, Qibin Yang +10 authors
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments.
V Team, Wenyi Hong, Xiaotao Gu +75 authors
GLM-5V-Turbo integrates multimodal perception as a core reasoning component for agentic tasks, demonstrating strong performance in multimodal coding and visual tool use while maintaining text-only capabilities.
Yaxuan Li, Yuxin Zuo, Bingxiang He +8 authors
On-policy distillation dynamics in large language models depend on compatible thinking patterns between teacher and student models, with successful distillation characterized by alignment on high-probability tokens and requiring teachers to provide novel capabilities beyond student training data.
Weian Mao, Xi Lin, Wei Huang +5 authors
TriAttention addresses KV cache memory bottlenecks in LLMs by leveraging Q/K vector concentration in pre-RoPE space to improve key importance estimation and enable efficient long-context generation.
Xinlei Yu, Zhangquan Chen, Yongbo He +34 authors
Latent space is emerging as a fundamental computational substrate for language-based models, offering advantages over explicit token-level approaches through continuous representation that mitigates linguistic redundancy and sequential inefficiency.
Meituan LongCat Team, Bin Xiao, Chao Wang +86 authors
Discrete Native Autoregressive framework enables unified multimodal processing by representing diverse modalities in a shared discrete space through a novel visual transformer architecture.
Inclusion AI, Tiwei Bie, Haoxing Chen +15 authors
LLaDA2.0-Uni is a unified discrete diffusion language model that integrates multimodal understanding and generation through a semantic discrete tokenizer, MoE-based backbone, and diffusion decoder, achieving performance comparable to specialized vision-language models while enabling efficient inference and high-fidelity image generation.
Chiyu Ma, Shuo Yang, Kexin Huang +7 authors
FIPO enhances reinforcement learning for language models by using discounted future-KL divergence to improve credit assignment and extend reasoning chains, achieving better mathematical problem-solving performance.
Bin Wang, Tianyao He, Linke Ouyang +40 authors
Training data engineering and optimized strategies improve document parsing performance without architectural changes, achieving state-of-the-art results on OmniDocBench v1.6.
Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan +6 authors
A large-scale dynamic dataset derived from AAA games is introduced to improve generative inverse and forward rendering, featuring high-resolution synchronized RGB and G-buffer data alongside a novel VLM-based evaluation method that correlates well with human judgment.
Xiangru Jian, Hao Xu, Wei Pang +13 authors
FORGE introduces a high-quality multimodal manufacturing dataset with fine-grained domain semantics to evaluate MLLMs on real-world tasks, revealing that domain-specific knowledge rather than visual grounding limits performance, and demonstrating that supervised fine-tuning on structured annotations significantly improves accuracy.
Zhengxu Yu, Yu Fu, Zhiyuan He +5 authors
OneManCompany (OMC) introduces an organizational framework for multi-agent systems that enables dynamic team assembly, governance, and improvement through portable agent identities and hierarchical decision-making processes.
Hongyuan Adam Lu, Z. L., Victor Wei +5 authors
A novel framework for improving large language model performance through textual frequency analysis, including laws, distillation, and curriculum training approaches.
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