Explore · 每周精选
发现最受关注的研究论文,追踪研究趋势,订阅感兴趣的期刊与关键词。
905 篇论文 · 按点赞排序
SII-OpenMOSS Team, Donghua Yu, Mingshu Chen +37 authors
MOVA is an open-source model that generates synchronized audio-visual content using a Mixture-of-Experts architecture with 32 billion parameters, supporting image-text to video-audio generation tasks.
Meng Luo, Yanlin Li, Hao Li +7 authors
AI applications across the game lifecycle are organized into six roles, highlighting transferable capabilities and setting-specific limitations that require targeted validation.
Veuns-Team, Changlong Gao, Zhangxuan Gu +24 authors
UI-Venus-1.5 is a unified GUI agent with improved performance through mid-training stages, online reinforcement learning, and model merging techniques.
Jianyu Wu, Yizhou Wang, Encheng Su +2 authors
Dual-Anchored Policy Distillation resolves privilege illusion in on-policy self-distillation by aligning matched-information paths and reducing reliance on privileged guidance, improving performance across model scales.
Fengkai Yang, Zherui Chen, Xiaohan Wang +10 authors
Group-based reinforcement learning from verifier rewards suffers from biased advantage estimation that underestimates hard prompts and overestimates easy prompts, which is addressed through a history-aware adaptive difficulty weighting method that improves performance on mathematical reasoning benchmarks.
Yinjie Wang, Xuyang Chen, Xiaolong Jin +2 authors
OpenClaw-RL framework enables policy learning from diverse next-state signals across multiple interaction modalities using asynchronous training with PRM judges and hindsight-guided distillation.
Simple AI, Yuteng Wei, Jinming Ma +15 authors
High-fidelity robot-free UMI data enables direct real-robot deployment without teleoperation anchors, improving manipulation policy accuracy and success rates.
Kaijin Chen, Dingkang Liang, Xin Zhou +4 authors
Hybrid Memory enables video world models to maintain consistent tracking of dynamic subjects during occlusion by combining archival storage for static backgrounds with active tracking for moving objects, using a specialized architecture with tokenized memory and spatiotemporal retrieval mechanisms.
Bingchen Yao, Haobo Xu, Haokun Lin +6 authors
STEPQuant is a spatial-temporal quantization framework that reduces memory usage of linear attention recurrent states by allocating precision based on error magnitude and memory lifetime.
Panrong Tong, Quyu Kong, Pengxiang Li +12 authors
MAI-Gym is an open platform that unifies GUI agent evaluation, data collection, and online reinforcement learning training across mobile, computer, and web environments, enabling scalable synthesis of verified tasks and training of a unified 9B agent with state-of-the-art open performance.
Yawen Luo, Xiaoyu Shi, Junhao Zhuang +5 authors
ShotStream enables real-time interactive multi-shot video generation through causal architecture design, dual-cache memory mechanisms, and two-stage distillation to maintain visual consistency and reduce latency.
Daxiang Dong, Mingming Zheng, Dong Xu +17 authors
Qianfan-OCR is a 4B-parameter vision-language model that unifies document parsing, layout analysis, and understanding while maintaining strong performance across multiple OCR benchmarks through its Layout-as-Thought mechanism.
Wenxuan Huang, Yu Zeng, Qiuchen Wang +12 authors
Vision-DeepResearch introduces a multimodal deep-research paradigm enabling multi-turn, multi-entity, and multi-scale visual and textual search with deep-research capabilities integrated through cold-start supervision and reinforcement learning.
Junyoung Seo, Hyunwook Choi, Minkyung Kwon +10 authors
What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificial environments by imagining all content. We present Seoul World Model (SWM), a city-scale world model grounded in the real city of Seoul. SWM anchors autoregressive video generation through retrieval-augmented conditioning on nearby street-view images. However, this design introduces several challenges, including temporal misalignment between retrieved references and the dynamic target scene, limited trajectory diversity and data sparsity from vehicle-mounted captures at sparse intervals. We address these challenges through cross-temporal pairing, a large-scale synthetic dataset enabling diverse camera trajectories, and a view interpolation pipeline that synthesizes coherent training videos from sparse street-view images. We further introduce a Virtual Lookahead Sink to stabilize long-horizon generation by continuously re-grounding each chunk to a retrieved image at a future location. We evaluate SWM against recent video world models across three cities: Seoul, Busan, and Ann Arbor. SWM outperforms existing methods in generating spatially faithful, temporally consistent, long-horizon videos grounded in actual urban environments over trajectories reaching hundreds of meters, while supporting diverse camera movements and text-prompted scenario variations.
Taoyang Jia, Weikai Huang, Linxin Song +6 authors
Reflex is a three-stage framework that enables humanoid robots to catch thrown boxes using egocentric vision by decomposing whole-body control, dynamics inference, and visual perception into sequentially trained stages.
Yu Wang, Yi Wang, Rui Dai +4 authors
Urban socio-semantic segmentation is achieved through a vision-language model framework that combines cross-modal recognition and multi-stage reasoning with reinforcement learning optimization.
Ruixiang Zhao, Hualei Wang, Renhe Sun +24 authors
A proactive full-duplex system with separate audio-visual and audio models enables continuous perception, native speech generation, and asynchronous tool execution for real-time dialogue.
Shuqi Lu, Chaofan Li, Kun Luo +21 authors
AREX is a recursively self-improving deep research agent that verifies answers constraint-wise, compresses verified evidence into a compact state, and uses targeted follow-up research to refine results over long horizons.
Zhanhui Zhou, Lingjie Chen, Hanghang Tong +1 authors
A unified open-source framework is presented that standardizes core components of diffusion language modeling for reproduction, customization, and accessible development of both large and small models.
Yifei Wang, Yueqi Wang, Zhenrui Yue +6 authors
FASA is a novel framework that uses query-aware token eviction and functional sparsity in RoPE to reduce KV cache memory usage while maintaining high performance in long-context LLM tasks.
Ziyang Ma, Zhikang Niu, Wenming Tu +30 authors
AuK is an open-source foundational model that unifies speech generation and editing via natural-language instructions and audio context, using a multimodal language model, joint VAE, hybrid rectified-flow Transformer, and efficient distillation for fast inference.
Haolin He, Yunfei Chu, Qi Chen +15 authors
OmniVChat-Studio synthesizes audio-visual dialogues to train and evaluate omni models via reinforcement learning, improving real-world dialogue performance.
Xunhao Lai, Weiqi Xu, Yufeng Yang +8 authors
MiniMax Sparse Attention enables efficient processing of ultra-long contexts in large language models through blockwise sparsity and optimized GPU execution, achieving significant speedups while maintaining performance.
Xin Ding, Liang Mi, Mingzhe Huang +12 authors
Zetta is a closed-loop embodied harness that evolves runtime critics and recovery skills online to govern physical execution at action frequency, achieving high success on robot benchmarks with faster inference and scaling self-exploration.
Heng Wang, Jielin Qiu, Wenting Zhao +7 authors
Random eviction of reasoning tokens matches selective KV cache compression because reasoning traces are self-protecting through redundancy, making scoring unnecessary once prompts are preserved.
Yibo Li, Jinhang Qiu, Zhi Zheng +4 authors
Learn2Play Bench evaluates LLM agents on novel text-based games to measure learning from interaction rather than pretrained reasoning, revealing benefits of full experience retention, a human performance gap, and harness design effects.
Yuxin Zuo, Zikai Xiao, Li Sheng +30 authors
Language-based world models enable agentic environment simulation across multiple domains and enhance general agent performance through scalable simulation and improved downstream task performance.
Yukang Cao, Haozhe Xie, Fangzhou Hong +4 authors
HSImul3R presents a unified framework for 3D reconstruction of human-scene interactions that bridges the perception-simulation gap through physics-grounded bidirectional optimization and reinforcement learning.
Liangcai Su, Zhaopeng Feng, Zhuo Chen +13 authors
FrontierChallenge evaluates end-to-end scientific workflows across domains, revealing that frontier models complete only about 20% of tasks despite high partial scores and frequent claims of completion.
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.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号