Jingwen Liang, Gengyu Wang
Daily Paper Cast
We update every weekday to discuss highest-voted papers from Huggingface Daily Paper (https://huggingface.co/papers). Both the podcast scripts and audio are generated by AI. Feedback and suggestions are welcome! Email us: dailypapercast.ai@gmail.comCreator:Jingwen Liang, 3D ML, https://www.linkedin.com/in/jingwen-liang/Gengyu Wang, LLM ML, http://wanggengyu.comListen on: Spotify: https://open.spotify.com/show/21nrhmdaA8qoBiH8q03NXLApple Podcast: https://podcasts.apple.com/us/podcast/daily-paper-cast/id1777620236Cover Image by Kawen Kuang https://kawen.art
Visita il sito del podcast e sostieni chi lo crea: dailypapercast.transistor.fm
Autore
Jingwen Liang, Gengyu Wang
Categoria
Sito del podcast
Ultimo episodio
15 ott 2026
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Episodi
Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation 15.10.2026 23:08
🤗 Upvotes: 166 | cs. CV Authors: Weijie Wang, Haomin Zhang, Ye Li, Akide Liu, Zeyu Zhang, Haoyu Zhao, Yefei He, Donny Y. Chen, Feng Chen, Bohan Zhuang Title: Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation Arxiv: http://arxiv.org/abs/2610.20571v1 Abstract: Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally co...
Player-1: Learning to Play from Synthetic Experience 15.10.2026 23:48
🤗 Upvotes: 117 | cs. CV, cs. AI, cs. HC Authors: Mingyu Ouyang, Yiran Wang, Siyuan Hu, Xingyilang Yin, Huiqiang Sun, Guangzhi Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Ying Shan, Mike Zheng Shou, Hwee Tou Ng Title: Player-1: Learning to Play from Synthetic Experience Arxiv: http://arxiv.org/abs/2610.20584v1 Abstract: Computer-use agents have demonstrated strong capabilities in interpreting scree...
LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation 15.10.2026 21:21
🤗 Upvotes: 50 | cs. LG Authors: Zefeng He, Yunkai Chai, Xiaoyang Chen, Tong Zhu, Zongliang Wu, Yun Luo, Yafu Li, Yu Cheng Title: LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation Arxiv: http://arxiv.org/abs/2610.19975v1 Abstract: Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work de...
Recovering Reliable Supervision via Critique-Guided On-Policy Distillation 15.10.2026 20:41
🤗 Upvotes: 47 | cs. LG, cs. AI Authors: Kaixiang Zhao, Tianrun Yu, Shangzhe Li, Yuxiao Yang, Amanda Hughes, Weitong Zhang, Taylor W. Killian Title: Recovering Reliable Supervision via Critique-Guided On-Policy Distillation Arxiv: http://arxiv.org/abs/2610.20572v1 Abstract: On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming...
Cortex: Consolidating Context into Parametric Memory 15.10.2026 22:52
🤗 Upvotes: 47 | cs. CL, cs. AI, cs. CV, cs. LG, cs. MA Authors: Jintian Zhang, Feng He, Xinnian Liang, Ziwen Xu, Haoming Xu, Haojing Huang, Shuangzhi Wu, Ningyu Zhang Title: Cortex: Consolidating Context into Parametric Memory Arxiv: http://arxiv.org/abs/2610.20399v1 Abstract: Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biol...
ActionOPD: Distilling Decisions, Not Tokens 15.10.2026 21:56
🤗 Upvotes: 44 | cs. AI Authors: Huining Yuan, Xiangmin Yi, Zelai Xu, Zixu Wang, Xinlei Chen, Xiao-Ping Zhang, Yu Wang, Chao Yu Title: ActionOPD: Distilling Decisions, Not Tokens Arxiv: http://arxiv.org/abs/2610.19820v1 Abstract: Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which sta...
Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents 15.10.2026 19:43
🤗 Upvotes: 39 | cs. SE, cs. AI Authors: Yuyao Ge, Shenghua Liu, Peizhong Gao, Chendi Ge, Chenxu Liu, Yiwei Wang, Lingrui Mei, Baolong Bi, Jiayu Yao, Jiafeng Guo, Xueqi Cheng Title: Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents Arxiv: http://arxiv.org/abs/2610.20344v1 Abstract: Agents driven by large language models are increasingly deployed on long-horizon ta...
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory 15.10.2026 19:49
🤗 Upvotes: 31 | cs. AI, cs. CL, cs. RO Authors: Minghao Guo, Qingyue Jiao, Xiujin Liu, Jianhong Pang, Ruimeng Yang, Naihao Xu, Liwei Che, Haochen Xue, Ziqi Wang, Xinying Cai, Jiaxuan Luo, Jiaxin Deng, Xuesong Ye, Jintao Qu, Wenyao Gao, Jingwen Gu, Chenyang Zhao, Shichun Liu, Guibin Zhang, Zefang Liu, Siyin Wang, Wujiang Xu, Danrui Li, Zeru Shi, Woongyeong Yeo, Shuang Liu, Wei-Chieh Huang, Weizhi...
LensErase: A Foundation Model for Erase Anything 15.10.2026 22:52
🤗 Upvotes: 30 | cs. CV Authors: Yunuo Chen, Xiuyu Wu, Qinhong Yang, Jinjing Zhao, Yanchen Dong, Sirui Zhang, Dong Chen, Fangyun Wei, Lei Shi, Ji Li, Dongdong Chen, Guo Lu, Yan Lu, Baining Guo Title: LensErase: A Foundation Model for Erase Anything Arxiv: http://arxiv.org/abs/2610.18072v1 Abstract: We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cov...
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization 15.10.2026 25:43
🤗 Upvotes: 25 | cs. LG, cs. AI Authors: Sihwan Park, Sung-Yub Kim, Doohyuk Jang, Eunho Yang Title: KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization Arxiv: http://arxiv.org/abs/2610.14448v1 Abstract: Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail generation....
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes 14.10.2026 22:24
🤗 Upvotes: 58 | cs. CV, cs. AI Authors: Yu Wang, Rui Dai, Wei Chen, Yi Wang, Bo Dang, Kaikui Liu, Xiangxiang Chu, Yansheng Li Title: Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes Arxiv: http://arxiv.org/abs/2610.18356v1 Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor s...
Vela: User Behavior as Supervision Sparks LLM Intelligence 14.10.2026 23:19
🤗 Upvotes: 55 | cs. CL, cs. AI Authors: Yang Xiao, Yusong Sun, Haoyi Wu, Wenyang Hui, Weiqiang Hang, Luqing Wang, Zejian Wang, Zirui Tu, Jingsheng Mao, Shaobo Hu, Yang Luo, Jiale Guo, Jin Zhang, Yudan Liu, Zhaokai Luo, Ren Huan, Yao Hu, Chengyue Jiang, Wenjie Li, Chuan Mu Title: Vela: User Behavior as Supervision Sparks LLM Intelligence Arxiv: http://arxiv.org/abs/2610.14709v1 Abstract: Large lan...
DoSA: Dots Sparse Attention 14.10.2026 21:24
🤗 Upvotes: 54 | cs. LG Authors: Fanxu Meng, Taosong Fang Title: DoSA: Dots Sparse Attention Arxiv: http://arxiv.org/abs/2610.13990v1 Abstract: Sparse attention visits few keys, yet finding them can still require scoring every key with every indexer head. We introduce DoSA (Dots Sparse Attention), a lossless method for making this computation conditional while preserving exact token selection. Its...
LeWAM: End-to-End World and Action Modeling with JEPAs 14.10.2026 21:00
🤗 Upvotes: 48 | cs. RO, cs. AI Authors: Minghao Fu, Tavis Siebert, Eryk Halicki, Randall Balestriero Title: LeWAM: End-to-End World and Action Modeling with JEPAs Arxiv: http://arxiv.org/abs/2610.12818v1 Abstract: Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representations and actio...
Jeva: System One Models from First Principles 14.10.2026 22:27
🤗 Upvotes: 40 | cs. AI Authors: Qi Li, Xinchao Wang Title: Jeva: System One Models from First Principles Arxiv: http://arxiv.org/abs/2610.18773v1 Abstract: System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, respect the structure of a decision problem. We argue that current designs underemphasize four...
TPPO: Tracking-Value PPO For Language Model Reasoning 14.10.2026 21:14
🤗 Upvotes: 36 | cs. LG, cs. CL Authors: Futing Wang, Chenxi Li, Jianhao Yan, Yun Luo, Zhi Wang, Yizhuo Li, Runzhe Zhan, Yu Cheng, Tao Lin Title: TPPO: Tracking-Value PPO For Language Model Reasoning Arxiv: http://arxiv.org/abs/2610.16794v1 Abstract: Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning. While \emph{value e...
VideoJev: System One Model for Video Understanding and Interaction 14.10.2026 24:29
🤗 Upvotes: 35 | cs. CV Authors: Yuhan Zhu, Xiangyu Zeng, Qingyi Si, Chenxu Yang, Yifei Li, Luozheng Qin, Junhao Zhou, Dingyu Yao, Jun Zhang, Chuanyu Qin, Zhiqiu Zhang, Yuandong Yang, Yuqi Xu, Tianyu Zhang, Hongcheng Guo, Jiaqi Wang, Nan Duan, Limin Wang Title: VideoJev: System One Model for Video Understanding and Interaction Arxiv: http://arxiv.org/abs/2610.18615v1 Abstract: Visual agents must t...
Harness^2: Recursive Agent Harnessing for an Open World 14.10.2026 22:22
🤗 Upvotes: 34 | cs. AI, cs. CL, cs. LG Authors: Ruiyao Xu, Yanfei Chen, Zhongying CuiZhu, Bhavana Dalvi Mishra, Yifei Ming, Han Yu, Rujun Han, Chen-Yu Lee, Tomas Pfister Title: Harness^2: Recursive Agent Harnessing for an Open World Arxiv: http://arxiv.org/abs/2610.14224v1 Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursiv...
UltraBERT: Beyond the Limit of BERT Pre-Training 14.10.2026 21:32
🤗 Upvotes: 33 | cs. CL, cs. AI Authors: Jungwoo Park, Taewhoo Lee, Jaewoo Kang Title: UltraBERT: Beyond the Limit of BERT Pre-Training Arxiv: http://arxiv.org/abs/2610.15028v1 Abstract: Recent bidirectional encoders have been modernized with innovations from causal large language models (LLMs). However, the pretraining recipe itself remains inherited from small-scale conventions, leaving encoders...
Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics 14.10.2026 21:37
🤗 Upvotes: 33 | cs. LG, cs. AI Authors: Maoqi Liu, Junwei He, Bowen Zhang, Feiran Li, Wentao Ma, Rongyi Lin, Shuhan Zhong, Quan Fang Title: Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics Arxiv: http://arxiv.org/abs/2609.38847v1 Abstract: Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judg...
GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives 13.10.2026 23:42
🤗 Upvotes: 107 | cs. CV, cs. AI, cs. LG, cs. RO Authors: Qize Yu, Lianrui Fan, Boyu Chen, Jiaqi Liang, Xini Ding, Yue Chen, Zetian Song, Yuran Wang, Yi Zou, Kaixuan Wang, Tianxing Chen, Wenxuan Song, Bohan Zhou, Mingleyang Li, Siqiao Huang, Yuqi Ye, Caigao Jiang, Wei Wei, Ruihai Wu, Hang Zhang, Yixiao Ge, Shuchang Zhou, Shilong Liu, Xianming Liu, Ping Luo, Shiyu Huang Title: GroundingPI: A Ground...
Scaling Agentic Data for Long-Horizon Terminal Intelligence 13.10.2026 19:58
🤗 Upvotes: 85 | cs. CL Authors: Shun Zou, Ziyu Ma, Yi Zou, Yong Wang, Lin Chen, Zehui Chen, Guanhua Chen, Xiangxiang Chu, Feng Zhao Title: Scaling Agentic Data for Long-Horizon Terminal Intelligence Arxiv: http://arxiv.org/abs/2610.13166v1 Abstract: Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and t...
AgentSWE: Can Coding Agents Build the Agent You Actually Want? 13.10.2026 21:07
🤗 Upvotes: 70 | cs. SE, cs. AI, cs. LG Authors: Jiahao Wang, Hongjin Qian, Yuyang Hu, Jiajun Zhang, Zheng Liu Title: AgentSWE: Can Coding Agents Build the Agent You Actually Want? Arxiv: http://arxiv.org/abs/2610.13361v1 Abstract: As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows...
Thinking Inertia: LLMs Keep Thinking When Told Not To 13.10.2026 23:02
🤗 Upvotes: 43 | cs. CL Authors: Dianqiao Lei, Kevin Qinghong Lin, Pan Lu, Philip Torr, James Zou Title: Thinking Inertia: LLMs Keep Thinking When Told Not To Arxiv: http://arxiv.org/abs/2610.11765v1 Abstract: Large Language Models (LLMs) increasingly ship with explicit "thinking modes", yet their counterpart, "no-thinking", has received far less attention. We study LLMs' no-thinking behavior alon...
Multi-Stream Attention Residuals 13.10.2026 23:38
🤗 Upvotes: 32 | cs. CL, cs. CV Authors: Xiaohan Qin, Xiangdong Zhang, Tuo Dai, Sunan Zou, Huaijin Wu, Yebin Yang, Zhuo Xia, Lin Yao, Yuliang Liu, Junchi Yan Title: Multi-Stream Attention Residuals Arxiv: http://arxiv.org/abs/2610.13630v1 Abstract: Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the...
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