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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

N'hésitez pas à visiter le site du podcast et à soutenir son créateur : dailypapercast.transistor.fm

Auteur

Jingwen Liang, Gengyu Wang

Catégorie

Science

Dernier épisode

9 oct. 2026

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Épisodes

AgentGarten: Code Worlds for Evolving Agents 09.10.2026

🤗 Upvotes: 96 | cs. CV Authors: Jiawei Chi, Shangchen Miao, Zhiyuan Shi, Kailu Wu, Hanyang Wang, Weiliang Chen, Qiyu Dai, Jinshan Ren, Jun Gao, Mingsheng Long, Yueqi Duan, Jiangran Lyu, Jialong Wu, Fangfu Liu Title: AgentGarten: Code Worlds for Evolving Agents Arxiv: http://arxiv.org/abs/2610.12374v1 Abstract: Interactive virtual worlds allow agents to learn through exploration and interaction. W...

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments? 09.10.2026

🤗 Upvotes: 95 | cs. AI Authors: Yibo Li, Jinhang Qiu, Zhi Zheng, Qianyun Guo, Jiaying Wu, Shuo Ji, Bryan Hooi Title: Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments? Arxiv: http://arxiv.org/abs/2610.08215v2 Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore i...

TokenRouter: Efficient Serving System for Token-Level LLM Routing 09.10.2026

🤗 Upvotes: 84 | cs. CL Authors: Tianyu Fu, Tengxuan Liu, Ruoxi Wang, Yixin Dong, Yi Ge, Yichen You, Yu Wang Title: TokenRouter: Efficient Serving System for Token-Level LLM Routing Arxiv: http://arxiv.org/abs/2610.12242v1 Abstract: Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving. While coarse-grained...

From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation 09.10.2026

🤗 Upvotes: 71 | cs. CL Authors: Quanyu Long, Xiao Chen, Jianda Chen, Haozhen Zhang, Qisheng Hu, Jianzhu Bao, Wenya Wang Title: From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation Arxiv: http://arxiv.org/abs/2610.06100v1 Abstract: Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems...

SuperNav: An Agentic Navigation System for Any Task in Any Scene 09.10.2026

🤗 Upvotes: 59 | cs. RO, cs. CV Authors: Jinkai Zhang, Jingyi Xu, Yuanhong Yu, Jiarui Guo, Ruizhen Hu, Hujun Bao, Xiaowei Zhou, Sida Peng Title: SuperNav: An Agentic Navigation System for Any Task in Any Scene Arxiv: http://arxiv.org/abs/2610.12126v1 Abstract: General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task ge...

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement 09.10.2026

🤗 Upvotes: 43 | cs. CL Authors: Xiaomi LLM-Core Team, Zongming Qiao, Ziyue Hua, Zirui Ou, Zihao Yue, Zihan Jiang, Zhuo Huang, Zhiyang Chen, Zhixian Zheng, Zhipeng Xu, Zhengrui Ma, Yuyang Hu, Yuhang Dong, Yuechen Zhang, Yudong Wang, Yuanxin Liu, Yixin Yang, Yishuo Cai, Yikai Zhao, Yihan Yan, Yifan Zhang, Yifan Song, Xiyu Wei, Xing Zhang, Xin Zhang, Xiaoqian Liu, Xiaodong Ji, Xiangwei Deng, Xueyu G...

In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks 09.10.2026

🤗 Upvotes: 38 | cs. RO Authors: Minxing Li, Minghao Han, Weizhi Zhao, Hanwen Wang, Xiangshuo Liu, Shuyao Shang, Jingxiang Zhou, Mingchao Sun, Hongyu Pan, Mu Xu, Yu Liu, Lue Fan, Zhaoxiang Zhang Title: In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks Arxiv: http://arxiv.org/abs/2609.38173v2 Abstract: We study robotic in-context learning (ICL), an emerging paradig...

Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction 09.10.2026

🤗 Upvotes: 36 | cs. CV, cs. AI Authors: Dahyun Chung, Siyoon Jin, Hyunwook Choi, Honggyu An, Junyoung Seo, Hyunsung Kim, Seung Wook Kim, Seungryong Kim Title: Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction Arxiv: http://arxiv.org/abs/2610.12299v1 Abstract: Egocentric world models predict first-person observations conditioned on an agent's actions, but most focus on a si...

DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training 09.10.2026

🤗 Upvotes: 32 | cs. RO, cs. CV Authors: Junyan Li, Ruizhi Li, Yu Liu, Xiangshuo Liu, Mingchao Sun, Hongyu Pan, Mu Xu, Lue Fan, Zhaoxiang Zhang Title: DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training Arxiv: http://arxiv.org/abs/2610.12468v1 Abstract: We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible vid...

Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching 09.10.2026

🤗 Upvotes: 24 | cs. CV, cs. LG Authors: Luping Liu, Bingyi Kang, Yifan Wang, Dong Xu Title: Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching Arxiv: http://arxiv.org/abs/2610.12421v1 Abstract: Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effective for class...

STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization 08.10.2026

🤗 Upvotes: 84 | cs. CL, cs. AI, cs. LG Authors: Bingchen Yao, Haobo Xu, Haokun Lin, Yichen Wu, Ziyu Guo, Renrui Zhang, Zhichao Lu, Zhenan Sun, Ying Wei Title: STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization Arxiv: http://arxiv.org/abs/2609.38169v1 Abstract: Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states...

Long-WAM: Scaling the Context of World-Action Models 08.10.2026

🤗 Upvotes: 78 | cs. RO, cs. AI, cs. CV Authors: Wei Huang, Bohan Zhang, Chenzhi Liu, Isabella Liu, Shuai Yang, Weian Mao, Luozhou Wang, Yicheng Xiao, Weifeng Lin, Qixin Hu, Bryan Chu, Sifei Liu, Linxi Fan, Xiaojuan Qi, Song Han, Yukang Chen Title: Long-WAM: Scaling the Context of World-Action Models Arxiv: http://arxiv.org/abs/2610.10528v1 Abstract: Real-time robot control demands enough visual h...

nanoMuse: An Open-Source Personal Agent for Every Device You Own 08.10.2026

🤗 Upvotes: 67 | cs. AI Authors: Guangyi Liu, Yong Liu, Jiangning Zhang Title: nanoMuse: An Open-Source Personal Agent for Every Device You Own Arxiv: http://arxiv.org/abs/2610.08699v1 Abstract: Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that l...

Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness 08.10.2026

🤗 Upvotes: 61 | cs. AI, cs. MA, cs. SE Authors: Jiajun Chen, Haoyu Wu, Mingda Jia, Xihui Liu Title: Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness Arxiv: http://arxiv.org/abs/2610.08621v1 Abstract: Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experience for player...

Questioning the Questions: Sustaining Self-Evolution in Reasoning Models 08.10.2026

🤗 Upvotes: 57 | cs. LG, cs. AI, cs. CL Authors: Jinyuan Li, Chengsong Huang, Langlin Huang, Donghong Cai, Shiping Gao, Yuyi Yang, Jiaxin Huang Title: Questioning the Questions: Sustaining Self-Evolution in Reasoning Models Arxiv: http://arxiv.org/abs/2610.04299v2 Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance c...

DecepEval: A Benchmark for Evaluating Deception in LLM Agents 08.10.2026

🤗 Upvotes: 55 | cs. LG Authors: Yiming Xu, Hongyue Yu, Beihua Yang, Zihan Chen, Yixin Liu, Zhen Peng, Bin Shi, Bo Dong, Chao Shen, Irwin King, Qinghua Zheng Title: DecepEval: A Benchmark for Evaluating Deception in LLM Agents Arxiv: http://arxiv.org/abs/2610.07967v1 Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, r...

GRACE: Generation-aware latent compression for efficient video generation 08.10.2026

🤗 Upvotes: 55 | cs. CV Authors: Jiyoung Kim, Paul Hyunbin Cho, Jisu Nam, Donghoon Lee, Hyunsung Go, Yeonkyeong Lee, Hansaem Kim, Seungryong Kim Title: GRACE: Generation-aware latent compression for efficient video generation Arxiv: http://arxiv.org/abs/2610.10524v1 Abstract: Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transfor...

VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction 08.10.2026

🤗 Upvotes: 51 | cs. CV Authors: Qiutong Chen, Yuchan Guo, Zhenlong Yuan, Haobo Yang, Fangfang Lin, Xinyi Long, Yin Wang, Zijian Song, Rui Lan, Shi Qiu, Boyuan Pan, Yang Luo, Yuyin Zhou Title: VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction Arxiv: http://arxiv.org/abs/2610.06293v1 Abstract: Multimodal Large Language Models (MLLMs) have demo...

SGF+: Decoupling Gradient Flows for Autoregressive Video Generation 08.10.2026

🤗 Upvotes: 46 | cs. CV Authors: Zihan Su, Junhao Zhuang, Yaowei Li, Siwen Lu, Haoran Li, Lingen Li, Haoyu Wu, Weiyang Jin, Songchun Zhang, Haoyang Huang, Chun Yuan, Zeyue Xue, Nan Duan Title: SGF+: Decoupling Gradient Flows for Autoregressive Video Generation Arxiv: http://arxiv.org/abs/2610.10429v1 Abstract: Autoregressive video generation requires denoising the current frames while writing thei...

UniWAM: Unified World-Action Model 08.10.2026

🤗 Upvotes: 43 | cs. RO Authors: Wenxuan Song, Jiayi Chen, Jingbo Wang, Shuai Zhou, Xicheng Gong, Zehua Fan, Ziyang Zhou, Junwu E, Haodong Yan, Fuhao Li, Qize Yu, Xu Huang, Pengwei Wang, Wen Chen, Shunbo Zhou, Haoang Li Title: UniWAM: Unified World-Action Model Arxiv: http://arxiv.org/abs/2610.02054v2 Abstract: Vision-language-action models benefit from the understanding and reasoning capabilities...

Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability 07.10.2026

🤗 Upvotes: 77 | cs. CL, cs. AI Authors: Bingxi Hou, Guochao Jiang, Guofeng Quan, Weiqing Li, Wenfeng Feng, Guohua Liu, Yuewei Zhang Title: Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability Arxiv: http://arxiv.org/abs/2610.08448v1 Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different...

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation 07.10.2026

🤗 Upvotes: 52 | cs. CV Authors: Jiahao Zhan, Yan Wang, Yongrui Ma, Qunliang Xing, Ruchang Yao, Runtao Liu, Shijie Zhao, Tianfan Xue Title: DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation Arxiv: http://arxiv.org/abs/2610.03543v1 Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of v...

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models 07.10.2026

🤗 Upvotes: 33 | cs. LG, cs. CL Authors: Xin Wang, Hao Yu, Zhengyang Zhuge, Bochao Mao, Zheng Li, Junda Feng, Yuyan Luo, Yi Zhang, Yizhong Cao, Mi Zhang, Dayiheng Liu, Jianwei Zhang Title: TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models Arxiv: http://arxiv.org/abs/2610.07767v1 Abstract: Reinforcement learning (RL) for post-training large lang...

EVISKILL: Grounding Skill Evolution in Replayable Evidence 07.10.2026

🤗 Upvotes: 30 | cs. AI Authors: Yan Zhou, Yili Wang, Yiwei Dai, Qinggang Zhang, Xin Wang Title: EVISKILL: Grounding Skill Evolution in Replayable Evidence Arxiv: http://arxiv.org/abs/2610.05030v1 Abstract: Continual skill evolution enables LLM agents to accumulate and refine reusable procedural knowledge from interaction experience without updating model parameters. Its effectiveness depends on d...

From Evidence to Action: How Tool-Using Agents Fail 07.10.2026

🤗 Upvotes: 27 | cs. CL, cs. AI Authors: Hongzhan Lin, Shidong Cao, Ziyang Luo, Wenhao Chai, Mong-Li Lee, Wynne Hsu Title: From Evidence to Action: How Tool-Using Agents Fail Arxiv: http://arxiv.org/abs/2610.07753v1 Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We...

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