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Summarize the top 30 most popular arXiv papers on Reddit, Hacker News and Hugging Face in the last 30 days.

Source: https://github.com/susumuota/arxiv-reddit-summary
Maintained by @ota.bsky.social
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paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2511.02864 3/30 https://arxiv.org/abs/2510.26745 4/30 https://arxiv.org/abs/2511.10647 5/30 https://arxiv.org/abs/2510.24797 6/30 https://arxiv.org/abs/2511.07416 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2511.09030 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2510.18212 11/30 https://arxiv.org/abs/2510.21890 12/30 https://arxiv.org/abs/2510.16888 13/30 https://arxiv.org/abs/2510.22371 14/30 https://arxiv.org/abs/2510.20822 15/30 https://arxiv.org/abs/2510.27190 16/30 https://arxiv.org/abs/2510.26583 17/30 https://arxiv.org/abs/2510.18002 18/30 https://arxiv.org/abs/2510.17800 19/30 https://arxiv.org/abs/2511.04570 20/30 https://arxiv.org/abs/2511.06876 21/30 https://arxiv.org/abs/2511.02743 22/30 https://arxiv.org/abs/2510.19788 23/30 https://arxiv.org/abs/2510.18147 24/30 https://arxiv.org/abs/2510.21614 25/30 https://arxiv.org/abs/2510.23607 26/30 https://arxiv.org/abs/2511.08892 27/30 https://arxiv.org/abs/2511.02208 28/30 https://arxiv.org/abs/2510.23564 29/30 https://arxiv.org/abs/2510.18121 30/30 https://arxiv.org/abs/2511.03276
November 18, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2511.02864 3/30 https://arxiv.org/abs/2510.26745 4/30 https://arxiv.org/abs/2510.24797 5/30 https://arxiv.org/abs/2511.10647 6/30 https://arxiv.org/abs/2511.07416 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2511.09030 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2510.18212 11/30 https://arxiv.org/abs/2510.21890 12/30 https://arxiv.org/abs/2510.22371 13/30 https://arxiv.org/abs/2510.16888 14/30 https://arxiv.org/abs/2510.20822 15/30 https://arxiv.org/abs/2510.27190 16/30 https://arxiv.org/abs/2510.26583 17/30 https://arxiv.org/abs/2510.17800 18/30 https://arxiv.org/abs/2510.18002 19/30 https://arxiv.org/abs/2511.04570 20/30 https://arxiv.org/abs/2511.06876 21/30 https://arxiv.org/abs/2511.02743 22/30 https://arxiv.org/abs/2510.19788 23/30 https://arxiv.org/abs/2510.18147 24/30 https://arxiv.org/abs/2510.23607 25/30 https://arxiv.org/abs/2510.21614 26/30 https://arxiv.org/abs/2511.08892 27/30 https://arxiv.org/abs/2510.23564 28/30 https://arxiv.org/abs/2511.02208 29/30 https://arxiv.org/abs/2510.18121 30/30 https://arxiv.org/abs/2511.03276
November 17, 2025 at 12:06 AM
paper.bsky.social
[27/30] 119 Likes, 1 Comments, 1 Posts
2510.23564, cs․AI | cs․CL | cs․LG, 28 Oct 2025

🆕ReCode: Unify Plan and Action for Universal Granularity Control

Zhaoyang Yu, Jiayi Zhang, Huixue Su, Yufan Zhao, Yifan Wu, Mingyi Deng, Jinyu Xiang, Yizhang Lin, Lingxiao Tang, Yingchao Li, Yuyu Luo, Bang...
Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose ReCode (Recursive Code Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control. The code is available at https://github.com/FoundationAgents/ReCode.
November 17, 2025 at 12:06 AM
paper.bsky.social
[28/30] 117 Likes, 17 Comments, 1 Posts
2511.02208, cs․AI | cs․CL | cs․LG, 04 Nov 2025

🆕Training Proactive and Personalized LLM Agents

Weiwei Sun, Xuhui Zhou, Weihua Du, Xingyao Wang, Sean Welleck, Graham Neubig, Maarten Sap, Yiming Yang
While existing work focuses primarily on task success, we argue that effective real-world agents require optimizing three dimensions: productivity (task completion), proactivity (asking essential questions), and personalization (adapting to diverse user preferences). We introduce UserVille, an interactive environment with LLM-based user simulators enabling diverse, configurable user preferences. Leveraging UserVille, we introduce PPP, a multi-objective reinforcement learning approach that jointly optimizes all three dimensions: Productivity, Proactivity, and Personalization. Experiments on software engineering and deep research tasks show that agents trained with PPP achieve substantial improvements over strong baselines such as GPT-5 (+21.6 on average), demonstrating the ability to ask strategic clarifying questions, adapt to unseen user preferences, and improve task success through better interaction. This work demonstrates that explicitly optimizing for user-centered interaction is critical for building practical and effective AI agents.
November 17, 2025 at 12:06 AM
paper.bsky.social
[30/30] 116 Likes, 15 Comments, 2 Posts
2511.03276, cs․LG, 05 Nov 2025

🆕Diffusion Language Models are Super Data Learners

Jinjie Ni, Qian Liu, Longxu Dou, Chao Du, Zili Wang, Hang Yan, Tianyu Pang, Michael Qizhe Shieh
Under strictly controlled pre-training settings, we observe a Crossover: when unique data is limited, diffusion language models (DLMs) consistently surpass autoregressive (AR) models by training for more epochs. The crossover shifts later with more or higher-quality data, earlier with larger models, and persists across dense and sparse architectures. We attribute the gains to three compounding factors: (1) any-order modeling, (2) super-dense compute from iterative bidirectional denoising, and (3) built-in Monte Carlo augmentation; input or parameter noise improves AR under data constraint but cannot close the gap. At scale, a 1.7B DLM trained with a ~1.5T-token compute budget on 10B unique Python tokens overtakes an AR coder trained with strictly matched settings. In addition, a 1B-parameter DLM achieves > 56% accuracy on HellaSwag and > 33% on MMLU using only 1B tokens, without any special tricks, just by repeating standard pre-training data. We also show that rising validation cross-entropy does not imply degraded downstream performance in this regime.
November 17, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2511.02864 3/30 https://arxiv.org/abs/2510.26745 4/30 https://arxiv.org/abs/2510.24797 5/30 https://arxiv.org/abs/2511.10647 6/30 https://arxiv.org/abs/2510.27688 7/30 https://arxiv.org/abs/2511.07416 8/30 https://arxiv.org/abs/2511.09030 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2510.18212 11/30 https://arxiv.org/abs/2510.21890 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.15742 14/30 https://arxiv.org/abs/2510.16888 15/30 https://arxiv.org/abs/2510.22371 16/30 https://arxiv.org/abs/2510.15831 17/30 https://arxiv.org/abs/2510.20822 18/30 https://arxiv.org/abs/2510.27190 19/30 https://arxiv.org/abs/2510.17800 20/30 https://arxiv.org/abs/2510.26583 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2511.04570 23/30 https://arxiv.org/abs/2510.19788 24/30 https://arxiv.org/abs/2511.06876 25/30 https://arxiv.org/abs/2511.02743 26/30 https://arxiv.org/abs/2510.18147 27/30 https://arxiv.org/abs/2510.21614 28/30 https://arxiv.org/abs/2510.23607 29/30 https://arxiv.org/abs/2510.15444 30/30 https://arxiv.org/abs/2511.08892
November 16, 2025 at 12:08 AM
paper.bsky.social
[30/30] 138 Likes, 10 Comments, 1 Posts
2511.08892, cs․AI, 12 Nov 2025

🆕Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds

Weihao Tan, Xiangyang Li, Yunhao Fang, Heyuan Yao, Shi Yan, Hao Luo, Tenglong Ao, Huihui Li, Hongbin Ren, Bairen Yi, Yujia Qin, Bo An, Libin Liu, Gua...
We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environments. Lumine adopts a human-like interaction paradigm that unifies perception, reasoning, and action in an end-to-end manner, powered by a vision-language model. It processes raw pixels at 5 Hz to produce precise 30 Hz keyboard-mouse actions and adaptively invokes reasoning only when necessary. Trained in Genshin Impact, Lumine successfully completes the entire five-hour Mondstadt main storyline on par with human-level efficiency and follows natural language instructions to perform a broad spectrum of tasks in both 3D open-world exploration and 2D GUI manipulation across collection, combat, puzzle-solving, and NPC interaction. In addition to its in-domain performance, Lumine demonstrates strong zero-shot cross-game generalization. Without any fine-tuning, it accomplishes 100-minute missions in Wuthering Waves and the full five-hour first chapter of Honkai: Star Rail. These promising results highlight Lumine's effectiveness across distinct worlds and interaction dynamics, marking a concrete step toward generalist agents in open-ended environments.
November 16, 2025 at 12:08 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2511.07416 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2510.18212 11/30 https://arxiv.org/abs/2511.09030 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.21890 14/30 https://arxiv.org/abs/2510.15742 15/30 https://arxiv.org/abs/2510.22371 16/30 https://arxiv.org/abs/2510.16888 17/30 https://arxiv.org/abs/2510.15831 18/30 https://arxiv.org/abs/2510.20822 19/30 https://arxiv.org/abs/2510.26583 20/30 https://arxiv.org/abs/2510.27190 21/30 https://arxiv.org/abs/2510.17800 22/30 https://arxiv.org/abs/2510.18002 23/30 https://arxiv.org/abs/2511.04570 24/30 https://arxiv.org/abs/2511.10647 25/30 https://arxiv.org/abs/2510.21614 26/30 https://arxiv.org/abs/2510.19788 27/30 https://arxiv.org/abs/2511.02743 28/30 https://arxiv.org/abs/2511.06876 29/30 https://arxiv.org/abs/2510.18147 30/30 https://arxiv.org/abs/2510.23607
November 15, 2025 at 12:07 AM
paper.bsky.social
[11/30] 338 Likes, 56 Comments, 4 Posts
2511.09030, cs․AI | cs․CL | cs․MA, 12 Nov 2025

🆕Solving a Million-Step LLM Task with Zero Errors

Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz Shahrzad, Olivier Francon, Conor F. Hayes, Xin Qiu, Babak Hodjat, Risto Miikkulainen
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.
November 15, 2025 at 12:07 AM
paper.bsky.social
[24/30] 185 Likes, 24 Comments, 2 Posts
2511.10647, cs․CV, 13 Nov 2025

🆕Depth Anything 3: Recovering the Visual Space from Any Views

Haotong Lin, Sili Chen, Junhao Liew, Donny Y. Chen, Zhenyu Li, Guang Shi, Jiashi Feng, Bingyi Kang
We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain transformer (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2). We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 44.3% in camera pose accuracy and 25.1% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.
November 15, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2511.07416 9/30 https://arxiv.org/abs/2511.02824 10/30 https://arxiv.org/abs/2510.25741 11/30 https://arxiv.org/abs/2510.18212 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.21890 14/30 https://arxiv.org/abs/2510.13999 15/30 https://arxiv.org/abs/2510.15742 16/30 https://arxiv.org/abs/2510.16888 17/30 https://arxiv.org/abs/2510.22371 18/30 https://arxiv.org/abs/2510.20822 19/30 https://arxiv.org/abs/2510.15831 20/30 https://arxiv.org/abs/2510.27190 21/30 https://arxiv.org/abs/2510.26583 22/30 https://arxiv.org/abs/2510.17800 23/30 https://arxiv.org/abs/2510.18002 24/30 https://arxiv.org/abs/2511.04570 25/30 https://arxiv.org/abs/2510.19788 26/30 https://arxiv.org/abs/2511.02743 27/30 https://arxiv.org/abs/2510.18147 28/30 https://arxiv.org/abs/2510.21614 29/30 https://arxiv.org/abs/2511.06876 30/30 https://arxiv.org/abs/2510.23607
November 14, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2511.07416 8/30 https://arxiv.org/abs/2510.27688 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2511.02824 11/30 https://arxiv.org/abs/2510.18212 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.13999 14/30 https://arxiv.org/abs/2510.21890 15/30 https://arxiv.org/abs/2510.15742 16/30 https://arxiv.org/abs/2510.22371 17/30 https://arxiv.org/abs/2510.16888 18/30 https://arxiv.org/abs/2510.15831 19/30 https://arxiv.org/abs/2510.20822 20/30 https://arxiv.org/abs/2510.17800 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2510.26583 23/30 https://arxiv.org/abs/2511.04570 24/30 https://arxiv.org/abs/2510.27190 25/30 https://arxiv.org/abs/2510.19788 26/30 https://arxiv.org/abs/2511.02743 27/30 https://arxiv.org/abs/2510.21614 28/30 https://arxiv.org/abs/2510.18147 29/30 https://arxiv.org/abs/2510.23607 30/30 https://arxiv.org/abs/2510.12954
November 13, 2025 at 12:07 AM
paper.bsky.social
[24/30] 183 Likes, 21 Comments, 2 Posts
2510.27190, cs․CR | cs․AI, 03 Nov 2025

🆕Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures

Dominik Schwarz
As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs. The analysis shows that inputs are often interpreted non-neutrally and can trigger implementation-shaped responses or unintended state changes even without explicit commands. We argue that these behaviors constitute architectural failure modes and that string-level filtering alone is insufficient. To mitigate such cross-stage vulnerabilities, we recommend zero-trust architectural principles, including provenance enforcement, context sealing, and plan revalidation, and we introduce "Countermind" as a conceptual blueprint for implementing these defenses.
November 13, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2510.25741 9/30 https://arxiv.org/abs/2510.18212 10/30 https://arxiv.org/abs/2511.02824 11/30 https://arxiv.org/abs/2511.07416 12/30 https://arxiv.org/abs/2510.13999 13/30 https://arxiv.org/abs/2510.15511 14/30 https://arxiv.org/abs/2510.21890 15/30 https://arxiv.org/abs/2510.15742 16/30 https://arxiv.org/abs/2510.22371 17/30 https://arxiv.org/abs/2510.16888 18/30 https://arxiv.org/abs/2510.15831 19/30 https://arxiv.org/abs/2510.20822 20/30 https://arxiv.org/abs/2510.17800 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2510.11696 23/30 https://arxiv.org/abs/2511.04570 24/30 https://arxiv.org/abs/2511.02743 25/30 https://arxiv.org/abs/2510.19788 26/30 https://arxiv.org/abs/2510.18147 27/30 https://arxiv.org/abs/2510.21614 28/30 https://arxiv.org/abs/2510.23607 29/30 https://arxiv.org/abs/2510.12954 30/30 https://arxiv.org/abs/2510.26583
November 12, 2025 at 12:06 AM
paper.bsky.social
[11/30] 313 Likes, 11 Comments, 4 Posts
2511.07416, cs․RO | cs․AI | cs․CV, 11 Nov 2025

🆕Robot Learning from a Physical World Model

Jiageng Mao, Sicheng He, Hao-Ning Wu, Yang You, Shuyang Sun, Zhicheng Wang, Yanan Bao, Huizhong Chen, Leonidas Guibas, Vitor Guizilini, Howard Zhou, Yue Wang
We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic visual demonstrations from language commands and images, offering a powerful yet underexplored source of training signals for robotics. However, directly retargeting pixel motions from generated videos to robots neglects physics, often resulting in inaccurate manipulations. PhysWorld addresses this limitation by coupling video generation with physical world reconstruction. Given a single image and a task command, our method generates task-conditioned videos and reconstructs the underlying physical world from the videos, and the generated video motions are grounded into physically accurate actions through object-centric residual reinforcement learning with the physical world model. This synergy transforms implicit visual guidance into physically executable robotic trajectories, eliminating the need for real robot data collection and enabling zero-shot generalizable robotic manipulation. Experiments on diverse real-world tasks demonstrate that PhysWorld substantially improves manipulation accuracy compared to previous approaches. Visit \href{https://pointscoder.github.io/PhysWorld_Web/}{the project webpage} for details.
November 12, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2510.25741 9/30 https://arxiv.org/abs/2510.18212 10/30 https://arxiv.org/abs/2510.15511 11/30 https://arxiv.org/abs/2510.13999 12/30 https://arxiv.org/abs/2510.15742 13/30 https://arxiv.org/abs/2510.21890 14/30 https://arxiv.org/abs/2510.22371 15/30 https://arxiv.org/abs/2510.16888 16/30 https://arxiv.org/abs/2510.15831 17/30 https://arxiv.org/abs/2510.20822 18/30 https://arxiv.org/abs/2511.02824 19/30 https://arxiv.org/abs/2510.11696 20/30 https://arxiv.org/abs/2510.17800 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2511.02743 23/30 https://arxiv.org/abs/2510.19788 24/30 https://arxiv.org/abs/2511.04570 25/30 https://arxiv.org/abs/2510.18147 26/30 https://arxiv.org/abs/2510.23607 27/30 https://arxiv.org/abs/2510.12954 28/30 https://arxiv.org/abs/2510.21614 29/30 https://arxiv.org/abs/2510.26583 30/30 https://arxiv.org/abs/2510.11690
November 11, 2025 at 12:06 AM
paper.bsky.social
[18/30] 212 Likes, 125 Comments, 2 Posts
2511.02824, cs․AI, 05 Nov 2025

🆕Kosmos: An AI Scientist for Autonomous Discovery

Ludovico Mitchener, Angela Yiu, Benjamin Chang, Mathieu Bourdenx, Tyler Nadolski, Arvis Sulovari, Eric C. Landsness, Daniel L. Barabasi, Siddharth Narayanan, Nicky Evans, ...
Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that can automate scientific research, but all such agents remain limited in the number of actions they can take before losing coherence, thus limiting the depth of their findings. Here we present Kosmos, an AI scientist that automates data-driven discovery. Given an open-ended objective and a dataset, Kosmos runs for up to 12 hours performing cycles of parallel data analysis, literature search, and hypothesis generation before synthesizing discoveries into scientific reports. Unlike prior systems, Kosmos uses a structured world model to share information between a data analysis agent and a literature search agent. The world model enables Kosmos to coherently pursue the specified objective over 200 agent rollouts, collectively executing an average of 42,000 lines of code and reading 1,500 papers per run. Kosmos cites all statements in its reports with code or primary literature, ensuring its reasoning is traceable. Independent scientists found 79.4% of statements in Kosmos reports to be accurate, and collaborators reported that a single 20-cycle Kosmos run performed the equivalent of 6 months of their own research time on average. Furthermore, collaborators reported that the number of valuable scientific findings generated scales linearly with Kosmos cycles (tested up to 20 cycles). We highlight seven discoveries made by Kosmos that span metabolomics, materials science, neuroscience, and statistical genetics. Three discoveries independently reproduce findings from preprinted or unpublished manuscripts that were not accessed by Kosmos at runtime, while four make novel contributions to the scientific literature.
November 11, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2510.25741 9/30 https://arxiv.org/abs/2510.18212 10/30 https://arxiv.org/abs/2510.15511 11/30 https://arxiv.org/abs/2510.13999 12/30 https://arxiv.org/abs/2510.15742 13/30 https://arxiv.org/abs/2510.22371 14/30 https://arxiv.org/abs/2510.16888 15/30 https://arxiv.org/abs/2510.15831 16/30 https://arxiv.org/abs/2510.20822 17/30 https://arxiv.org/abs/2510.17800 18/30 https://arxiv.org/abs/2510.18002 19/30 https://arxiv.org/abs/2510.11696 20/30 https://arxiv.org/abs/2510.21890 21/30 https://arxiv.org/abs/2510.19788 22/30 https://arxiv.org/abs/2511.02743 23/30 https://arxiv.org/abs/2510.18147 24/30 https://arxiv.org/abs/2510.23607 25/30 https://arxiv.org/abs/2510.26583 26/30 https://arxiv.org/abs/2510.12954 27/30 https://arxiv.org/abs/2510.21614 28/30 https://arxiv.org/abs/2511.04570 29/30 https://arxiv.org/abs/2510.11690 30/30 https://arxiv.org/abs/2510.15444
November 10, 2025 at 12:06 AM
paper.bsky.social
[20/30] 183 Likes, 14 Comments, 3 Posts
2510.21890, cs․LG | cs․AI | cs․GR, 24 Oct 2025

🆕The Principles of Diffusion Models

Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon
This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas. Diffusion modeling starts by defining a forward process that gradually corrupts data into noise, linking the data distribution to a simple prior through a continuum of intermediate distributions. The goal is to learn a reverse process that transforms noise back into data while recovering the same intermediates. We describe three complementary views. The variational view, inspired by variational autoencoders, sees diffusion as learning to remove noise step by step. The score-based view, rooted in energy-based modeling, learns the gradient of the evolving data distribution, indicating how to nudge samples toward more likely regions. The flow-based view, related to normalizing flows, treats generation as following a smooth path that moves samples from noise to data under a learned velocity field. These perspectives share a common backbone: a time-dependent velocity field whose flow transports a simple prior to the data. Sampling then amounts to solving a differential equation that evolves noise into data along a continuous trajectory. On this foundation, the monograph discusses guidance for controllable generation, efficient numerical solvers, and diffusion-motivated flow-map models that learn direct mappings between arbitrary times. It provides a conceptual and mathematically grounded understanding of diffusion models for readers with basic deep-learning knowledge.
November 10, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.27688 8/30 https://arxiv.org/abs/2510.25741 9/30 https://arxiv.org/abs/2510.18212 10/30 https://arxiv.org/abs/2510.13999 11/30 https://arxiv.org/abs/2510.15511 12/30 https://arxiv.org/abs/2510.15742 13/30 https://arxiv.org/abs/2510.22371 14/30 https://arxiv.org/abs/2510.16888 15/30 https://arxiv.org/abs/2510.09272 16/30 https://arxiv.org/abs/2510.15831 17/30 https://arxiv.org/abs/2510.20822 18/30 https://arxiv.org/abs/2510.17800 19/30 https://arxiv.org/abs/2510.18002 20/30 https://arxiv.org/abs/2510.11696 21/30 https://arxiv.org/abs/2510.19788 22/30 https://arxiv.org/abs/2510.23607 23/30 https://arxiv.org/abs/2510.18147 24/30 https://arxiv.org/abs/2510.26583 25/30 https://arxiv.org/abs/2510.12954 26/30 https://arxiv.org/abs/2510.21614 27/30 https://arxiv.org/abs/2510.11690 28/30 https://arxiv.org/abs/2511.02743 29/30 https://arxiv.org/abs/2510.15444 30/30 https://arxiv.org/abs/2511.04570
November 9, 2025 at 12:06 AM
paper.bsky.social
[28/30] 147 Likes, 43 Comments, 1 Posts
2511.02743, cs․DC, 04 Nov 2025

🆕Making Democracy Work: Fixing and Simplifying Egalitarian Paxos (Extended Version)

Fedor Ryabinin, Alexey Gotsman, Pierre Sutra
Classical state-machine replication protocols, such as Paxos, rely on a distinguished leader process to order commands. Unfortunately, this approach makes the leader a single point of failure and increases the latency for clients that are not co-located with it. As a response to these drawbacks, Egalitarian Paxos introduced an alternative, leaderless approach, that allows replicas to order commands collaboratively. Not relying on a single leader allows the protocol to maintain non-zero throughput with up to $f$ crashes of any processes out of a total of $n = 2f+1$. The protocol furthermore allows any process to execute a command $c$ fast, in $2$ message delays, provided no more than $e = \lceil\frac{f+1}{2}\rceil$ other processes fail, and all concurrently submitted commands commute with $c$; the latter condition is often satisfied in practical systems. Egalitarian Paxos has served as a foundation for many other replication protocols. But unfortunately, the protocol is very complex, ambiguously specified and suffers from nontrivial bugs. In this paper, we present EPaxos* -- a simpler and correct variant of Egalitarian Paxos. Our key technical contribution is a simpler failure-recovery algorithm, which we have rigorously proved correct. Our protocol also generalizes Egalitarian Paxos to cover the whole spectrum of failure thresholds $f$ and $e$ such that $n \ge \max\{2e+f-1, 2f+1\}$ -- the number of processes that we show to be optimal.
November 9, 2025 at 12:06 AM
paper.bsky.social
[30/30] 145 Likes, 2 Comments, 1 Posts
2511.04570, cs․CV | cs․CL, 06 Nov 2025

🆕Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm

Jingqi Tong, Yurong Mou, Hangcheng Li, Mingzhe Li, Yongzhuo Yang, Ming Zhang, Qiguang Chen, Tianyi Liang, Xiaomeng Hu, Yining Zhen...
"Thinking with Text" and "Thinking with Images" paradigm significantly improve the reasoning ability of large language models (LLMs) and Vision Language Models (VLMs). However, these paradigms have inherent limitations. (1) Images capture only single moments and fail to represent dynamic processes or continuous changes, and (2) The separation of text and vision as distinct modalities, hindering unified multimodal understanding and generation. To overcome these limitations, we introduce "Thinking with Video", a new paradigm that leverages video generation models, such as Sora-2, to bridge visual and textual reasoning in a unified temporal framework. To support this exploration, we developed the Video Thinking Benchmark (VideoThinkBench). VideoThinkBench encompasses two task categories: (1) vision-centric tasks (e.g., Eyeballing Puzzles), and (2) text-centric tasks (e.g., subsets of GSM8K, MMMU). Our evaluation establishes Sora-2 as a capable reasoner. On vision-centric tasks, Sora-2 is generally comparable to state-of-the-art (SOTA) VLMs, and even surpasses VLMs on several tasks, such as Eyeballing Games. On text-centric tasks, Sora-2 achieves 92% accuracy on MATH, and 75.53% accuracy on MMMU. Furthermore, we systematically analyse the source of these abilities. We also find that self-consistency and in-context learning can improve Sora-2's performance. In summary, our findings demonstrate that the video generation model is the potential unified multimodal understanding and generation model, positions "thinking with video" as a unified multimodal reasoning paradigm.
November 9, 2025 at 12:05 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2511.02864 4/30 https://arxiv.org/abs/2510.15061 5/30 https://arxiv.org/abs/2510.26745 6/30 https://arxiv.org/abs/2510.24797 7/30 https://arxiv.org/abs/2510.08558 8/30 https://arxiv.org/abs/2510.27688 9/30 https://arxiv.org/abs/2510.25741 10/30 https://arxiv.org/abs/2510.18212 11/30 https://arxiv.org/abs/2510.13999 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.15742 14/30 https://arxiv.org/abs/2510.16888 15/30 https://arxiv.org/abs/2510.22371 16/30 https://arxiv.org/abs/2510.09272 17/30 https://arxiv.org/abs/2510.15831 18/30 https://arxiv.org/abs/2510.20822 19/30 https://arxiv.org/abs/2510.17800 20/30 https://arxiv.org/abs/2510.11696 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2510.19788 23/30 https://arxiv.org/abs/2510.23607 24/30 https://arxiv.org/abs/2510.26583 25/30 https://arxiv.org/abs/2510.12954 26/30 https://arxiv.org/abs/2510.21614 27/30 https://arxiv.org/abs/2510.18147 28/30 https://arxiv.org/abs/2510.11690 29/30 https://arxiv.org/abs/2510.15444 30/30 https://arxiv.org/abs/2510.12276
November 8, 2025 at 12:06 AM
paper.bsky.social
[27/30] 165 Likes, 32 Comments, 1 Posts
2510.18147, cs․CL, 20 Oct 2025

🆕LLMs Encode How Difficult Problems Are

William Lugoloobi, Chris Russell
Large language models exhibit a puzzling inconsistency: they solve complex problems yet frequently fail on seemingly simpler ones. We investigate whether LLMs internally encode problem difficulty in a way that aligns with human judgment, and whether this representation tracks generalization during reinforcement learning post-training. We train linear probes across layers and token positions on 60 models, evaluating on mathematical and coding subsets of Easy2HardBench. We find that human-labeled difficulty is strongly linearly decodable (AMC: $\rho \approx 0.88$) and exhibits clear model-size scaling, whereas LLM-derived difficulty is substantially weaker and scales poorly. Steering along the difficulty direction reveals that pushing models toward "easier" representations reduces hallucination and improves accuracy. During GRPO training on Qwen2.5-Math-1.5B, the human-difficulty probe strengthens and positively correlates with test accuracy across training steps, while the LLM-difficulty probe degrades and negatively correlates with performance. These results suggest that human annotations provide a stable difficulty signal that RL amplifies, while automated difficulty estimates derived from model performance become misaligned precisely as models improve. We release probe code and evaluation scripts to facilitate replication.
November 8, 2025 at 12:06 AM
paper.bsky.social
Top 30 most popular arXiv papers in the last 30 days.
[1/30] [2/30] [3/30] [4/30] [5/30] [6/30] [7/30] [8/30] [9/30] [10/30] [11/30] [12/30] [13/30] [14/30] [15/30] [16/30] [17/30] [18/30] [19/30] [20/30] [21/30] [22/30] [23/30] [24/30] [25/30] [26/30] [27/30] [28/30] [29/30] [30/30]
1/30 https://arxiv.org/abs/2510.26692 2/30 https://arxiv.org/abs/2510.14876 3/30 https://arxiv.org/abs/2510.15061 4/30 https://arxiv.org/abs/2510.26745 5/30 https://arxiv.org/abs/2510.24797 6/30 https://arxiv.org/abs/2511.02864 7/30 https://arxiv.org/abs/2510.08558 8/30 https://arxiv.org/abs/2510.25741 9/30 https://arxiv.org/abs/2510.18212 10/30 https://arxiv.org/abs/2510.27688 11/30 https://arxiv.org/abs/2510.13999 12/30 https://arxiv.org/abs/2510.15511 13/30 https://arxiv.org/abs/2510.15742 14/30 https://arxiv.org/abs/2510.22371 15/30 https://arxiv.org/abs/2510.16888 16/30 https://arxiv.org/abs/2510.09272 17/30 https://arxiv.org/abs/2510.20822 18/30 https://arxiv.org/abs/2510.15831 19/30 https://arxiv.org/abs/2510.11696 20/30 https://arxiv.org/abs/2510.17800 21/30 https://arxiv.org/abs/2510.18002 22/30 https://arxiv.org/abs/2510.19788 23/30 https://arxiv.org/abs/2510.23607 24/30 https://arxiv.org/abs/2510.12954 25/30 https://arxiv.org/abs/2510.26583 26/30 https://arxiv.org/abs/2510.21614 27/30 https://arxiv.org/abs/2510.11690 28/30 https://arxiv.org/abs/2510.15444 29/30 https://arxiv.org/abs/2510.12276 30/30 https://arxiv.org/abs/2510.11328
November 7, 2025 at 12:06 AM