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Joy (@joyousjoyness@mastodon.social)
Attached: 1 image ✨ A Sprinkle of JoyousJoyness ✨ MLEM! Have a JoyousJoyfulJoyness day! #happy #cat #mlem #cute #joyousjoyness #funny

Oh My. Borderline Erotic Sit-Ups
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Season 5 Episode 13 "Rubber Ducky"
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FreeViS: Training-free Video Stylization with Inconsistent References
Jiacong Xu, Yiqun Mei, Ke Zhang, Vishal M. Patel
https://arxiv.org/abs/2510.01686 https://

FreeViS: Training-free Video Stylization with Inconsistent References
Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our…

SafeProtein: Red-Teaming Framework and Benchmark for Protein Foundation Models
Jigang Fan, Zhenghong Zhou, Ruofan Jin, Le Cong, Mengdi Wang, Zaixi Zhang
https://arxiv.org/abs/2509.03487

SafeProtein: Red-Teaming Framework and Benchmark for Protein Foundation Models
Proteins play crucial roles in almost all biological processes. The advancement of deep learning has greatly accelerated the development of protein foundation models, leading to significant successes in protein understanding and design. However, the lack of systematic red-teaming for these models has raised serious concerns about their potential misuse, such as generating proteins with biological safety risks. This paper introduces SafeProtein, the first red-teaming framework designed for prote…

HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution
Jinzhou Tang, Jusheng Zhang, Qinhan Lv, Sidi Liu, Jing Yang, Chengpei Tang, Keze Wang
https://arxiv.org/abs/2509.00189

HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution
Autonomous agents play a crucial role in advancing Artificial General Intelligence, enabling problem decomposition and tool orchestration through Large Language Models (LLMs). However, existing paradigms face a critical trade-off. On one hand, reusable fixed workflows require manual reconfiguration upon environmental changes; on the other hand, flexible reactive loops fail to distill reasoning progress into transferable structures. We introduce Hierarchical Variable Agent (HiVA), a novel framew…

Activation-Guided Local Editing for Jailbreaking Attacks
Jiecong Wang, Haoran Li, Hao Peng, Ziqian Zeng, Zihao Wang, Haohua Du, Zhengtao Yu
https://arxiv.org/abs/2508.00555 http…

Activation-Guided Local Editing for Jailbreaking Attacks
Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbreak methods face significant drawbacks. Token-level jailbreak attacks often produce incoherent or unreadable inputs and exhibit poor transferability, while prompt-level attacks lack scalability and rely heavily on manual effort and human ingenuity. We propose a concise and effective two-stage framework that combines the advantages of these approaches. The …

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Yicong Zhao, Shisong Chen, Jiacheng Zhang, Zhixu Li
https://arxiv.org/abs/2509.02330 htt…

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Recent advances in large language models (LLMs) have demonstrated impressive capabilities in code-related tasks, such as code generation and automated program repair. Despite their promising performance, most existing approaches for code repair suffer from high training costs or computationally expensive inference. Retrieval-augmented generation (RAG), with its efficient in-context learning paradigm, offers a more scalable alternative. However, conventional retrieval strategies, which are often…

FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring
Xiaoyang Liu, Zhengyan Zhou, Zihang Xu, Jiezhang Cao, Zheng Chen, Yulun Zhang
https://arxiv.org/abs/2510.01641

FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring
Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in true-world modeling, have shown great promise for high-quality image restoration tasks such as deblurring, demonstrating stronger generative capabilities than CNN and transformer-based methods. However, challenges such as unbearable inference time and compromised fidelity still limit the full potential of the diffusion model…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/10]:
- Panorama: Fast-Track Nearest Neighbors
Vansh Ramani, Alexis Schlomer, Akash Nayar, Panagiotis Karras, Sayan Ranu, Jignesh M. Patel

When Tracking Fails: Analyzing Failure Modes of SAM2 for Point-Based Tracking in Surgical Videos
Woowon Jang, Jiwon Im, Juseung Choi, Niki Rashidian, Wesley De Neve, Utku Ozbulak
https://arxiv.org/abs/2510.02100

When Tracking Fails: Analyzing Failure Modes of SAM2 for Point-Based Tracking in Surgical Videos
Video object segmentation (VOS) models such as SAM2 offer promising zero-shot tracking capabilities for surgical videos using minimal user input. Among the available input types, point-based tracking offers an efficient and low-cost alternative, yet its reliability and failure cases in complex surgical environments are not well understood. In this work, we systematically analyze the failure modes of point-based tracking in laparoscopic cholecystectomy videos. Focusing on three surgical targets,…