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SSRL: Self-Search Reinforcement Learning
Yuchen Fan, Kaiyan Zhang, Heng Zhou, Yuxin Zuo, Yanxu Chen, Yu Fu, Xinwei Long, Xuekai Zhu, Che Jiang, Yuchen Zhang, Li Kang, Gang Chen, Cheng Huang, Zhizhou He, Bingning Wang, Lei Bai, Ning Ding, Bowen Zhou
https://arxiv.org/abs/2508.10874

SSRL: Self-Search Reinforcement Learning
We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence on costly interactions with external search engines. To this end, we first quantify the intrinsic search capability of LLMs via structured prompting and repeated sampling, which we term Self-Search. Our results reveal that LLMs exhibit strong scaling behavior with respect to the inference budget, achieving high pass@k …

#Canada Charter #Rights turn out to be useless against #police overreach

Tälläistä sitten tekohengitetään kymmenillä miljoonilla tämänkin hallituksen johdolla. Erityismaininta menee Keskustalle, RKP:lle ja KD:lle joille eläinrääkkäyksen tukeminen on ollut tärkeä poliittinen ja ideologinen tavoite eri hallituskokoonpanoissa.
Turkisalan tilastot paljastavat: ala jatkaa romahdustaan https://

Turkisalan tilastot paljastavat: ala jatkaa romahdustaan | Animalia-media
Suomessa kasvatettiin 0,8 miljoonaa turkiseläintä vuonna 2024. Tämä tarkoittaa, että jäljellä on enää 17 prosenttia vuoden 2015 tuotannosta.

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
Yuchen Zhu, Wei Guo, Jaemoo Choi, Guan-Horng Liu, Yongxin Chen, Molei Tao
https://arxiv.org/abs/2508.10684 h…

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π\propto\mathrm{e}^{-U}$ is known up to a normalizing constant, which is an important task in fields such as statistical physics, machine learning, combinatorial optimization, etc. To better address this challenging task when the state space has a large cardinality and the distribution is multi-modal, we propose $\textbf{M}$asked $\textbf{D}$iffusion $\te…

How well can LLMs provide planning feedback in grounded environments?
Yuxuan Li, Victor Zhong
https://arxiv.org/abs/2509.09790 https://arxiv.org/pdf/2509.0…

How well can LLMs provide planning feedback in grounded environments?
Learning to plan in grounded environments typically requires carefully designed reward functions or high-quality annotated demonstrations. Recent works show that pretrained foundation models, such as large language models (LLMs) and vision language models (VLMs), capture background knowledge helpful for planning, which reduces the amount of reward design and demonstrations needed for policy learning. We evaluate how well LLMs and VLMs provide feedback across symbolic, language, and continuous c…

EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
Yanjun Li, Yuqian Fu, Tianwen Qian, Qi'ao Xu, Silong Dai, Danda Pani Paudel, Luc Van Gool, Xiaoling Wang
https://arxiv.org/abs/2508.10729

EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
Recent advances in Multimodal Large Language Models (MLLMs) have significantly pushed the frontier of egocentric video question answering (EgocentricQA). However, existing benchmarks and studies are mainly limited to common daily activities such as cooking and cleaning. In contrast, real-world deployment inevitably encounters domain shifts, where target domains differ substantially in both visual style and semantic content. To bridge this gap, we introduce \textbf{EgoCross}, a comprehensive ben…

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li, Dongmian Zou
https://arxiv.org/abs/2508.10785 https://

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. However, fairness considerations in GAD remain largely underexplored. Indeed, GNN-based GAD models can inherit and amplify biases present in training data, potentially leading to unfair outcomes. While existing efforts have focused on developing fair GNNs, most approaches target…

Exploiting Discriminative Codebook Prior for Autoregressive Image Generation
Longxiang Tang, Ruihang Chu, Xiang Wang, Yujin Han, Pingyu Wu, Chunming He, Yingya Zhang, Shiwei Zhang, Jiaya Jia
https://arxiv.org/abs/2508.10719

Exploiting Discriminative Codebook Prior for Autoregressive Image Generation
Advanced discrete token-based autoregressive image generation systems first tokenize images into sequences of token indices with a codebook, and then model these sequences in an autoregressive paradigm. While autoregressive generative models are trained only on index values, the prior encoded in the codebook, which contains rich token similarity information, is not exploited. Recent studies have attempted to incorporate this prior by performing naive k-means clustering on the tokens, helping to…

FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Yunfeng Zhao, Yixin Liu, Shiyuan Li, Qingfeng Chen, Yu Zheng, Shirui Pan
https://arxiv.org/abs/2508.10594

FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. Despite the current advancements in deep learning-based GAD, existing approaches often suffer from high deployment costs and poor scalability due to their complex and resource-intensive training processes. Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as cr…

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Haoxuan Qu, Yujun Cai, Hossein Rahmani, Ajay Kumar, Junsong Yuan, Jun Liu
https://arxiv.org/abs/2507.11321

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel frame…