Tootfinder

Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective
Wen Yang, Junhong Wu, Chong Li, Chengqing Zong, Jiajun Zhang
https://arxiv.org/abs/2510.02272

Parallel Scaling Law: Unveiling Reasoning Generalization through A Cross-Linguistic Perspective
Recent advancements in Reinforcement Post-Training (RPT) have significantly enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased interest in the generalization of RL-based reasoning. While existing work has primarily focused on investigating its generalization across tasks or modalities, this study proposes a novel cross-linguistic perspective to investigate reasoning generalization. This raises a crucial question: $\textit{Does the reasoning capability achieved from E…

DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
Jingkai Sun, Gang Han, Pihai Sun, Wen Zhao, Jiahang Cao, Jiaxu Wang, Yijie Guo, Qiang Zhang
https://arxiv.org/abs/2510.07152

DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
Recent advancements in legged robot perceptive locomotion have shown promising progress. However, terrain-aware humanoid locomotion remains largely constrained to two paradigms: depth image-based end-to-end learning and elevation map-based methods. The former suffers from limited training efficiency and a significant sim-to-real gap in depth perception, while the latter depends heavily on multiple vision sensors and localization systems, resulting in latency and reduced robustness. To overcome …

MMDEW: Multipurpose Multiclass Density Estimation in the Wild
Villanelle O'Reilly, Jonathan Cox, Georgios Leontidis, Marc Hanheide, Petra Bosilj, James Brown
https://arxiv.org/abs/2510.02213

MMDEW: Multipurpose Multiclass Density Estimation in the Wild
Density map estimation can be used to estimate object counts in dense and occluded scenes where discrete counting-by-detection methods fail. We propose a multicategory counting framework that leverages a Twins pyramid vision-transformer backbone and a specialised multi-class counting head built on a state-of-the-art multiscale decoding approach. A two-task design adds a segmentation-based Category Focus Module, suppressing inter-category cross-talk at training time. Training and evaluation on t…

Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model
Chung-Soo Ahn, Rajib Rana, Sunil Sivadas, Carlos Busso, Jagath C. Rajapakse
https://arxiv.org/abs/2510.10078

Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model
Although speech emotion recognition (SER) research has been advanced, thanks to deep learning methods, it still suffers from obtaining inputs from large quality-labelled training data. Data augmentation methods have been attempted to mitigate this issue, generative models have shown success among them recently. We propose a data augmentation framework that is aided by cross-modal information transfer and mutual information regularization. Mutual information based metric can serve as an indicato…

Doc2Query : Topic-Coverage based Document Expansion and its Application to Dense Retrieval via Dual-Index Fusion
Tzu-Lin Kuo, Wei-Ning Chiu, Wei-Yun Ma, Pu-Jen Cheng
https://arxiv.org/abs/2510.09557

Doc2Query++: Topic-Coverage based Document Expansion and its Application to Dense Retrieval via Dual-Index Fusion
Document expansion (DE) via query generation tackles vocabulary mismatch in sparse retrieval, yet faces limitations: uncontrolled generation producing hallucinated or redundant queries with low diversity; poor generalization from in-domain training (e.g., MS MARCO) to out-of-domain data like BEIR; and noise from concatenation harming dense retrieval. While Large Language Models (LLMs) enable cross-domain query generation, basic prompting lacks control, and taxonomy-based methods rely on domain-…