Tootfinder

🇺🇦 #NowPlaying on KEXP's #SoundsOfSurvivance
Wampums:
🎵 Back and Forth
#Wampums
https://wampums.bandcamp.com/track/back-and-forth
https://open.spotify.com/track/7u7hZz2B6nHd3mlZyBp7ui

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Chuzhan Hao, Wenfeng Feng, Yuewei Zhang, Hao Wang
https://arxiv.org/abs/2507.17365

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Multi-step agentic retrieval systems based on large language models (LLMs) have demonstrated remarkable performance in complex information search tasks. However, these systems still face significant challenges in practical applications, particularly in generating factually inconsistent intermediate queries and inefficient search trajectories, which can lead to reasoning deviations or redundant computations. To address these issues, we propose DynaSearcher, an innovative search agent enhanced by…

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Dexuan He, Xiao Zhou, Wenbin Guan, Liyuan Zhang, Xiaoman Zhang, Sinuo Xu, Ge Wang, Lifeng Wang, Xiaojun Yuan, Xin Sun, Yanfeng Wang, Kun Sun, Ya Zhang, Weidi Xie
https://arxiv.org/abs/2508.15904

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over 70% of cases. While pathology vision-language (VL) foundation models show promising zero-shot capabilities for common cancer subtyping, their clinical performance for rare cancers remains limited. Existing multi-instance learning (MIL) methods rely only on visual features, overlooking cross-modal knowledge and compro…

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Minxi Ouyang, Lianghui Zhu, Yaqing Bao, Qiang Huang, Jingli Ouyang, Tian Guan, Xitong Ling, Jiawen Li, Song Duan, Wenbin Dai, Li Zheng, Xuemei Zhang, Yonghong He
https://arxiv.org/abs/2507.18433

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both data quality and reasoning transparency: pervasive noise and incomplete annotations in public datasets predispose vision language models to factual hallucinations when generating diagnostic text, while the absence of explicit intermediate reasoning chains renders the outputs difficult to audit and thus less trustwort…

Bed, Bath & Bankrupt doesn't have a marketing budget. #wompwompwomp
From: @…
http…

MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
Rui Xu, Tianyang Xue, Qiujie Dong, Le Wan, Zhe Zhu, Peng Li, Zhiyang Dou, Cheng Lin, Shiqing Xin, Yuan Liu, Wenping Wang, Taku Komura
https://arxiv.org/abs/2509.19995

MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottlenecks and limited quantization resolution, primarily due to the large number of tokens required and constrained quantization granularity. These issues prevent faithful reproduction of fine geometric details and structured density patterns. We introduce MeshMosaic, a novel local-to-global framework for artist mesh gen…

THEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Hoyoung Lee, Wonbin Ahn, Suhwan Park, Jaehoon Lee, Minjae Kim, Sungdong Yoo, Taeyoon Lim, Woohyung Lim, Yongjae Lee
https://arxiv.org/abs/2508.16936

THEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Thematic investing aims to construct portfolios aligned with structural trends, yet selecting relevant stocks remains challenging due to overlapping sector boundaries and evolving market dynamics. To address this challenge, we construct the Thematic Representation Set (TRS), an extended dataset that begins with real-world thematic ETFs and expands upon them by incorporating industry classifications and financial news to overcome their coverage limitations. The final dataset contains both the ex…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[2/2]:
- Datasets for Fairness in Language Models: An In-Depth Survey
Jiale Zhang, Zichong Wang, Avash Palikhe, Zhipeng Yin, Wenbin Zhang

Just a tech note: for file transfer within your own network, #Warpinator on Linux, but available for Android and as "winpinator" for Winders, is brilliant: lightening fast and data doesn't go via the web.

DeepSeek releases V3.1, adding a longer context window, with few other details; Chinese local media blames CEO Liang Wenfeng's perfectionism for R2's delay (Bloomberg)
https://www.bloomberg.com/news/articles/2025-08-19…