Tootfinder

heise meets … Die Einführung der E-Rechnung ist nicht so schlimm
Bei der Einführung der E-Rechnung wurde die Chance verpasst, einen verbindlichen Standard festzulegen. Cross Industry Invoice (CII) wäre eine gute Wahl gewesen.

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Bin Hong, Jiayu Liu, Zhenya Huang, Kai Zhang, Mengdi Zhang
https://arxiv.org/abs/2508.10164

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs increase computational costs and may lead to overthinking, raising challenges in balancing reasoning effectiveness and efficiency. Current methods for efficient reasoning often compromise reasoning quality or require extensive resources. This paper investigates efficient methods to reduce the generation length of LRM…

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
LG AI Research, :, Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Yemuk Choi, Kyubeen Han, Seokhee Hong, Junwon Hwang, Taewan Hwang, Joonwon Jang, Hyojin Jeon, Kijeong Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, Euisoon Kim, Hyosang Kim, Jihoon Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Youchul Kim, Edward Hwayoung Lee, Gwangho Lee, Ha…

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimi…

VGR: Visual Grounded Reasoning
Jiacong Wang, Zijiang Kang, Haochen Wang, Haiyong Jiang, Jiawen Li, Bohong Wu, Ya Wang, Jiao Ran, Xiao Liang, Chao Feng, Jun Xiao
https://arxiv.org/abs/2506.11991

VGR: Visual Grounded Reasoning
In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias and is largely confined to math or science domains. This narrow focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper introduces VGR, a novel reasoning multimodal large language model (MLLM) with enhanced …

Nextdoor unveils new features like an interactive neighborhood map called Alerts, partners with 3,500 local news outlets, and more, to revitalize its app (Mia Sato/The Verge)
https://www.theverge.com/news/706618/nextdoor-redesign-ai-reco…

Can Nextdoor overhaul itself to be useful?
Neighborhood app Nextdoor is adding local news content, an AI-powered recommendations chatbot, and a map of emergency events.

Kremlin: Russian-American dialogue to continue despite diplomatic tensions: https://benborges.xyz/2025/07/14/kremlin-russianamerican-dialogue-to-continue.html

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, Wei Lin
https://arxiv.org/abs/2508.10293 ht…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency. Existing efficient reasoning methods typically require accurate task assessment to preset token budgets or select reasoning modes, which limits their flexibility and reliability. In this work, we revisit the essence of…

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Minhao Wang, Yunhang He, Cong Xu, Zhangchi Zhu, Wei Zhang
https://arxiv.org/abs/2508.10312

Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate through LLM backbones layer by layer, as opposed to tradition…

STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Zhenye Yang, Jinpeng Chen, Huan Li, Xiongnan Jin, Xuanyang Li, Junwei Zhang, Hongbo Gao, Kaimin Wei, Senzhang Wang
https://arxiv.org/abs/2508.10669

STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph…

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Yanjian Zhang, Guillaume Wisniewski, Nadi Tomeh, Thierry Charnois
https://arxiv.org/abs/2507.11423

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Human reasoning involves different strategies, each suited to specific problems. Prior work shows that large language model (LLMs) tend to favor a single reasoning strategy, potentially limiting their effectiveness in diverse reasoning challenges. In this work, we investigate whether prompting can control LLMs reasoning strategies and assess its impact on logical problem-solving. While our experiments show that no single strategy consistently improves accuracy, performance could be enhanced if …