Tootfinder

We've updated the What Uses More app to reflect last week's finding by Luccioni and Gamazaychikov that "reasoning" mode increases energy and water usage by 30x. The study casts doubt on the improved efficiency AI companies are claiming for newer models
https://www.

Cold-atom fountain for atom-surface interaction measurements mediated by a near-resonant evanescent light field
Taro Mashimo, Masashi Abe, Athanasios Laliotis, Satoshi Tojo
https://arxiv.org/abs/2511.08115

Much of what I post on here is about Vancouver transit. But today I got to read about (some of) the history of Seattle. I recommend it. And venture to suggest that you might find Transit Sleuth's upcoming material worthy of your attention too.
https://transitsleuth.com/2025/12/06/s

(Some) of Seattle’s Origin Story: From Chief Sealth’s Grace to the Interurban’s Legacy
Seattle’s founding is marked by the arrival of white settlers in 1851, led by Arthur Denny, who exploited the Duwamish people’s land. Despite the settlers’ entitlement, Chief Sealth aided the…

Is #AI really just dumb statistics? "Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles," says the (abstract of the) paper https://arxiv.org/abs/2511.10515: "The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods." Oops ...?

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Davide Maltoni, Matteo Ferrara
https://arxiv.org/abs/2510.09340 https://arxiv.org/…

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Recent large language models have demonstrated relevant capabilities in solving problems that require logical reasoning; however, the corresponding internal mechanisms remain largely unexplored. In this paper, we show that a small language model can solve a deductive reasoning task by learning the underlying rules (rather than operating as a statistical learner). A low-level explanation of its internal representations and computational circuits is then provided. Our findings reveal that inducti…

Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation
Sondos Mahmoud Bsharat, Zhiqiang Shen
https://arxiv.org/abs/2510.09599 https://arxi…

Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation
Large language models (LLMs) have demonstrated impressive reasoning capabilities when provided with chain-of-thought exemplars, but curating large reasoning datasets remains laborious and resource-intensive. In this work, we introduce Prompting Test-Time Scaling (P-TTS), a simple yet effective inference-time data augmentation strategy for enhancing LLM reasoning through finetuning. Rather than collecting thousands or even millions of examples, P-TTS leverages a small pool of only 90 manually se…

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi, Likang Wu, Hongke Zhao, Fei Pan, Qingpeng Cai, Peng Jiang
https://arxiv.org/abs/2510.09167

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcement learning (RL) to model long-term user value. However, these efforts face significant challenges due to the vast, dynamic action spaces inherent in recommendation, which hinder stable policy learning. To resolve this bottleneck, we introduce Hierarchical Semantic RL (HSRL), which reframes RL-based recommendation over…

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic
Manuel Vargas Guzm\'an, Jakub Szymanik, Maciej Malicki
https://arxiv.org/abs/2510.09472 https://

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic
Despite the remarkable progress in neural models, their ability to generalize, a cornerstone for applications like logical reasoning, remains a critical challenge. We delineate two fundamental aspects of this ability: compositionality, the capacity to abstract atomic logical rules underlying complex inferences, and recursiveness, the aptitude to build intricate representations through iterative application of inference rules. In the literature, these two aspects are often confounded together un…

Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang, Junguang Jiang, Bin Liu, Ziru Xu, Han Zhu, Jian Xu, Bo Zheng, Xiang Wang
https://arxiv.org/abs/2510.12211

Reinforced Preference Optimization for Recommendation
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is achieved by generating target items conditioned on historical interactions. Yet current generative recommenders still suffer from two core limitations: the lack of high-quality negative modeling and the reliance on implicit rewards. Reinforcement learning with verifiable rewards (RLVR) offers a natural solution by enabli…

Generative Data Augmentation in Graph Contrastive Learning for Recommendation
Yansong Wang, Qihui Lin, Junjie Huang, Tao Jia
https://arxiv.org/abs/2510.09129 https://

Generative Data Augmentation in Graph Contrastive Learning for Recommendation
Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learning through most existing random data augmentation methods often leads to the alteration of original semantic information. In this paper, w…