Tootfinder

looool eric fuckin' adams
https://patch.com/new-york/washington-heights-inwood/s/jjc29/mamdani-leading-nyc-mayor-race-by-nearly-20-points-new-poll-finds?

Retrospective Sparse Attention for Efficient Long-Context Generation
Seonghwan Choi, Beomseok Kang, Dongwon Jo, Jae-Joon Kim
https://arxiv.org/abs/2508.09001 https://

Retrospective Sparse Attention for Efficient Long-Context Generation
Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, inference over extended contexts is bottlenecked by the Key-Value (KV) cache, whose memory footprint grows linearly with sequence length and dominates latency at each decoding step. While recent KV cache compression methods identify and load important tokens, they focus predominantly on input contexts and fail to address the cumulative attention erro…

Push or Light: Nudging Standing to Break Prolonged Sitting
Sohshi Yoshida, Ko Watanabe, Andreas Dengel, Shoya Ishimaru, Shingo Ata, Manato Fujimoto
https://arxiv.org/abs/2507.08659

Push or Light: Nudging Standing to Break Prolonged Sitting
Prolonged sitting is a health risk leading to metabolic and cardiovascular diseases. To combat this, various "nudging" strategies encourage stand-ups. Behavior change triggers use explicit prompts such as smartphone push notifications or light controls. However, comparisons of the effects of such interactions, discomfort, and user context have not yet been performed. The present study evaluated these methods in a mixed design experiment with 15 college students. Three intervention methods (none…

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
Yuvraj Dutta, Soumyajit Chatterjee, Sandip Chakraborty, Basabdatta Palit
https://arxiv.org/abs/2508.08479

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. …

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Yuchen Tian, Kaixin Li, Hao Chen, Ziyang Luo, Hongzhan Lin, Sebastian Schelter, Lun Du, Jing Ma
https://arxiv.org/abs/2508.09631

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Large Language Models (LLMs) have recently demonstrated strong capabilities in translating natural language into database queries, especially when dealing with complex graph-structured data. However, real-world queries often contain inherent ambiguities, and the interconnected nature of graph structures can amplify these challenges, leading to unintended or incorrect query results. To systematically evaluate LLMs on this front, we propose a taxonomy of graph-query ambiguities, comprising three …

Corporate reports miss the mark on ocean health, according to new analysis #ocean

Corporate reports miss the mark on ocean health, according to new analysis
New research led by Stanford University and co-authored by Lancaster University explores the industrial impacts on the ocean and compares them with what leading companies in the ocean economy disclose, underscoring the need for greater transparency and accountability.

Solving Approximation Tasks with Greedy Deep Kernel Methods
Marian Klink, Tobias Ehring, Robin Herkert, Robin Lautenschlager, Dominik G\"oddeke, Bernard Haasdonk
https://arxiv.org/abs/2508.08759

Solving Approximation Tasks with Greedy Deep Kernel Methods
Kernel methods are versatile tools for function approximation and surrogate modeling. In particular, greedy techniques offer computational efficiency and reliability through inherent sparsity and provable convergence. Inspired by the success of deep neural networks and structured deep kernel networks, we consider deep, multilayer kernels for greedy approximation. This multilayer structure, consisting of linear kernel layers and optimizable kernel activation function layers in an alternating fas…

Nonlinear dynamics of reaction-diffusion wave trains under large and fully nonlocalized modulations
Joannis Alexopoulos, Bj\"orn de Rijk
https://arxiv.org/abs/2508.08637 ht…

Nonlinear dynamics of reaction-diffusion wave trains under large and fully nonlocalized modulations
We study the dynamics of periodic wave trains in reaction-diffusion systems on the real line under large, fully nonlocalized modulations. We prove that solutions with nearby initial data converge, at an enhanced diffusive rate, to a modulated wave train whose leading-order phase and wavenumber dynamics are governed by an explicit solution to the viscous Hamilton-Jacobi equation. This constitutes a global stability result: such initial data are generally not close to the large-time modulated wav…

Toward the "platinum standard" of quantum chemistry on quantum computers: perturbative quadruple corrections in unitary coupled cluster theory
Zachary W. Windom, Luke Bertels, Daniel Claudino, Rodney J. Bartlett
https://arxiv.org/abs/2508.09840

Toward the "platinum standard" of quantum chemistry on quantum computers: perturbative quadruple corrections in unitary coupled cluster theory
We propose a non-iterative, post hoc correction to the unitary coupled cluster theory with single, double, and triple excitations (UCCSDT) ansatz, which considers the leading-order effects of neglected quadruple excitations. We present two ways to derive this quadruples correction to UCCSDT, henceforth referred to as [Q-6], which leads to an improvement in the correlation energy shown to be correct through sixth-order in many-body perturbation theory (MBPT). A comparison between the UCC-based […

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Runpeng Dai, Linfeng Song, Haolin Liu, Zhenwen Liang, Dian Yu, Haitao Mi, Zhaopeng Tu, Rui Liu, Tong Zheng, Hongtu Zhu, Dong Yu
https://arxiv.org/abs/2509.09675

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often explore poorly, leading to premature convergence and entropy collapse. To address this challenge, we introduce Curiosity-Driven Exploration (CDE), a framework that leverages the model's own intrinsic sense of curiosity to guide exploration. We formalize curiosity with signals from both the actor and the critic: for the a…