Tootfinder

Kids and gun violence
The episode talked about how this mass murderer "true crime" brain-rot subculture flattens everything into just "content." They didn't talk about how algorithms play a role in this type of violence.
"The algorithm" incentivzes maximumally impactful content. When stripped of a moral framework, by capitalism and white supremacy, humans will search for a value system to inform them of what to do. A mass shooting is simply the lowest effort for the highest impact in terms of pure content generation.
This is simply the behavior incentivzed by the algorithm. The behavior incentivzed by capitalism is, of course, no less horrific but is presented as far more acceptable. It includes, of course, creating a machine that makes money off of content (without any differentiation in type) by maximizing engagement and incentivzing content generation.

Evaluate Neighbor Search for Curve-based Vector Field Processing
Nguyen Phan, Guoning Chen
https://arxiv.org/abs/2509.00180 https://arxiv.org/pdf/2509.0018…

Evaluate Neighbor Search for Curve-based Vector Field Processing
Curve-based representations, particularly integral curves, are often used to represent large-scale computational fluid dynamic simulations. Processing and analyzing curve-based vector field data sets often involves searching for neighboring segments given a query point or curve segment. However, because the original flow behavior may not be fully represented by the set of integral curves and the input integral curves may not be evenly distributed in space, popular neighbor search strategies oft…

Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Xu Chen, Yunmeng Shu, Yuangang Pan, Jinsong Lan, Xiaoyong Zhu, Shuai Xiao, Haojin Zhu, Ivor W. Tsang, Bo Zheng
https://arxiv.org/abs/2509.26172

Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Modeling sequential user behaviors for future behavior prediction is crucial in improving user's information retrieval experience. Recent studies highlight the importance of incorporating contextual information to enhance prediction performance. One crucial but usually neglected contextual information is the scene feature which we define as sub-interfaces within an app, created by developers to provide specific functionalities, such as ``text2product search" and ``live" modules in e-commence ap…

Explicit Global Convergence Rates of BFGS without Line Search
Jianjiang Yu, Weiguo Gao, Luo Luo
https://arxiv.org/abs/2509.22508 https://arxiv.org/pdf/2509…

Explicit Global Convergence Rates of BFGS without Line Search
This paper studies the convergence rates of the Broyden--Fletcher--Goldfarb--Shanno~(BFGS) method without line search. We show that the BFGS method with an adaptive step size [Gao and Goldfarb, Optimization Methods and Software, 34(1):194-217, 2019] exhibits a two-phase non-asymptotic global convergence behavior when minimizing a strongly convex function, i.e., a linear convergence rate of $\mathcal{O}((1 - 1 / \varkappa)^{k})$ in the first phase and a superlinear convergence rate of $\mathcal{…

SSRL: Self-Search Reinforcement Learning
Yuchen Fan, Kaiyan Zhang, Heng Zhou, Yuxin Zuo, Yanxu Chen, Yu Fu, Xinwei Long, Xuekai Zhu, Che Jiang, Yuchen Zhang, Li Kang, Gang Chen, Cheng Huang, Zhizhou He, Bingning Wang, Lei Bai, Ning Ding, Bowen Zhou
https://arxiv.org/abs/2508.10874

SSRL: Self-Search Reinforcement Learning
We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence on costly interactions with external search engines. To this end, we first quantify the intrinsic search capability of LLMs via structured prompting and repeated sampling, which we term Self-Search. Our results reveal that LLMs exhibit strong scaling behavior with respect to the inference budget, achieving high pass@k …

The Hidden Cost of Defaults in Recommender System Evaluation
Hannah Berlin, Robin Svahn, Alan Said
https://arxiv.org/abs/2508.21180 https://arxiv.org/pdf/2…

The Hidden Cost of Defaults in Recommender System Evaluation
Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern. In this work, we shift focus from model benchmarking to auditing the behavior of RecBole, a widely used recommendation framework. We show that RecBole's internal defaults, particularly an undocumented early-stopping policy, can prematurely terminate Random Search and Bayesian Optimization. This limits search coverage in ways tha…

Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
Sidahmed Benabderrahmane, Talal Rahwan
https://arxiv.org/abs/2508.19019

Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
Advanced Persistent Threats (APTs) pose a severe challenge to cyber defense due to their stealthy behavior and the extreme class imbalance inherent in detection datasets. To address these issues, we propose a novel active learning-based anomaly detection framework that leverages similarity search to iteratively refine the decision space. Built upon an Attention-Based Autoencoder, our approach uses feature-space similarity to identify normal-like and anomaly-like instances, thereby enhancing mod…

A Generic Complete Anytime Beam Search for Optimal Decision Tree
Harold Silv\`ere Kiossou, Siegfried Nijssen, Pierre Schaus
https://arxiv.org/abs/2508.06064 https://

A Generic Complete Anytime Beam Search for Optimal Decision Tree
Finding an optimal decision tree that minimizes classification error is known to be NP-hard. While exact algorithms based on MILP, CP, SAT, or dynamic programming guarantee optimality, they often suffer from poor anytime behavior -- meaning they struggle to find high-quality decision trees quickly when the search is stopped before completion -- due to unbalanced search space exploration. To address this, several anytime extensions of exact methods have been proposed, such as LDS-DL8.5, Top-k-DL…

AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
Sihun Baek, Zhehan Qu, Maria Gorlatova
https://arxiv.org/abs/2509.13468

AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe's Guided Search model, and captured performance, ga…

from my link log —
A new approach to analyzing Robin Hood hashing. (2014)
https://arxiv.org/abs/1401.7616
saved 2025-10-05 https://dotat.at/:/HOGQW.html…

A New Approach to Analyzing Robin Hood Hashing
Robin Hood hashing is a variation on open addressing hashing designed to reduce the maximum search time as well as the variance in the search time for elements in the hash table. While the case of insertions only using Robin Hood hashing is well understood, the behavior with deletions has remained open. Here we show that Robin Hood hashing can be analyzed under the framework of finite-level finite-dimensional jump Markov chains. This framework allows us to re-derive some past results for the in…

Addressing Personalized Bias for Unbiased Learning to Rank
Zechun Niu, Lang Mei, Liu Yang, Ziyuan Zhao, Qiang Yan, Jiaxin Mao, Ji-Rong Wen
https://arxiv.org/abs/2508.20798 https…

Addressing Personalized Bias for Unbiased Learning to Rank
Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has studied a variety of biases in users' clicks, such as position bias, presentation bias, and outlier bias. However, existing work often assumes that the behavior logs are collected from an ``average'' user, neglecting the differences between different users in their search and browsing behaviors. In this paper, we intro…

Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Eric R. Damm, Eli S. Lancaster, Felix A. Sanchez, Kiana Bronder, Jason M. Gregory, Thomas M. Howard
https://arxiv.org/abs/2508.04384 …

Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Mobile robot motion planners rely on theoretical models to predict how the robot will move through the world. However, when deployed on a physical robot, these models are subject to errors due to real-world physics and uncertainty in how the lower-level controller follows the planned trajectory. In this work, we address this problem by presenting three methods of incorporating stochastic controller behavior into the recombinant search space of the Kinodynamic Efficiently Adaptive State Lattice …

Beyond conventional half-metals: gapless states and spin gapless semiconducting behavior in X$_2$MnGa (X = Ti, Ir) Heusler compounds
N. Bouteldja, N. Hacini, I. Ouadha, H. Rached
https://arxiv.org/abs/2509.12803

Beyond conventional half-metals: gapless states and spin gapless semiconducting behavior in X$_2$MnGa (X = Ti, Ir) Heusler compounds
The search for high-performance spintronic materials motivates the exploration of Heusler alloys with unconventional electronic properties. Using density functional theory with Hubbard correction (DFT+$U$, $U = 4$~eV), we investigate X$_2$MnGa (X = Ti, Ir) alloys, which stabilize in the ferromagnetic L2$_1$-type structure with strong thermodynamic stability. Electronic structure calculations reveal contrasting behaviors: Ti$_2$MnGa transitions from a metallic L2$_1$-type phase to a spin gapless…

Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
Jiahe Jin, Abhijay Paladugu, Chenyan Xiong
https://arxiv.org/abs/2510.06534 https://

Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
Agentic search leverages large language models (LLMs) to interpret complex user information needs and execute a multi-step process of planning, searching, and synthesizing information to provide answers. This paradigm introduces unique challenges for LLMs' reasoning and agentic capabilities when interacting with retrieval systems and the broader web. In this paper, we propose a reasoning-driven LLM-based pipeline to study effective reasoning behavior patterns in agentic search. Using this pipel…

Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
Teng Shi, Weijie Yu, Xiao Zhang, Ming He, Jianping Fan, Jun Xu
https://arxiv.org/abs/2508.04145

Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies show that incorporating search signals boosts recommendation performance. However, the effectiveness of these methods relies heavily on rich search interactions. They primarily benefit a small subset of users with abundant search behavior, while offering limited improvements for the majority of users who exhibit only s…

A Sample of HeII {\lambda}4686 "Changing-Look" Quasars
Wentao Lu, Jun-Xian Wang
https://arxiv.org/abs/2510.06753 https://arxiv.org/pdf/2510.06753…

A Sample of HeII λ4686 "Changing-Look" Quasars
We present the first systematic search for "changing-look" ("CL") behavior in the broad He ii λ4686 emission line in quasars, utilizing repeated spectroscopy from the Sloan Digital Sky Survey (SDSS). The He ii line, originating from high-ionization gas and powered by extreme ultraviolet photons, serves as a sensitive tracer of changes in the ionizing continuum. After applying strict spectral selection criteria and visual inspection to a parent sample of over 9,000 quasars with multi-epoch spec…

AI Guided Accelerator For Search Experience
Jayanth Yetukuri, Mehran Elyasi, Samarth Agrawal, Aritra Mandal, Rui Kong, Harish Vempati, Ishita Khan
https://arxiv.org/abs/2508.05649

AI Guided Accelerator For Search Experience
Effective query reformulation is pivotal in narrowing the gap between a user's exploratory search behavior and the identification of relevant products in e-commerce environments. While traditional approaches predominantly model query rewrites as isolated pairs, they often fail to capture the sequential and transitional dynamics inherent in real-world user behavior. In this work, we propose a novel framework that explicitly models transitional queries--intermediate reformulations occurring durin…

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
Qihang Zhao, Xiaoyang Zheng, Ben Chen, Zhongbo Sun, Chenyi Lei
https://arxiv.org/abs/2509.19876

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise. Current methods predominantly follow an "identify-aggregate" paradigm, assuming sequences immutably reflect user preferences while overlooking the organic entanglement of noise and genuine interest. Moreover, they output static, context-agnostic representations, failing to adapt to dynamic intent shifts under varying Query-User-Item-C…