Tootfinder

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…

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…

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…

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{…

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…

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…