Tootfinder

Bayesian online collective anomaly and change point detection in fine-grained time series
Xian Chen, Weichi Wu
https://arxiv.org/abs/2508.06385 https://arx…

Bayesian online collective anomaly and change point detection in fine-grained time series
Fine-grained time series data are crucial for accurate and timely online change detection. While both collective anomalies and change points can coexist in such data, their joint online detection has received limited attention. In this research, we develop a Bayesian framework capturing time series with collective anomalies and change points, and introduce a recursive online inference algorithm to detect the most recent collective anomaly and change point jointly. For scaling, we further propos…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Shalev Manor, Mohammad Kohandel
https://arxiv.org/abs/2509.07245 https://arxiv.org/pdf/2…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offl…

ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
Rui Dong, Wentao Ouyang, Xiangzheng Liu
https://arxiv.org/abs/2509.07594 https://

ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
Click-through rate (CTR) prediction plays an important role in online advertising systems. On the one hand, traditional CTR prediction models capture the collaborative signals in tabular data via feature interaction modeling, but they lose semantics in text. On the other hand, Large Language Models (LLMs) excel in understanding the context and meaning behind text, but they face challenges in capturing collaborative signals and they have long inference latency. In this paper, we aim to leverage …

Statistical Inference for Misspecified Contextual Bandits
Yongyi Guo, Ziping Xu
https://arxiv.org/abs/2509.06287 https://arxiv.org/pdf/2509.06287

Statistical Inference for Misspecified Contextual Bandits
Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment and efficient use of data. Yet these advantages create challenges for statistical inference due to adaptivity. A fundamental property that supports valid inference is policy convergence, meaning that action-selection probabilities converge in probability given the context. Convergence ensures replicability of adaptive experiments and stability of online algorithms. In…

Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
Julian Lemmel, Manuel Kranzl, Adam Lamine, Philipp Neubauer, Radu Grosu, Sophie Neubauer
https://arxiv.org/abs/2508.00804

Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling capabilities, they are typically trained offline and remain static during deployment. Our method enables online adaptation by continuously updating model parameters in response to incoming data. We evaluate our approach for linear-recurrent-unit SSMs using a small carbo…

LLM-Powered CPI Prediction Inference with Online Text Time Series
Yingying Fan, Jinchi Lv, Ao Sun, Yurou Wang
https://arxiv.org/abs/2506.09516 https://

LLM-Powered CPI Prediction Inference with Online Text Time Series
Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large se…

MapKD: Unlocking Prior Knowledge with Cross-Modal Distillation for Efficient Online HD Map Construction
Ziyang Yan, Ruikai Li, Zhiyong Cui, Bohan Li, Han Jiang, Yilong Ren, Aoyong Li, Zhenning Li, Sijia Wen, Haiyang Yu
https://arxiv.org/abs/2508.15653

MapKD: Unlocking Prior Knowledge with Cross-Modal Distillation for Efficient Online HD Map Construction
Online HD map construction is a fundamental task in autonomous driving systems, aiming to acquire semantic information of map elements around the ego vehicle based on real-time sensor inputs. Recently, several approaches have achieved promising results by incorporating offline priors such as SD maps and HD maps or by fusing multi-modal data. However, these methods depend on stale offline maps and multi-modal sensor suites, resulting in avoidable computational overhead at inference. To address t…

Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner, Pawel Herman, Artur Podobas
https://arxiv.org/abs/2506.18530

Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overparameterized, energy-hungry, and dependent on cloud connectivity. Brain-Like Neural Networks (BLNNs), such as the Bayesian Confidence Propagation Neural Network (BCPNN), propose a neuromorphic alternative by mimicking cortical architecture and biologically-constrained learning. They offer sparse architectures with local …

Online Rolling Controlled Sequential Monte Carlo
Liwen Xue, Axel Finke, Adam M. Johansen
https://arxiv.org/abs/2508.00696 https://arxiv.org/pdf/2508.00696

Online Rolling Controlled Sequential Monte Carlo
We introduce methodology for real-time inference in general-state-space hidden Markov models. Specifically, we extend recent advances in controlled sequential Monte Carlo (CSMC) methods-originally proposed for offline smoothing-to the online setting via a rolling window mechanism. Our novel online rolling controlled sequential Monte Carlo (ORCSMC) algorithm employs two particle systems to simultaneously estimate twisting functions and perform filtering, ensuring real-time adaptivity to new obse…

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
Zixi Chen, Yinyu Ye, Zijie Zhou
https://arxiv.org/abs/2508.14544 https://arxiv.or…

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
We study the problem of optimizing Large Language Model (LLM) inference scheduling to minimize total latency. LLM inference is an online and multi-task service process and also heavily energy consuming by which a pre-trained LLM processes input requests and generates output tokens sequentially. Therefore, it is vital to improve its scheduling efficiency and reduce the power consumption while a great amount of prompt requests are arriving. A key challenge in LLM inference scheduling is that whil…

Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking
Qing Zhang, Alex Deng, Michelle Du, Huiji Gao, Liwei He, Sanjeev Katariya
https://arxiv.org/abs/2508.00751

Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking
Evaluation plays a crucial role in the development of ranking algorithms on search and recommender systems. It enables online platforms to create user-friendly features that drive commercial success in a steady and effective manner. The online environment is particularly conducive to applying causal inference techniques, such as randomized controlled experiments (known as A/B test), which are often more challenging to implement in fields like medicine and public policy. However, businesses face…

InferLog: Accelerating LLM Inference for Online Log Parsing via ICL-oriented Prefix Caching
Yilun Wang, Pengfei Chen, Haiyu Huang, Zilong He, Gou Tan, Chuanfu Zhang, Jingkai He, Zibin Zheng
https://arxiv.org/abs/2507.08523

InferLog: Accelerating LLM Inference for Online Log Parsing via ICL-oriented Prefix Caching
Modern software systems generate massive volumes of runtime logs, necessitating efficient and accurate log parsing to enable critical downstream tasks such as anomaly detection and root cause analysis. Recently, large language models (LLMs) have achieved advanced accuracy on log parsing, but their deployment in production environments faces two major limitations: (1) the privacy risks associated with commercial LLMs, driving the adoption of local deployment, and (2) the stringent latency and th…

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks
Ali Emre Balc{\i} (TU Delft), Erhan Ege Keyvan (Middle East Technical University), Emre \"Ozkan (Middle East Technical University)
https://arxiv.org/abs/2508.16459

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks
We present a novel Simultaneous Localization and Mapping (SLAM) method that employs Gaussian Process (GP) based landmark (object) representations. Instead of conventional grid maps or point cloud registration, we model the environment on a per object basis using GP based contour representations. These contours are updated online through a recursive scheme, enabling efficient memory usage. The SLAM problem is formulated within a fully Bayesian framework, allowing joint inference over the robot p…

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
Rui Zhao, Vladyslav Melnychuk, Jun Zhao, Jesse Wright, Nigel Shadbolt
https://arxiv.org/abs/2507.14214

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with t…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
https://arxiv.org/abs/2508.20367 https://…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the analytically intractable nature of the predictor, we approximate it using a learned neural operator mapping. This mapping is trained once, offline, and then deployed online, leveraging the fast inference capabilities of neural networks. We provide a theoretical stability analysis based on the universal approximation the…

Online survival analysis with quantile regression
Yi Deng, Shuwei Li, Liuquan Sun, Baoxue Zhang
https://arxiv.org/abs/2507.15696 https://

Online survival analysis with quantile regression
We propose an online inference method for censored quantile regression with streaming data sets. A key strategy is to approximate the martingale-based unsmooth objective function with a quadratic loss function involving a well-justified second-order expansion. This enables us to derive a new online convex function based on the current data batch and summary statistics of historical data, thereby achieving online updating and occupying low storage space. To estimate the regression parameters, we…

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang, Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin, Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuehui Wang, Songze Li, Xiangyu Zhao, Haodong Duan, Nianche…

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g.…

Online selective conformal inference: adaptive scores, convergence rate and optimality
Pierre Humbert, Ulysse Gazin, Ruth Heller, Etienne Roquain
https://arxiv.org/abs/2508.10336

Online selective conformal inference: adaptive scores, convergence rate and optimality
In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of \cite{gibbs2021adaptive}. For any given point-prediction algorithm, their method (ACI) produces a conformal prediction set with an average missed coverage getting close to a pre-specified level $α$ for a long time horizon. We introduce an extended version of this algorithm, called OnlineSCI, allowing the user to additionally select times where such an inference should be made. OnlineSCI encompasses…

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments
Zhenkang Peng (Philip), Chengzhang Li (Philip), Ying Rong (Philip), Renyu (Philip), Zhang
https://arxiv.org/abs/2508.10331

Synthesizing Evidence: Data-Pooling as a Tool for Treatment Selection in Online Experiments
Randomized experiments are the gold standard for causal inference but face significant challenges in business applications, including limited traffic allocation, the need for heterogeneous treatment effect estimation, and the complexity of managing overlapping experiments. These factors lead to high variability in treatment effect estimates, making data-driven policy roll out difficult. To address these issues, we introduce the data pooling treatment roll-out (DTR) framework, which enhances pol…

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
Yanjun Dai, Haoyang Feng, Yuan Gao
https://arxiv.org/abs/2506.13787

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
While online advertising is highly dependent on implicit interaction networks of anonymous users for engagement inference, and for the selection and optimization of delivery strategies, existing graph models seldom can capture the multi-scale temporal, semantic and higher-order dependency features of these interaction networks, thus it's hard to describe the complicated patterns of the anonymous behavior. In this paper, we propose Decoupled Temporal-Hierarchical Graph Neural Network (DTH-GNN), …

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.09090 https:…

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system
Modeling multi-interests has arisen as a core problem in real-world RS. Current multi-interest retrieval methods pose three major challenges: 1) Interests, typically extracted from predefined external knowledge, are invariant. Failed to dynamically evolve with users' real-time consumption preferences. 2) Online inference typically employs an over-exploited strategy, mainly matching users' existing interests, lacking proactive exploration and discovery of novel and long-tail interests. To addres…