arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.
By En Fu, Yanyan Hu, Zengwang Jin, Kaixiang Peng
D-TAIA is a framework that adapts large language models for multi‑task predictive process monitoring, jointly predicting the next activity and remaining time of ongoing cases. It uses domain‑aware triplet loss pre‑training, FAISS‑based nearest‑neighbor retrieval for time estimation, and a TAIA inference strategy to preserve sequential reasoning while fine‑tuning a 10 M‑parameter backbone. Across four real‑world event logs, D‑TAIA achieves state‑of‑the‑art or competitive results compared to a fine‑tuned LLM and a recurrent neural network baseline, with ablation studies showing the effectiveness of NLP and computer‑vision techniques for this domain.
By Sjoerd van Straten, Christine Jacob, Marwan Hassani
OneBid is a unified auto‑bidding foundation model that consolidates diverse cost‑per‑X (oCPX) advertising scenarios into a single framework. It builds on Decision Transformer by conditioning on two atomic signals—Return‑to‑Go for conversion value and Cost‑to‑Go for cost ratio—and incorporates value‑aware regularization. A sequence‑level Mixture‑of‑Experts architecture captures cross‑scenario knowledge while preserving low latency, and a Critic‑guided Relative Offline Policy optimization (CROP) aligns the backbone with scenario‑specific preferences without unsafe online exploration. In production at Kuaishou, OneBid achieved a 2.2% overall ADVV increase and up to 13.1% in the ROAS scenario.
By Yewen Li, Peng Jiang, Yitian Li, Pengfei Lv, Xialong Liu, Peng Jiang, Qingpeng Cai
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
By Liang Cao, Weide Liu, Yan Qin, Jun Cheng, Weisi Lin, Bhushan Gopaluni
arXiv:2607. 22769v1 Announce Type: cross Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data.
By He Zhang
Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e.