WaveTLM introduces a unified compiler‑executor framework that transforms natural‑language requests into typed task states and reliable numerical or structured outputs for time‑series tasks. The authors present ExecTS‑QA, a benchmark covering forecasting, imputation, classification, anomaly detection, and waveform analysis, and show that a single WaveTLM checkpoint achieves 99.40% contract‑valid coverage versus 37.83% for a string‑first baseline while maintaining balanced predictive performance. Additional evaluations on SciTS, TSQA, IRTS‑ToolBench, and ARFBench demonstrate the model’s transferability.
By Jiahui Chen, Bingke Zhu, Hongyu Pan, Yingying Chen
arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.
By Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen, Dai Do, Hung Le
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.
arXiv:2607. 06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online.
By Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He
arXiv:2508. 09904v3 Announce Type: replace-cross Abstract: Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form.
By Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, \'Etienne Marcotte, Valentina Zantedeschi, Alexandre Drouin
arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.
By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
arXiv:2610.19725v1 Announce Type: new
Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for m...
By Haoxin Liu, Deqing Fu, Lucheng Fu, Yiqiao Jin, Rushi Qiang, Harshavardhan Kamarthi, B. Aditya Prakash
arXiv:2605.03762v2 Announce Type: replace
Abstract: Forecasting is a composite capability encompassing information gathering, evidence integration, situational assessment, and decision-making. Large...
By Yiding Ma, Chengyun Ruan, Kaibo Huang, Jin Tan, Zhongliang Yang, Linna Zhou
The paper introduces BUSTA, a method for training small language models to reason about binary execution. By fine‑tuning Qwen3‑1.7B and Nemotron‑3‑Nano with Group Relative Policy Optimization, the authors improve the models’ ability to predict input register values for a given x86‑64 basic block, raising accuracy from 73.2% to 85.3% and from 63.3% to 95.2% respectively. The fine‑tuned Nemotron‑3‑Nano achieves performance within four points of frontier models while using far fewer parameters, and the authors release both the fine‑tuned models and the training dataset.
By Ritika Poddar, Aidan Fennelly, Deirdre Murphy, Eric Wolos, Corre L. Steele, Michael Doyle
arXiv:2608. 16224v1 Announce Type: cross Abstract: By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training.
By Xinlong Dai, Jinchuan Zhang, Lei Gao, Xinzhe Hu, Yuefeng He, Hui Gao
arXiv:2608.30976v1 Announce Type: new
Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...
By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen
arXiv:2608.20359v1 Announce Type: new
Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performanc...
By Ravisri Valluri, Tung Nguyen, Aditya Grover