arXiv AI August 18, 2026 By Ziyue Yang, Chaolin Xu, Yijing Wang, Tiankai Gu, Hui Yang, Yanhong Lin, Kaiyuan Liu, Fei Xiao

ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning

Read the original on arXiv AI →

arXiv:2608. 15291v1 Announce Type: new Abstract: Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dynamics, while future promotions, holidays, price changes, and platform interventions provide forward-looking knowledge.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 5

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.

By Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 11

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.

By Yizhou Chi, Eric Chamoun, Zifeng Ding, Andreas Vlachos

llms agents benchmarks

arXiv Machine Learning

Aug 4

ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

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

llms fine-tuning benchmarks

arXiv AI

Aug 5

Traceable Multi-Agent System for Knowledge-Based Forecasting

arXiv:2608. 03339v1 Announce Type: new Abstract: Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise models.

By Junhyeok Kang, Sangjun Han, Hyeokjun Choe, Soonyoung Lee

agents

arXiv AI

Jun 2

Bridging the Last Mile of Time Series Forecasting with LLM Agents

arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.

By Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang

llms agents safety

arXiv AI

Jun 2

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.

By Yaxuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yilei Shao, Stefan Zohren, Anna Vettoruzzo, Joaquin Vanschoren, Ming Jin, Qingsong Wen

llms agents benchmarks

Related stories

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

Traceable Multi-Agent System for Knowledge-Based Forecasting

Bridging the Last Mile of Time Series Forecasting with LLM Agents

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning