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.
LLM Agents for Time-Series: A Survey
The survey reviews LLM-based agents tailored to time-series tasks, organizing them by problem type rather than technical components. It categorizes existing systems into forecasting and reasoning, augmentation and synthesis, anomaly detection and diagnosis, and decision support, and analyzes how task demands influence agent architecture, tool use, and memory design. The paper also summarizes datasets, environments, and compares model performance, providing a task-oriented guide and highlighting gaps for future research.
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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.
arXiv Machine Learning
Jun 5
Harnessing Generalist Agents for Contextualized Time Series
arXiv:2606. 05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling.
arXiv AI
Jun 30
An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations
arXiv:2606. 28467v1 Announce Type: cross Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use.
arXiv AI
Jun 11
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
arXiv:2509. 11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer.
arXiv AI
Jun 16
Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
arXiv:2606. 15684v1 Announce Type: new Abstract: We present TickingCollabBench, a Minecraft-based multi-agent benchmark for a novel class of time-sensitive complementary collaboration tasks.
arXiv AI
2d ago
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
arXiv AI
Jun 16
Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.
arXiv AI
Jun 6
Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
arXiv:2606. 06448v1 Announce Type: new Abstract: LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories.
arXiv AI
Aug 25
MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
MetaCaster is a meta-harness-optimized multi-agent framework that enables few-shot learning for lightweight time series forecasters. It uses agentic data generation to automatically train specialized forecasters from only a few examples and textual contexts, positioning agents as intermediary engineers rather than direct forecasters. Experiments on 18 datasets and 23 lightweight forecasters show that MetaCaster achieves data and computational efficiency while maintaining high forecasting quality.
arXiv AI
Aug 6
OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents
arXiv:2608. 05013v1 Announce Type: cross Abstract: LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life.
arXiv AI
Sep 24
TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent
TimeEvo is a new method for time‑series agents that autonomously evolves its tool library based on failures observed during runtime. By clustering diagnosed failures into capability gaps, planning measurements, synthesizing evidence‑only tools, and admitting candidates through a paired gate, the system starts from an empty library and improves accuracy across ten QA tasks and three backbones. Experiments show that even a library built on a cheap model benefits stronger models when installed.