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.
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.
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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
Sep 15
TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for qu...
arXiv AI
Jun 2
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
arXiv:2606. 02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction.
arXiv AI
Aug 19
TSQueryBench: LLM-as-a-Judge for Time Series Explanations
TSQueryBench is a synthetic benchmark comprising 500 time‑series instances and 10 query types, each paired with correct, partially correct, and incorrect natural‑language explanations. The study evaluates six large language models on explanation generation, ranking, scoring, and anomaly detection, revealing that models often fail to generate numerically correct explanations yet can reliably identify or score correct ones. These findings suggest that rubric‑guided LLM evaluation is more dependable than generation for numerically grounded time‑series reasoning.
arXiv AI
Jun 10
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.
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 12
Representing Time Series as Structured Programs for LLM Reasoning
arXiv:2606. 12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis.
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.
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.
arXiv AI
Jun 16
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv:2603. 02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub.
arXiv AI
Jul 9
Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics
arXiv:2607. 06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored.