Time Series Augmented Generation for Financial Applications
Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. Standard benchmarks often fail to isolate an agent's core ability to parse queries and orchestrate computations. To address this, we introduce a novel evaluation methodology and benchmark designed to rigorously measure an LLM agent's reasoning for financial time-series analysis. We apply this methodology in a large-scale empirical study using our framework, Time Series Augmented Generation (TSAG), where an LLM agent delegates quantitative tasks to verifiable, external tools. Our benchmark, consisting of 100 financial questions, is used to co...
| Authors | Anton Kolonin, Alexey Glushchenko, Evgeny Bochkov et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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