Time Series Augmented Generation for Financial Applications

• URL: http://arxiv.org/abs/2604.19633v1
• Date: Tue, 21 Apr 2026 16:20:59 GMT
• Title: Time Series Augmented Generation for Financial Applications
• Abstract summary: We introduce a novel evaluation methodology and benchmark designed to rigorously measure an agent's reasoning for financial time-series analysis.<n>We apply this methodology in a large-scale empirical study using our framework, Time Series Augmented Generation.<n>The results demonstrate that capable agents can achieve near-perfect tool-use accuracy with minimal hallucination.
• Score: 0.10499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 compare multiple SOTA agents (e.g., GPT-4o, Llama 3, Qwen2) on metrics assessing tool selection accuracy, faithfulness, and hallucination. The results demonstrate that capable agents can achieve near-perfect tool-use accuracy with minimal hallucination, validating the tool-augmented paradigm. Our primary contribution is this evaluation framework and the corresponding empirical insights into agent performance, which we release publicly to foster standardized research on reliable financial AI.

Related papers

• From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities [103.60384544335072]
  Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence.<n>Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities.<n> Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles.
  arXiv  Detail & Related papers  (2026-08-27T10:52:06Z)
• Offloading Score: Measuring AI Reliance Through Counterfactual Workflows [70.84727355516559]
  We introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool.<n>We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study.<n>We show that offloading score detects significantly higher reliance in time-constrained settings.
  arXiv  Detail & Related papers  (2026-05-28T05:44:31Z)
• FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol [25.065258058016052]
  FinMCP-Bench is a benchmark for evaluating large language models (LLMs) in solving real-world financial problems.<n>It incorporates 65 real financial MCPs and three types of samples, single tool, multi-tool, and multi-turn, allowing evaluation of models across different levels of task complexity.
  arXiv  Detail & Related papers  (2026-03-26T02:20:04Z)
• FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use [11.47696602663462]
  FinToolBench is the first real-world, runnable benchmark dedicated to evaluating financial tool learning agents.<n>We propose a novel evaluation framework that goes beyond binary execution success, assessing agents on finance-critical dimensions.<n>We present FATR, a finance-aware tool retrieval and reasoning baseline that enhances stability and compliance.
  arXiv  Detail & Related papers  (2026-03-09T11:33:05Z)
• Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs [21.656551146954587]
  Large Language Models (LLMs) offer a path to automation.<n>We introduce a novel, structured dataset from 190 corporate reports.<n>Our results reveal a clear performance gap between qualitative and quantitative tasks.
  arXiv  Detail & Related papers  (2025-12-30T15:28:03Z)
• Benchmarking LLM Agents for Wealth-Management Workflows [0.0]
  This dissertation extends TheAgentCompany with a finance-focused environment.<n>It investigates whether a general purpose LLM agent can complete representative wealth-management tasks both accurately and economically.
  arXiv  Detail & Related papers  (2025-12-01T21:56:21Z)
• CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency [60.83660377169452]
  This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents.<n>Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges.
  arXiv  Detail & Related papers  (2025-11-29T09:52:34Z)
• LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering [90.84806758077536]
  We introduce textbfLoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess large language models (LLMs) agents in realistic, long-context software engineering.<n>Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations.<n>Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens.
  arXiv  Detail & Related papers  (2025-11-17T23:57:24Z)
• On Generalization in Agentic Tool Calling: CoreThink Agentic Reasoner and MAVEN Dataset [16.921428284844684]
  Generalization across Agentic tool-calling environments remains a key unsolved challenge in developing reliable reasoning systems.<n>We present a framework that augments large language models with a lightweight symbolic reasoning layer for structured decomposition and adaptive tool orchestration.
  arXiv  Detail & Related papers  (2025-10-27T00:58:48Z)
• FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering [57.43420753842626]
  FinLFQA is a benchmark designed to evaluate the ability of Large Language Models to generate long-form answers to complex financial questions.<n>We provide an automatic evaluation framework covering both answer quality and attribution quality.
  arXiv  Detail & Related papers  (2025-10-07T20:06:15Z)
• FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
  FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
  arXiv  Detail & Related papers  (2025-08-07T22:15:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.