CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering
Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answering), a deterministic tool-grounded multi-agent LLM framework for financial question answering. CIFQA separates language understanding from numerical execution by assigning specialized agents to query interpretation, routing, parameter extracti...
| Authors | Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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