FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability

Financial search poses a demanding challenge for LLM agents, requiring not only correct final answers but also responses grounded in temporally valid information, authoritative sources, and correctly aligned units and definitions. Yet existing benchmarks predominantly evaluate only final answer correctness, obscuring both the causes of error and the completeness of supporting evidence. Our analysis revealed that 17.48% of correctly answered tasks lack a complete and verifiable evidence chain. To address this gap, we introduce **FinFIRST** (**F**inancial **I**nformation **R**etrieval, **S**ourcing and **T**raceability), a financial-search benchmark that jointly evaluates answers and evidential support through atomic rubrics. FinFIRST comprises 123 expert-authored tasks spanning a graduated difficulty spectrum, derived from aggregate patterns of real-world financial scenarios and informed by an 18-field taxonomy, a 6-axis coverage blueprint, and a registry of 138 financial sources. Task construction involved contributions from over 50 finance experts and a 6-stage quality-control pipeline. Each task is paired with an evidence-grounded reference package decomposed into atomic criteria across three dimensions: raw-information acquisition, source verification, and computation and answer formation. We evaluate multiple model configurations under a unified tool setting: Claude-Opus-5 achieves the highest Loose Pass rate at 87.61%, while GPT-5.6-Sol attains the highest Strict Pass rate at 71.54%, with computation and answer formation consistently lagging behind raw-information acquisition across all systems. FinFIRST thus retains final-answer correctness as the primary objective while making the supporting research process measurable, verifiable, and diagnosable.