Financial agents are expected to answer factual investment queries through open search, but financial multi-hop retrieval differs from general multi-hop QA in a crucial way: each intermediate answer is an exact typed constraint, such as an entity, date, event, or numerical indicator, that determines the next query. This strict answer-conditioned dependency makes small intermediate errors cascade into plausible but wrong search trajectories. We introduce FinHopper, a benchmark for complex financial agents with controlled reasoning depth from 1 to 6 hops, covering stocks, funds, and macroeconomic facts, with gold intermediate answers for process-level evaluation. To construct FinHopper at scale, we design a state-transition-based synthesis framework that composes verified atomic financial QA patterns into dependency-preserving chains, followed by targeted in-synthesis verification, model-ensemble checking, and public-search validation by financial experts. Experiments with 20 LLM-agent configurations show that public search tools are necessary but insufficient: performance drops sharply as hop depth increases, the best model remains far below human experts, and process-aware scoring reveals partial reasoning progress hidden by final-answer-only evaluation. The benchmark is available at https://anonymous.4open.science/r/FinHopper-aabb/

Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yielding queries that fail to capture the implicit, event-driven nature of real-world needs. Moreover, its reliance on static tool sets overlooks the dynamic retrieval process required to navigate massive tool spaces. To address these challenges, we introduce FinToolSyn, a forward synthesis framework designed to generate high-quality financial dialogues. Progressing from persona instruction and atomic tool synthesis to dynamic retrieval dialogue generation, our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate the noisy candidate sets typical of massive tool spaces. We also establish a dedicated benchmark to evaluate tool-calling capabilities in realistic financial scenarios. Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06% improvement, providing a robust foundation for tool learning in financial scenarios.

Direct Preference Optimization (DPO) is notoriously sensitive to attribution ambiguity: when chosen and rejected responses differ along multiple entangled dimensions, the model cannot identify which behavior the preference label teaches. We propose Criterion-Activated Preference Synthesis (CAPS), a criterion-first framework that turns explicit quality standards into attribution-verified preference pairs. CAPS synthesizes contexts and responses from a target criterion, then applies dual-signal scoring: a rubric-bound judge measures criterion compliance, while a blind general judge measures holistic quality. Two filters close the attribution loop: the Empirical Activation Test (EAT) keeps criterion-context pairs where satisfying the criterion improves blind quality, and the Attribution Ratio (AR) keeps pairs whose quality gap is dominated by the target criterion. Instantiated with general rubrics, medical rubrics, and Chinese legal statutes, CAPS shows strong data efficiency and domain transfer. With 20k general synthetic pairs, it outperforms 35k human-annotated HelpSteer3 pairs on IFEval, IFBench, AlpacaEval, and Creative Writing v3. In domain settings, CAPS improves HealthBench with 2k medical pairs and improves Qwen3-8B/14B by +4.9 to +6.6 points on legal benchmarks using only public statutes, without human preference labels. We additionally construct the CAPS-Legal preference dataset.