Modern retrieval embeddings are highly compressible, often retaining most of their effectiveness after the removal of a large fraction of dimensions. This suggests that retrieval-relevant information is organized in a lower-dimensional structure, but it remains unclear how broadly that structure generalizes and what ultimately limits compression.

We study this question in long-document financial retrieval where relevant evidence must be distinguished from many highly similar passages containing repeated entities, financial metrics, tables, and boilerplate. Across three financial retrieval benchmarks and eight retrievers, we find that PCA projections learned on one corpus transfer effectively to others, retaining most of the effectiveness of corpus-specific fitting. Transfer quality is strongly associated with principal-subspace overlap, consistent with substantial shared low-dimensional structure across financial corpora. We further show that global geometric measures such as intrinsic dimension only partially explain retrieval robustness under dimensionality reduction. A case study on a financial question answering benchmark links retrieval failures to dense clusters of competing table chunks: Removing these distractors substantially reduces performance degradation. These results suggest that transferability is enabled by low-dimensional structure that generalizes across financial corpora, while compression failures are associated with local retrieval competition that is not captured by existing global geometric measures.

Answering complex questions that require numerical reasoning over financial documents is challenging due to the diverse and scatterednature of relevant information. While large language models (LLMs) excel at financial reasoning, their enterprise deployment is often limited by cost and latency. Small language models (SLMs) present a cost-effective alternative but need to be fine-tuned with high-quality, domain-specific question-answer (QA) data. Acquiring such data requires manual expert annotation, presenting a bottleneck to the wider application of SLMs.This work introduces a modular, scalable end-to-end agentic pipeline that extracts and selects relevant content from unstructured financial documents and then generates QA pairs from the selected content for SLM fine-tuning. Compared to the same models trained on previous manually generated data for the task, one of the models trained on our pipeline-produced synthetic data achieved competitive in-distribution performance, and all tested models demonstrated superior generalization. The framework thus demonstrates considerable potential to accelerate the deployment of smaller, cost-effective models by reducing manual data creation efforts.

Recent research has shown excellent performance of large language models (LLMs) for answering questions requiring multi-step financial reasoning. While the larger models have been used with zero-shot or few-shot prompting, the smaller variants need fine-tuning on training data containing questions and the corresponding answers that includes detailed reasoning demonstrations. To alleviate the significant cost of creating a data set with complex questions and corresponding answers, we explore the use of synthetic data for financial question answering using a multi-step LLM based approach to generate question as well as the answers with reasoning steps. We consider standard as well as conversational financial question answering scenarios. We experiment with synthetic data generation for three different real financial reasoning problems that already have manually collected data sets created with the help of financial experts. Using the same document sources, we use the proposed LLM based approach to generate synthetic questions and answers. To measure the effectiveness, we train multiple small language models (SLMs) on these synthetic data and compare the performance with that of the same SLMs trained on the real data. We further perform extensive experimental analysis generating important evidence on the potential of using synthetic data in financial reasoning tasks.

Recent research has shown that smaller language models can acquire substantial reasoning abilities when fine-tuned with reasoning exemplars crafted by a significantly larger teacher model. We explore this paradigm for the financial domain, focusing on the challenge of answering questions that require multi-hop numerical reasoning over financial texts. We assess the performance of several smaller models that have been fine-tuned to generate programs that encode the required financial reasoning and calculations. Our findings demonstrate that these fine-tuned smaller models approach the performance of the teacher model.To provide a granular analysis of model performance, we propose an approach to investigate the specific student model capabilities that are enhanced by fine-tuning. Our empirical analysis indicates that fine-tuning refines the student models ability to express and apply the required financial concepts along with adapting the entity extraction for the specific data format. In addition, we hypothesize and demonstrate that comparable financial reasoning capability can be induced using relatively smaller datasets.