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Rex Ying

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Book Open access Aug 2026

NumCache: KV Cache Compression and Retrieval for Financial Document QA

Large Language Models (LLMs) are increasingly deployed in financial applications, particularly for interpreting U.S. Securities and Exchange Commission (SEC) filings. However, financial QA over these filings is challenging, as they are extremely long, numerically dense, and often require cross-document reasoning. Existing approaches struggle to scale to such settings due to long-context degradation and loss of numerical fidelity under context compression. Long-document Retrieval-Augmented Generation (RAG) improves evidence coverage through coarse-to-fine retrieval, yet semantic retrievers collapse fine-grained magnitudes and units, returning passages that lack the precise values needed for correct reasoning. Cache-Augmented Generation (CAG) projects attention states into compact KV representations and treats precomputed caches as reusable internal memory, but typically assumes caches are already well-formed and query-relevant, leaving open how to build and select number-faithful caches. To address this gap, we propose NumCache, which compresses SEC filings into KV caches initialized from numerically dense regions and trained directly on financial QAs. On top of these caches, we then train a contrastive retriever that aligns questions with cache representations, thus improving retrieval performance. We evaluate NumCache on the Fin-RATE benchmark, which comprises SEC-filing QA tasks covering single-filing reasoning, cross-firm comparison, and longitudinal trend analysis. NumCache achieves up to 4× context compression with competitive accuracy (41.4% vs.\ 44.8% for uncompressed Qwen3-4B on single-filing reasoning) and a 6.4× inference speedup, while its contrastive retriever attains 68.3% Recall@1 on single-filing retrieval; 2.3× the strongest text-based baseline (29.7%). These results highlight cache-based retrieval with number-preserving representations as an effective approach for long-context financial QA.

Eftychia Makri, Peiwen Li, Yidong Jiang et al. · 0 citations
Book Open access Aug 2026

Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models

Large pretrained models have reshaped artificial intelligence, yet their Euclidean design assumptions often limit their ability to model hierarchy, curvature, symmetry, and heterogeneous relations in real-world data. The Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models (GALOP) workshop is an accepted half-day KDD 2026 workshop that examines how geometric principles can make large pretrained models more expressive, robust, interpretable, and efficient. The workshop is organized around three complementary themes: (1) Geometric Space, which studies non-Euclidean representation spaces such as hyperbolic, spherical, and mixed-curvature manifolds; (2) Geometric Architecture, which designs model architectures that respect data symmetries, manifold structure, and relational geometry; and (3) Geometric Learning Objective, which develops objectives and optimization methods that preserve distances, angles, curvature, and topology during training. Through two invited talks and four contributed talks, the workshop brings together researchers from machine learning, data mining, and related fields to advance geometrically-informed foundation models for language, vision, graphs, knowledge discovery, and scientific discovery.

Menglin Yang, Jiahong Liu, Lucas Vinh Tran et al. · 0 citations