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A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

High-dimensional dense text embeddings and large language models face real obstacles in financial-disclosure analysis: context-window limits, hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across independently trained models. We present a training-free, alignment-free framework for corporate intelligence built on deterministic sparse seed vectors. Hashing word strings into a fixed high-dimensional basis places all documents and all temporal epochs in a common coordinate system by construction, removing any need for training or alignment. Accumulating t

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