AQEA: Technical Report — Domain-Adaptive Semantic Compression of Embeddings

This technical report introduces AQEA (Aurora Quantum Encoding Algorithm), a domain-adaptive compression method for high-dimensional embeddings used in retrieval-augmented generation (RAG), semantic search, and multimodal systems. Key contributions: - Extreme compression ratios with controllable quality tradeoffs via named operating points (“discovery”, “balanced”, “precision”). - Preservation of semantic similarity structure, validated through intrinsic (Spearman correlation) and extrinsic (human-labeled STS tasks) benchmarks. - Generalization to unseen datasets and support for multiple embedding spaces, including text and multimodal models. The report emphasizes reproducibility: all quantitative claims are backed by auditable repository artifacts, including benchmark scripts, integrity hashes, and a public verification path using HuggingFace datasets (no private data required). Limitations, failure modes, and deployment guardrails are explicitly discussed to enable responsible use. For hands-on testing: - Live demo (no sign-up): https://demo.aqea.ai - Web platform: https://platform.aqea.ai - CLI tool: https://compress.aqea.ai Version 0.1 | January 2026 | NextX AG, Switzerland