Addressing the Semantic Twins Problem in Legal RAG: A Lens Steering Approach

This paper responds to the "Liability Trap / Semantic Twins" challenge in retrieval-augmented generation (RAG) systems, highlighting a critical failure mode in legal and compliance applications: embedding models often rank contradictory clauses (e.g., "shall" vs. "shall not") as nearest neighbors due to high lexical overlap, risking the injection of opposite legal facts. We present a lens steering approach using the AQEA framework that achieves: - Complete suppression of hard-negative contradictions (HN@10 reduced from ~56% to 0.000) - Improved diagnostic utility over the uncompressed baseline - ~61× storage reduction (67 bytes/item vs. 4096 bytes for 1024D float32) - ~5.8× faster query latency on CPU-only benchmarks Results are validated on public legal distractor pools (LexGLUE, EDGAR) with reproducible artifacts. The approach is evaluated conservatively; extreme ratios are noted as unsuitable for safety-critical legal use. For hands-on testing of AQEA compression and steering: Live demo (no sign-up): https://demo.aqea.ai Web platform: https://platform.aqea.ai CLI tool: https://compress.aqea.ai Keywords: RAG, embedding compression, legal AI, retrieval safety, semantic twins, liability trap, vector database optimization Document ID: AQEA-LT-2026-001 | January 2026 | NextX AG, Switzerland