REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling
Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K tokens only through architectural workarounds or by stretching billion-parameter LLMs. We propose REIGN (Refurbished Embeddings with Integrated Guidance Networks), a contrastively trained bi-encoder that operates on sequences of contextualised chunk embeddings from a frozen Guidance Network (GN) rather than on raw tokens. REIGN targets multi-chunk inputs, primarily for document-to-document retrieval; single-chunk inputs stay with the GN. Decoupling token-level processing from document-level reasoning, and caching the GN embeddings ...
| Authors | Devrim Çavuşoğlu, Emre Akbaş |
|---|---|
| Primary category | cs.CL |
| Published | 30 August 2026 |
| Last updated at source | 1 September 2026 |
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