REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling

arXiv:2608.29899 · cs.CL, cs.AI, cs.IR · Submitted 2026-08-30 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2026-08-30

Updated: 2026-09-01

Code: https://github.com/devrimcavusoglu/reign

Terminology

• Embeddings — 50× in this paper · explained in 2 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Guidance Network — 21× in this paper
• Transformer — 18× in this paper · explained in 2 episodes
• Ablation — 16× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Provenance — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval
• Longformer: The Long-Document Transformer
• Linq-Embed-Mistral Technical Report
• Scaling Laws for Neural Language Models
• Gemini Embedding: Generalizable Embeddings from Gemini
• Towards General Text Embeddings with Multi-stage Contrastive Learning
• Representation Learning with Contrastive Predictive Coding
• GPT-4o System Card
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Jasper and Stella: distillation of SOTA embedding models
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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