ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG

2026-01-08

arXiv.org

AI Summary

This work addresses the degradation of semantic understanding and generation quality in retrieval-augmented generation (RAG) systems caused by excessive context compression. To mitigate this issue, the authors propose ArcAligner, a lightweight embedding module that dynamically modulates internal information processing through an adaptive gating mechanism and a recursive alignment strategy. ArcAligner effectively leverages highly compressed contexts without sacrificing compression efficiency, thereby preserving critical semantic signals. The method achieves significant performance gains on knowledge-intensive tasks—particularly multi-hop reasoning and long-tail question answering—while maintaining high compression rates. Experimental results across multiple benchmarks demonstrate that ArcAligner consistently outperforms existing context compression approaches, highlighting its effectiveness in enhancing RAG system performance under stringent compression constraints.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Retrieval-Augmented Generation (RAG) helps LLMs stay accurate, but feeding long documents into a prompt makes the model slow and expensive. This has motivated context compression, ranging from token pruning and summarization to embedding-based compression. While researchers have tried''compressing''these documents into smaller summaries or mathematical embeddings, there is a catch: the more you compress the data, the more the LLM struggles to understand it. To address this challenge, we propose ArcAligner (Adaptive recursive context *Aligner*), a lightweight module integrated into the language model layers to help the model better utilize highly compressed context representations for downstream generation. It uses an adaptive''gating''system that only adds extra processing power when the information is complex, keeping the system fast. Across knowledge-intensive QA benchmarks, ArcAligner consistently beats compression baselines at comparable compression rates, especially on multi-hop and long-tail settings. The source code is publicly available.

Problem

• Retrieval-Augmented Generation
• context compression
• compressed context embeddings
• LLM understanding
• information loss

Innovation

• ArcAligner
• context compression
• adaptive gating
• RAG
• compressed embeddings