ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG

• URL: http://arxiv.org/abs/2601.05038v1
• Date: Thu, 08 Jan 2026 15:44:52 GMT
• Title: ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG
• Authors: Jianbo Li, Yi Jiang, Sendong Zhao, Bairui Hu, Haochun Wang, Bing Qin,
• Abstract summary: ArcAligner is a lightweight module integrated into the language model layers.<n>It uses an adaptive ''gating'' system that only adds extra processing power when the information is complex.
• Score: 46.14646374046088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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