ArcAligner: Helping AI use compressed context without losing accuracy
LLMs with Retrieval-Augmented Generation (RAG) work better when they read lots of context — but long prompts are slow and pricey. Compressing the context helps, yet models often lose the thread and answer worse.
ArcAligner is a lightweight module that helps models make sense of highly compressed context. It "aligns" the model’s internal reasoning to the compact representation and uses adaptive gating to add extra compute only when needed.
- Better accuracy at similar compression levels
- Especially strong on multi-hop and long-tail questions
- Keeps latency and cost in check
From Jianbo Li, Yi Jiang, Sendong Zhao, Bairui Hu, Haochun Wang, and Bing Qin. Paper and code: https://arxiv.org/abs/2601.05038v1
Paper: https://arxiv.org/abs/2601.05038v1
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