Encode Once, Answer Many: Internalizing Context via Parametric Flow

Authors: Xinhao Zhong , Mengxuan Wu , Chonghua Liao , Linxi Xie , Ruicheng Feng , Yanpei Guo , Hao Tan , Jiaxin Hong , Jason Li , Panpan Zhang , Ziheng Qin , Qinglin Lu , Yuzhang Shang , Tongliang Liu , Damian Borth , Haggai Maron , Yongxiang Hua , Zhixiang Wei , Jian Hu , Yunfei Wu , Xing Sun , Xin Li , Haoyu Cao , Zhangyang Wang , Kai Wang

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Abstract: Large language models are typically adapted to a task either by placing knowledge in the input context, which keeps it external and re-encodes it on every call, or by fine-tuning, which internalizes it but demands a costly round of optimization per task. We aim to internalize the knowledge carried by a context directly into model parameters, without per-task optimization and without keeping the context in the input at inference time. To this end, we propose CLIK, a context-conditioned parameter generator that maps a given context to LoRA parameters for internalizing knowledge in a single forward pass through a Transformer encoder with cross-attention, requiring no test-time gradient updates. Once the generated LoRA is inserted into a frozen base model, the model answers downstream queries from the query alone, having internalized the context into its weights rather than re-reading it. Because the adapter depends only on the context, it is generated once and reused for every query on the same document, so the cost of reading that document is amortized across queries. On document question answering, CLIK substantially improves over the no-context base model and outperforms existing hypernetwork-based generators, while remaining behind a base model that reads the context directly, and it carries over to multi-turn dialogue without any multi-turn-specific training. Analysis further shows that the context-dependent part of the generated adapter concentrates in a compact and specific substructure.

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Artificial Intelligence (cs.AI)

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arXiv:2610.38356 [cs.AI]

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arXiv:2610.38356v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.38356

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From: Mengxuan Wu [ view email ]

[v1]

Wed, 28 Oct 2026 06:55:19 UTC (405 KB)

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