arXiv AI

Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models

The paper introduces Internalizer, a portable Context-to-Parameter Mapping hypernetwork that generates document‑specific LoRA adapters for the 284B‑parameter DeepSeek v4 Flash model. Unlike previous work limited to models up to 14B parameters, Internalizer’s architecture places most parameters in a model‑agnostic trunk, allowing cheap training on small models before porting to the large one. On unseen 4096‑token documents, the generated adapters achieve 84.9% top‑1 and 97.8% top‑5 teacher‑forced accuracy, far surpassing the base model’s 63.4% and 83.5% respectively, using only a three‑word instruction in the context window.

arXiv AI

Oct 31

Encode Once, Answer Many: Internalizing Context via Parametric Flow

Encode Once, Answer Many: Internalizing Context via Parametric Flow proposes CLIK, a context-conditioned parameter generator that transforms a given context into LoRA parameters in a single forward pass, allowing a frozen base model to answer queries without re-reading the context. By internalizing knowledge into model weights, CLIK eliminates per-task optimization and test-time gradient updates, amortizing the cost of reading a document across multiple queries. Experiments show CLIK improves over a no-context base model and outperforms other hypernetwork generators, though it still lags behind models that read the context directly, and it generalizes to multi-turn dialogue without specialized training.

By 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

llms nlp fine-tuning

arXiv AI

Jul 7

SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass

arXiv:2602. 06358v3 Announce Type: replace-cross Abstract: We propose SHINE (Scalable Hyper In-context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapters for large language models (LLMs).

By Yewei Liu, Xiyuan Wang, Yansheng Mao, Yoav Gelbery, Haggai Maron, Muhan Zhang

llms nlp fine-tuning

arXiv Machine Learning

Jul 23

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.

By Nischay Dhankhar, Dos Baha, Abulhair Saparov

llms fine-tuning

arXiv Machine Learning

Oct 19

HydraLLM: Adaptive Width Slicing for Elastic Text and Vision-Language Models

arXiv:2610.23455v1 Announce Type: cross Abstract: LLMs and LVLMs are increasingly deployed across heterogeneous hardware, ranging from data-center GPUs to resource-constrained edge devices. Their fix...

By Oscar Kemme, Janek Haberer, Ali Hojjat, Olaf Landsiedel

llms rag efficiency multimodal benchmarks

arXiv AI

Jun 19

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

arXiv:2606. 19348v1 Announce Type: cross Abstract: We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.

By DeepSeek-AI, Anyi Xu, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, Chenchen Ling, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chengyu Hou, Chenhao Xu, Chenze Shao, Chong Ruan, Conner Sun, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Donghao Li, Dongjie Ji, Erhang Li, Fang Wei, Fangyun Lin, Fangzhou Yuan, Feiyu Xia, Fucong Dai, Guangbo Hao, Guanting Chen, Guoai Cao, Guolai Meng, Guowei Li, Han Yu, Han Zhang, Hanwei Xu, Hao Li, Haofen Liang, Haoling Zhang, Haoming Luo, Haoran Wei, Haotian Yuan, Haowei Zhang, Haowen Luo, Haoyu Chen, Haozhe Ji, Hengqing Zhang, Honghui Ding, Hongxuan Tang, Huanqi Cao, Huazuo Gao, Hui Qu, Hui Zeng, J Yang, JQ Zhu, Jia Luo, Jia Song, Jia Yu, Jialiang Huang, Jialu Cai, Jian Liang, Jiangting Zhou, Jiasheng Ye, Jiashi Li, Jiaxin Xu, Jiewen Hu, Jieyu Yang, Jin Chen, Jin Yan, Jingchang Chen, Jingli Zhou, Jingting Xiang, Jingyang Yuan, Jingyuan Cheng, Jingzi Zhou, Jinhua Zhu, Jiping Yu, Joseph Sun, Jun Ran, Junguang Jiang, Junjie Qiu, Junlong Li, Junmin Zheng, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Kexing Zhou, Kezhao Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Wang, Leyi Xia, Li Zhang, Liang Zhao, Lihua Guo, Lingxiao Luo, Linwang Ma, Linyan Zhu, Litong Wang, Liyu Cai, Liyue Zhang, Longhao Chen, MS Di, MY Xu, Max Mei, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Mingxu Zhou, Minmin Han, Ning Wang, Panpan Huang, Panpan Wang, Peixin Cong, Peiyi Wang, Peng Zhang, Qiancheng Wang, Qihao Zhu, Qingyang Li, Qinyu Chen, Qiushi Du, Qiwei Jiang, Rui Tian, Ruifan Xu, Ruijie Lu, Ruiling Xu, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runqian Chen, Runqiu Yin, Runxin Xu, Ruomeng Shen, Ruoyu Zhang, Ruyi Chen, SH Liu, Shanghao Lu, Shangmian Sun, Shangyan Zhou, Shanhuang Chen, Shaofei Cai, Shaoheng Nie, Shaoqing Wu, Shaoyuan Chen, Shengding Hu, Shengyu Liu, Shiqiang Hu, Shirong Ma, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, Shuying Yu, Songyang Zhou, Tao Ni, Tao Yun, Tian Jin, Tian Pei, Tian Ye, Tianle Lin, Tianran Ji, Tianyi Cui, Tianyuan Yue, Tingting Yu, Tun Wang, W Zhang, WL Xiao, Wangding Zeng, Wei An, Weilin Zhao, Wen Liu, Wenfeng Liang, Wenjie Pang, Wenjing Luo, Wenjing Yao, Wenjun Gao, Wenkai Yang, Wenlve Huang, Wenqing Hou, Wentao Zhang, Wenting Ma, Xi Gao, Xiang He, Xiangwen Wang, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaokang Zhang, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingchen Liu, Xingkai Yu, Xingyou Li, Xinyu Yang, Xinyu Zhang, Xu Chen, Xuanyu Wang, Xuecheng Su, Xueyin Chen, Xuheng Lin, Xuwei Fu, YC Yan, YQ Wang, YW Ma, Yanfeng Luo, Yang Zhang, Yanhong Xu, Yanru Ma, Yanwen Huang, Yao Li, Yao Li, Yao Xu, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Qian, Yi Shao, Yi Yu, Yichao Zhang, Yifan Ding, Yifan Shi, Yijia Wu, Yiliang Xiong, Yiling Ma, Ying He, Ying Tang, Ying Zhou, Yingjia Luo, Yinmin Zhong, Yishi Piao, Yisong Wang, Yixiang Zhang, Yixiao Chen, Yixuan Tan, Yixuan Wei, Yiyang Ma, Yiyuan Liu, Yonglun Yang, Yongqiang Guo, Yongtong Wu, Yu Wu, YuKun Li, Yuan Cheng, Yuan Ou, Yuanfan Xu, Yuanhao Li, Yuduan Wang, Yuehan Yang, Yuer Xu, Yuhan Wu, Yuhao Meng, Yuheng Zou, Yukun Zha, Yunfan Xiong, Yupeng Chen, Yuping Lin, Yuqian Cao, Yuqian Wang, Yushun Zhang, Yuting Yan, Yutong Lin, Yuxian Gu, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuxuan Zhou, Yuyang Zhou, Yuzhen Huang, ZF Wu, Zehao Wang, Zehua Zhao, Zehui Ren, Zekai Zhang, Zhangli Sha, Zhe Fu, Zhe Ju, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zheren Gao, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhixian Huang, Zhixuan Chen, Zhiyu Wu, Zhizhou Ren, Zhongyu Wu, Zhuoshu Li, Zhuping Zhang, Zian Xu, Zihao Wang, Zihua Qu, Zihui Gu, Zijia Zhu, Zilin Li, Zipeng Zhang, Ziwei Xie, Ziyi Gao, Ziyi Wan, Zizheng Pan, Zongqing Yao

llms efficiency benchmarks

arXiv Computation and Language

1d ago

Accelerating Long-Context Decoding in Large-Scale Hybrid LLMs Without Retraining

arXiv:2611.06133v1 Announce Type: new Abstract: The full-attention layers of large language models (LLMs) make every generated token more expensive than the previous one, slowing models down on long-...

By Jo\~ao Eduardo Batista, Emil Vatai, Mohamed Wahib

llms

arXiv Computation and Language

4d ago

HyperRepair: Recovering Forgotten General Capabilities of Fine-Tuned Language Models

HyperRepair is a framework that restores the general capabilities of fine‑tuned language models while keeping their domain specialization, using only inference time. It learns a hypernetwork from repairs on source models, which generates a Repairing Side Net (RSN) that selectively corrects the model based on activation fingerprints. Evaluated on two model families, 12 LoRA adaptation datasets, and 15 general benchmarks, HyperRepair retains 99.5% of fine‑tuned domain performance and 93.4% of the base model’s general performance.

By Junrong Tang, Jianghui Wang, Hao Kong, Yang Lin, Yibo Yang

llms fine-tuning benchmarks

arXiv AI

Jun 4

L$^3$: Large Lookup Layers

arXiv:2601. 21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP "experts.

By Albert Tseng, Christopher De Sa

llms rag nlp efficiency

arXiv Machine Learning

Sep 22

LoRA-generating hypernetworks for efficient on-device LLM generative personalization

arXiv:2609.24979v1 Announce Type: new Abstract: On-device large language models (`LLMs'), e.g. running on mobile phones, are ripe for improvement via personalization. The limited compute resources of...

By Sean Augenstein, Li Ding, Jihwan Lee, Keith Rush, Andrey Zhmoginov

llms fine-tuning

arXiv AI

Sep 17

Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data

The paper proposes an Infinite-Parameter LLM architecture that generates and adapts its weights from live interaction data using a compact hypernetwork and Bayesian updating, allowing the model to learn from real-time user input rather than relying solely on static pretraining. This approach keeps the stored footprint fixed while effectively enabling an infinite set of weights, potentially improving compute efficiency, freeing context windows, and providing persistent, generalizable knowledge across turns. The authors also outline an evaluation protocol to compare this method against traditional in-context learning and retrieval techniques.

By Jinli Hu, Ross M. Clarke, Yichuan Zhang, Jos\'e Miguel Hern\'andez-Lobato

llms

arXiv Computation and Language

2d ago

Library of Layers (LoL): Adaptively Ordering Transformer Layers at Runtime

The paper introduces Library of Layers (LoL), an architecture that dynamically orders transformer layers during inference, allowing a single model to adapt its computation to the task at hand. LoL’s single hyperparameter—the size of its global library of transformer blocks—enables a smooth transition between parameter reuse and sparsity, unifying dense, depth‑recurrent, and mixture‑of‑experts trade‑offs. Experiments show LoL matches or exceeds these baselines on text, synthetic Sudoku and maze tasks, and demonstrates task‑specific routing in multilingual and safety‑focused settings.

By Abdellah Rahmani, Sean McLeish, Ang Li, Sanae Lotfi, Pascal Frossard, Micah Goldblum

llms efficiency safety

arXiv Machine Learning

Jul 1

Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining

arXiv:2509. 10406v4 Announce Type: replace Abstract: Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs.

By Rupert Mitchell, Kristian Kersting

llms efficiency

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