Artificial Hippocampus Networks for Efficient Long-Context Modeling

• URL: http://arxiv.org/abs/2510.07318v1
• Date: Wed, 08 Oct 2025 17:59:55 GMT
• Title: Artificial Hippocampus Networks for Efficient Long-Context Modeling
• Authors: Yunhao Fang, Weihao Yu, Shu Zhong, Qinghao Ye, Xuehan Xiong, Lai Wei,
• Abstract summary: Long-sequence modeling faces a trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of growing memory in attention-based Transformers.<n>Inspired by the Multi-Store Model in cognitive science, we introduce a memory framework of artificial neural networks.<n>Experiments on long-context benchmarks LV-Eval and InfiniteBench demonstrate that AHN-augmented models consistently outperform sliding window baselines.
• Score: 17.23148291364832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in attention-based Transformers. Inspired by the Multi-Store Model in cognitive science, we introduce a memory framework of artificial neural networks. Our method maintains a sliding window of the Transformer's KV cache as lossless short-term memory, while a learnable module termed Artificial Hippocampus Network (AHN) recurrently compresses out-of-window information into a fixed-size compact long-term memory. To validate this framework, we instantiate AHNs using modern RNN-like architectures, including Mamba2, DeltaNet, and Gated DeltaNet. Extensive experiments on long-context benchmarks LV-Eval and InfiniteBench demonstrate that AHN-augmented models consistently outperform sliding window baselines and achieve performance comparable or even superior to full-attention models, while substantially reducing computational and memory requirements. For instance, augmenting the Qwen2.5-3B-Instruct with AHNs reduces inference FLOPs by 40.5% and memory cache by 74.0%, while improving its average score on LV-Eval (128k sequence length) from 4.41 to 5.88. Code is available at: https://github.com/ByteDance-Seed/AHN.

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