Proteus: Incremental Memory Activation for Long-Context Sequence Modeling
Proteus delays how much memory a model can use, so later context has room instead of fighting early-token clutter.
The paper argues that static memory lets early tokens consume too much capacity before compression is needed. Its mechanism, incremental memory activation, starts with a bottleneck and unlocks more memory as the sequence grows. The authors say Proteus can be added to memory architectures such as SWLA, Comba, Titans, and Hope-Attention at no extra cost. They report consistent gains in language modeling, reasoning, and long-context retrieval, with larger improvements at longer context lengths. ArXiv · AI/CL/LG's note
The paper argues that static memory lets early tokens consume too much capacity before compression is needed. Its mechanism, incremental memory activation, starts with a bottleneck and unlocks more memory as the sequence grows. The authors say Proteus can be added to memory architectures such as SWLA, Comba, Titans, and Hope-Attention at no extra cost. They report consistent gains in language modeling, reasoning, and long-context retrieval, with larger improvements at longer context lengths. ArXiv · AI/CL/LG's note
score 6