A post describes a parameter-free token-surprise metric and a 64-token cache intended to address catastrophic forgetting in linear attention. It claims to outperform full-attention baselines.
The post presents HOLA, an approach combining a parameter-free token-surprise metric with a 64-token cache to address catastrophic forgetting in models using linear attention. According to the source, the method outperforms full-attention baselines; it provides no details about the comparison setup or metrics.
The topic may interest engineers balancing inference cost, memory retrieval, and cache size. Performance claims should be assessed in the context of the reported task and tests. If you use AI to study or apply the idea, avoid submitting internal data, identifiers, or credentials; use synthetic or authorized examples and follow your organization’s privacy rules.