Sparse Attention Masking Effects on Llama-3.1-8B Multi-Hop Reasoning Accuracy in RULER Benchmark

Abstract

Abstract: We introduce the State Stream Transformer (SST), a novel LLM architecture that reveals emergent reasoning behaviours and capabilities latent in pretrained weights through addressing a fundamental limitation in traditional transformer models: the lack of latent computational continuity across autoregressive generations in the state space. SST introduces a sliding window latent state (FFN) cache with weighted decay that maintains and evolves persistent latent processes throughout autoregressive generations. Through controlled experiments comparing base and SST architectures using the same frozen

Research Question

How does sparse attention masking in Llama-3.1-8B affect multi-hop reasoning accuracy on the RULER benchmark compared to dense attention baselines?

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