Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds
Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these distant cognitive leaps remain poorly understood. Traditional attention-based interpretability often fails to capture true semantic proximity due to routing artifacts like attention sinks. In this paper, we bypass attention weights to directly analyze the dynamic geometry of the hidden state manifold, proving that deep LLM latent spaces natively organize into Small-World networks. By sparsifying the continuous similarity matrices of long-context representations into unweighted graphs, we trace the connectivity between highly disjoint semantic ...
| Authors | Md. Faiyaz Abdullah Sayeedi |
|---|---|
| Primary category | cs.CL |
| Published | 18 August 2026 |
| Last updated at source | 21 August 2026 |
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