AI RESEARCH
Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoning
arXiv CS.LG
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ArXi:2505.16950v4 Announce Type: replace Transformer LLMs have been shown to exhibit strong reasoning ability that scales with inference-time compute, most prominently through token-space "thinking" chains of thought. A growing line of work pushes extra computation into the model's latent space, which we term Auxiliary Latent-Space Computation (ALSC). Existing ALSC methods largely fall into three buckets: (i) token-mediated latent rollouts, (ii) residual/activation steering, and (iii) memory (KV) compression.