Author

Gleb Gerasimov

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#machine learning Feb 2025

You Do Not Fully Utilize Transformer's Representation Capacity

Layer-Integrated Memory (LIMe) is introduced, a lightweight extension that leverages existing key-value buffers and learns per-head, per-layer routing weights to integrate representations from previous layers to improve perplexity per FLOP and yield strong gains on synthetic tasks while preserving higher value-vector entropy and token separability.

Gleb Gerasimov, Yaroslav Aksenov, Nikita Balagansky et al. · arXiv.org · 4 citations

#machine learning Preprint Aug 2026

A Model with No Head and Many Thoughts

Large language models decode by projecting hidden states through a large vocabulary head at every step. This operation is computationally costly and forces all reasoning to be expressed in discrete tokens. We introduce Soft Latent Thinking, a method that replaces the LM head during reasoning with a lightweight projector, enabling autoregressive rollout in embedding space where reasoning steps remain continuous rather than tokenized. Experiments on DeepSeek-Qwen-1.5B and LLaMA-3.2-3B show that Soft Latent Thinking consistently improves pass@k across all k while reducing per-step compute during chain-of-thought. Our method achieves the highest pass@32 among all soft-thinking approaches, demonstrating that effective reasoning can be carried out in continuous space without discrete token generation.

N. Koriagin, Yaroslav Aksenov, George Bredis et al. · 0 citations