Author

Yaroslav Aksenov

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#machine learning Preprint May 2025

Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders

KronSAE is proposed, a design that factorizes the latent space into heads and forms post-latent features as pairwise compositions of lower-dimensional pre-latents using mAND, a differentiable AND-like interaction that imposes a compositional co-activation prior while remaining compatible with standard SAE objectives and variants.

Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev et al. · 1 citation

#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

This work introduces 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.

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