32 citations · 161 across the 38 of their papers we have counts for
8 papers · 1 filter
Predictive Geometry of Hidden Trajectories in Transformers
Timur Mudarisov, Mikhail Burtsev, Tatiana Petrova +1
Decoder-only transformers are trained only through a terminal next-token prediction loss, yet this loss constrains every intermediate hidden state through the fixed downstream comp…
Feed-Forward Steering in Transformer Residual Dynamics
Timur Mudarisov, Mikhail Burtsev, Radu State
Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere. We extend this framework by incorporating the feed-forward network (FF…
Geometry-Guided Layerwise FFN Width Allocation in Transformers
Timur Mudarisov, Mikhail Burtsev, Radu State
Feed-forward networks (FFNs) account for a large fraction of Transformer parameters, yet their hidden width is usually constant across depth. We ask whether this capacity can inste…
Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
Artyom Sorokin, Nazar Buzun, Alexander Anokhin +7
Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most e…
Limitations of Normalization in Attention Mechanism
Timur Mudarisov, Mikhail Burtsev, Tatiana Petrova +1
This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective…
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
Ivan Rodkin, Daniil Orel, Konstantin Smirnov +9
Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-autom…