32 citations · 161 across the 38 of their papers we have counts for
41 papers
Retrieval Capacity of Self-Attention Under Competition
Timur Mudarisov, Mikhail Burtsev, Radu State
How many tokens from its context does a language model actually use, and what determines that number? We study this question through self-attention. Without retraining, we retain o…
The Geometry of Inference in Transformer Residual Streams
Timur Mudarisov, Mikhail Burtsev, Radu State
Transformer language models build predictions through successive residual updates, but how their representations become specific to an eventual outcome remains unclear. We study th…
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…
Recursive Criticality of AI Self-Improvement
Mikhail Burtsev
AI is increasingly used in the R\&D process that produces future AI systems. We study the conditions under which this feedback becomes self-amplifying. Our model describes how the…
MARS: Multi-Specialist LLM Relay System for Competitive Programming
Andrei Mikhailov, Mikhail Burtsev, Alsu Sagirova
Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner,…
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…