3 citations · 5 across the 20 of their papers we have counts for
15 papers · 1 filter
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…
HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models
Yuchen Liu, Luigi Palmieri, Lujun Li +3
Approaches to incorporating human awareness into mobile robot decision-making mainly focus on collision avoidance in low-level motion planning, often overlooking the challenges pos…
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…
Agents Don't Paginate: First-Chunk Selection for LLM Tool Responses
Tatiana Petrova, Andrei Mazniak, Radu State
Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that routinely exceed the agent's…
QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction
Yangjie Xu, Hui Huang, Li Ning +1
Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within high-dimensional vector spa…
The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models
Lujun Li, Lama Sleem, Niccolo Gentile +4
Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored. A natural extension of ``How m…
Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory
Tatiana Petrova, Evgeny Polyachenko, Radu State
We study the thermodynamic memory capacity of modern Hopfield networks (Dense Associative Memory models) with continuous states under geometric constraints, extending classical ana…
Friction-Augmented Drifting Models for Resource-Efficient Domain Translation
Arkadii Kazanskii, Tatiana Petrova, Andrey Ustyuzhanin +3
Single-step generators promise high-fidelity synthesis at a fraction of the inference and training cost of ordinary differential equation (ODE)-based flow models, a central concern…
How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues
Tatiana Petrova, Stanislav Sokol, Radu State
Which persuasion strategies, if any, are associated with donation compliance? Answering this requires fine-grained strategy labels across a full corpus and statistical tests correc…
The Necessity of Setting Temperature in LLM-as-a-Judge
Lujun Li, Lama Sleem, Yangjie Xu +4
Large Language Models (LLMs) are increasingly used as automated judges, yet decoding temperature is often set by intuition rather than evidence. We run over one million large scale…
Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments
Yangjie Xu, Lujun Li, Lama Sleem +7
Skills are getting increasingly used in recent agentic frameworks, performing robustly with proprietary LLMs. Yet, their effectiveness for small and medium-sized open-source models…
Geometric Analysis of Token Selection in Multi-Head Attention
Timur Mudarisov, Mikhal Burtsev, Tatiana Petrova +1
We present a geometric framework for analysing multi-head attention in large language models (LLMs). Without altering the mechanism, we view standard attention through a top-N sele…