9 papers
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…
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…
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…
Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
Tatiana Petrova
Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature conditio…
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…
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…
From Multi-Agent Systems and the Semantic Web to Agentic AI: A Unified Narrative of the Web of Agents
Tatiana Petrova, Boris Bliznioukov, Aleksandr Puzikov +1
The Web of Agents (WoA) transforms the document-centric Web into an environment of autonomous agents acting on users' behalf, a vision newly tractable as large language models (LLM…