From the 1 of 10 linked papers with an AI index.
11 papers
Extending LLM Context via Associative Recurrent Memory
Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8
The paper introduces the Associative Recurrent Memory Transformer (ARMT) to enable large language models to handle much longer contexts with constant memory usage and reduced compu…
GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent
Yuri Kuratov, Matvey Kairov, Aydar Bulatov +2
Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which i…
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
Valeriy Vyaltsev, Alsu Sagirova, Anton Andreychuk +5
Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared envi…
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…
Revisiting Tree Search for LLMs: Gumbel and Sequential Halving for Budget-Scalable Reasoning
Leonid Ugadiarov, Yuri Kuratov, Aleksandr Panov +1
Neural tree search is a powerful decision-making algorithm widely used in complex domains such as game playing and model-based reinforcement learning. Recent work has applied Alpha…
Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity
Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov +1
A range of recent works addresses the problem of compression of sequence of tokens into a shorter sequence of real-valued vectors to be used as inputs instead of token embeddings o…