From the 1 of 6 linked papers with an AI index.
6 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…
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…
Diagonal Batching Unlocks Parallelism in Recurrent Memory Transformers for Long Contexts
Danil Sivtsov, Ivan Rodkin, Gleb Kuzmin +2
Transformer models struggle with long-context inference due to their quadratic time and linear memory complexity. Recurrent Memory Transformers (RMTs) offer a solution by reducing…
Associative Recurrent Memory Transformer
Ivan Rodkin, Yuri Kuratov, Aydar Bulatov +1
This paper addresses the challenge of creating a neural architecture for very long sequences that requires constant time for processing new information at each time step. Our appro…
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
Yuri Kuratov, Aydar Bulatov, Petr Anokhin +4
In recent years, the input context sizes of large language models (LLMs) have increased dramatically. However, existing evaluation methods have not kept pace, failing to comprehens…