3 papers
cs.CL2026
Extending LLM Context via Associative Recurrent Memory
Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8
Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and lin…
cs.CL2026
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…
cs.LG2025
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…