Associative Recurrent Memory Transformer (2024 · 8 citations)
The authors address a core difficulty in long-context modeling: building a network whose cost for each new chunk of input stays fixed, however long the sequence grows.
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling (2026)
The paper asks whether multi-step reasoning in neural models reflects real generalization or memorization, and how difficulty grows with the number of required inference steps.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack ↗arXiv:2406.10149 · no summary yetYuri Kuratov et al.2024 · 88 citations
Associative Recurrent Memory TransformerarXiv:2407.04841Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.2024 · 8 citations
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute ScalingFindings of the Association for Computational LinguisticsIvan Rodkin, Daniil Orel, Konstantin Smirnov, et al.2026

Co-authors here

9 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 80 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated 1 November 2026