8 citations · 19 across the 5 of their papers we have counts for
5 papers
Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
Artyom Sorokin, Nazar Buzun, Alexander Anokhin +7
Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most e…
HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Petr Anokhin, Roman Khalikov, Stefan Rebrikov +3
Large language models (LLMs) perform well on step-by-step reasoning benchmarks such as mathematics and code generation, yet their ability to carry out robust long-horizon planning…
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Petr Anokhin, Nikita Semenov, Artyom Sorokin +4
Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could lear…
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…
In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss
Yuri Kuratov, Aydar Bulatov, Petr Anokhin +3
This paper addresses the challenge of processing long documents using generative transformer models. To evaluate different approaches, we introduce BABILong, a new benchmark design…