1 citations · 2 across the 9 of their papers we have counts for
9 papers
Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration
Maksim Sheverev, David Finkelstein, Sergey Nikolenko
Long-term memory is becoming a core component of LLM agents, but most memory benchmarks evaluate conversations or compact summaries, while research agents need to restore evidence…
AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
Andrey Podivilov, Vadim Lomshakov, Sergey Savin +4
We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people w…
Sparse Prefix Caching for Hybrid and Recurrent LLM Serving
Mikhail Shirokikh, Sergey Nikolenko
Prefix caching is a key latency optimization for autoregressive LLM serving, yet existing systems assume dense per-token key/value reuse. State-space models change the structure of…
Query2Diagram: Answering Developer Queries with UML Diagrams
Oleg Baryshnikov, Anton M. Alekseev, Sergey I. Nikolenko
Software documentation frequently becomes outdated or fails to exist entirely, yet developers need focused views of their codebase to understand complex systems. While automated re…
LAMeD: LLM-generated Annotations for Memory Leak Detection
Ekaterina Shemetova, Ilya Shenbin, Ivan Smirnov +5
Static analysis tools are widely used to detect software bugs and vulnerabilities but often struggle with scalability and efficiency in complex codebases. Traditional approaches re…
Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option
Konstantin Yakovlev, Sergey Nikolenko, Andrey Bout
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and…
Robust AI-Generated Text Detection by Restricted Embeddings
Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva +4
Growing amount and quality of AI-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the g…
Improving Interpretability and Robustness for the Detection of AI-Generated Images
Tatiana Gaintseva, Laida Kushnareva, German Magai +5
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suff…
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule
Andrey Bout, Alexander Podolskiy, Sergey Nikolenko +1
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data. Sufficient amounts of high-quality manually annotated data are not availab…