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Elena Tutubalina

Elena Tutubalina has 5 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning; often with Andrey Kuznetsov, Andrey Moskalenko, Daria Pugacheva.

5 papers in our library · 2026

OpenAlex · h-index 1 · 39 works · 13 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 62 times in our library · ⭐ Paper Stars ranking: #12693

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🤝AIRI - Artificial Intelligence Research InstituteMoscow · Russia2026 · 13 paperscurrent🔬Institute for System ProgrammingMoscow · Russia2026 · 1 papercurrent🎓National Research University Higher School of EconomicsMoscow · Russia2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArts and Humanities · 4 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksDecision Sciences · 1 workStatistics, Probability and Uncertainty · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 works

 Works by year

2020s

20265 papers

 Papers

The most cited first (OpenAlex and our library).

  1. One Task Vector is not Enough: A Large-Scale Study for In-Context Learning (2026 · 1 citation)
    Task vectors are hidden-state activations that may compress the demonstrations of an in-context learning (ICL) prompt into a single representation of the task.
  2. Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models (2026)
    Vision-Language-Action (VLA) models are usually tested on short, clean instructions. Real users, however, rephrase commands and wrap them in chatter. This paper measures how much such linguistic noise hurts robot policies. The authors built an evaluation protocol with two kinds of perturbation:
  3. Confidence Leaps in LLM Reasoning: Early Stopping and Cross-Model Transfer (2026)
    Reasoning LLMs are often assumed to become more certain gradually as they think. This paper tests that assumption on multiple-choice tasks and finds that certainty often arrives abruptly. The authors split chain-of-thought traces from Qwen3-32B, QwQ-32B and DeepSeek-R1 Distill Llama-70B into chunks at shift markers…
  4. Evolutionary Search for Automated Design of Uncertainty Quantification Methods (2026)
    Detecting hallucinations in large language models often relies on uncertainty quantification (UQ) scores. These scores are usually hand-crafted from researchers' intuitions, which limits how far they scale. The authors instead let an LLM invent such scores automatically. They use the EvoTune framework for LLM-driven…
  5. SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space (2026)
    The paper examines how reasoning segmentation models react when users phrase the same request differently.

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 62 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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