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Ivan Oseledets

TTOpt: A Maximum Volume Quantized Tensor Train-Based Optimization and Its Application to Reinforcement Learning
2022 · Konstantin Sozykin, Andrei Chertkov, et al. · neural information processing systems · 13
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PROTES: Probabilistic Optimization with Tensor Sampling
2023 · Anastasiia Batsheva, Andrei Chertkov, et al. · neural information processing systems · 9
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The Shape of Learning: Anisotropy and Intrinsic Dimensions in Transformer-Based Models
2024 · Anton Razzhigaev, Matvey Mikhalchuk, et al. · Findings of the Association for Computational Linguistics: EACL · 4
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Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?
2026 · Korznikov, Anton, Galichin, Andrey, et al. · arXiv (Cornell University) · 3
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Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities
2026 · Li, Pengyi, Goncharova, Elizaveta, et al. · arXiv (Cornell University) · 1
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Smoothed Embeddings for Certified Few-Shot Learning
2022 · Mikhail Pautov, Olesya Kuznetsova, et al. · neural information processing systems · 1
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TensorSpectra: Bridging the Spectral and Tensor Worlds for High-Dimensional PDEs
2026 · Hu, Bingcheng, Yu, Tianchi, et al. · arXiv (Cornell University)
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Separating Thinking from Spelling for Efficient Generative Recommendation
2026 · Gusak, Danil, Volodkevich, Anna, et al. · arXiv (Cornell University)
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From Stories to Features: Scaling Concept Vectors for AI Alignment & Interpretability
2026 · Scherbakov, Dmitry, Chernikov, Oleg, et al. · arXiv (Cornell University)
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TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
2026 · QuocViet Pham, Elvir Karimov, et al. · Lecture notes in computer science
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What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling
2026 · Shakhvalieva, Yuliana, Kharchev, Dmitrii, et al. · arXiv (Cornell University)
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Reasoning on the Simplex: Geometric Fixed-Point Models
2026 · Daulbaev, Talgat, Glazkov, Ilya, et al. · arXiv (Cornell University)
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LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models
2026 · Tikhonov, Pavel, Tutubalina, Elena, et al. · arXiv (Cornell University)
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Change the Product, Keep the Parameters: Associative Algebra Layers for Transformers
2026 · Koziev, Ilya, Oseledets, Ivan · arXiv (Cornell University)
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Evaluating Losslessness in Speculative Decoding Under Finite-Precision Inference
2026 · Koziev, Ilya, Sinev, Leonid, et al. · arXiv (Cornell University)
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Structured Transforms for Low-Overhead Quantization of Language Models
2026 · Cherniuk, Daria, Rudikov, Alexander, et al. · arXiv (Cornell University)
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EvoMem: Memory-Augmented Evolution for Code Optimization
2026 · Volkov, Viktor, Khrulkov, Valentin, et al. · arXiv (Cornell University)
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Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning
2026 · Beresnev, Dmitry, Makharev, Vladimir, et al. · arXiv (Cornell University)
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One Task Vector is not Enough: A Large-Scale Study for In-Context Learning
2026 · Association for Computational Linguistics 2026, Oseledets, Ivan, et al. · Underline Science Inc.
OCC-RAG: Optimal Cognitive Core for Faithful Question Answering
2026 · Savkin, Maksim, Goncharov, Mikhail, et al. · arXiv (Cornell University)
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Harnessing non-adversarial robustness in large language models
2026 · Zhou, Qinghua, Aleshina, Ellina, et al. · arXiv (Cornell University)
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TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
2026 · Ivanov, Arseny, Kholkin, Sergei, et al. · arXiv (Cornell University)
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OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models
2026 · Aliev, Ali, Garifullin, Kamil, et al. · arXiv (Cornell University)
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LLM-Guided Evolutionary Search for Algebraic T-Count Optimization
2026 · Fisher, Daniil, Khrulkov, Valentin, et al. · arXiv (Cornell University)
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Feature Drift: How Fine-Tuning Repurposes Representations in LLMs
2026 · Association for Computational Linguistics 2026, Dontsov, Alexey, et al. · Underline Science Inc.
Works count: 36
Citation count: 22
H-index: 3
I10-index: 1
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