49 citations · 115 across the 16 of their papers we have counts for
17 papers
Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models
Xiangming Gu, Soham De, Larisa Markeeva +2
Large Reasoning Models (LRMs) have shown remarkable performance on challenging questions, such as math and coding. However, to obtain a high quality solution, one may need to sampl…
The Illusion of Stochasticity in LLMs
Xiangming Gu, Soham De, Michalis Titsias +3
In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) operating as agents. Agentic systems ar…
How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models
Dharshan Kumaran, Stephen M Fleming, Larisa Markeeva +8
Large language models (LLMs) exhibit strikingly conflicting behaviors: they can appear steadfastly overconfident in their initial answers whilst at the same time being prone to exc…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva +4
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady prog…
Transformers meet Neural Algorithmic Reasoners
Wilfried Bounsi, Borja Ibarz, Andrew Dudzik +5
Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unma…
The CLRS-Text Algorithmic Reasoning Language Benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz +7
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning foc…
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Viorica Pătrăucean, Lucas Smaira, Ankush Gupta +21
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GP…
TT-SDF2PC: Registration of Point Cloud and Compressed SDF Directly in the Memory-Efficient Tensor Train Domain
Alexey I. Boyko, Anastasiia Kornilova, Rahim Tariverdizadeh +4
This paper addresses the following research question: ``can one compress a detailed 3D representation and use it directly for point cloud registration?''. Map compression of the sc…
Laser: Latent Set Representations for 3D Generative Modeling
Pol Moreno, Adam R. Kosiorek, Heiko Strathmann +6
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a…
TAP-Vid: A Benchmark for Tracking Any Point in a Video
Carl Doersch, Ankush Gupta, Larisa Markeeva +6
Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences ab…
A Generalized Lottery Ticket Hypothesis
Ibrahim Alabdulmohsin, Larisa Markeeva, Daniel Keysers +1
We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters. We present ev…
Knowledge distillation: A good teacher is patient and consistent
Lucas Beyer, Xiaohua Zhai, Amélie Royer +3
There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In…
Towards Understanding Normalization in Neural ODEs
Julia Gusak, Larisa Markeeva, Talgat Daulbaev +3
Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly…
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva +3
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the litera…
MUSCO: Multi-Stage Compression of neural networks
Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev +3
The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank…
QTT-isogeometric solver in two dimensions
L. Markeeva, I. Tsybulin, I. Oseledets
The goal of this paper is to develop a numerical algorithm that solves a two-dimensional elliptic partial differential equation in a polygonal domain using tensor methods and ideas…