collaborators

9 papers

cs.SE2026

Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data

Nursultan Askarbekuly, Mohamad Al Mdfaa, Ahmed Helaly +2

Coding agents can now be left alone to improve software against a score. In this pattern--recently popularized as "autoresearch"--the agent receives a dataset, an evaluation script…

cs.CV2026

MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors

Denis Fatykhoph, Timur Akhtyamov, Konstantin Pakulev +2

Classical image correspondence is solved at the level of sparse keypoints or dense pixels, but the systems that consume these matches - object-level mapping, topological navigation…

cs.RO2026

VL-MemKnG: Hybrid Memory with a Spatio-Temporal Knowledge Graph for Question Answering over Long Egocentric Navigation Trajectories

Svetlana Lukina, Mohamad Al Mdfaa, Gloria Haro +2

Answering navigation-relevant questions over long egocentric videos requires retrieving and organizing evidence distributed across distant temporal moments while maintaining spatia…

cs.RO2026

DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation

Danil Tokhchukov, Veronika Morozova, Gonzalo Ferrer

Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world space…

cs.CV2026

CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models

Vladislav Pyatov, Gleb Bobrovskikh, Saveliy Galochkin +6

We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to s…

cs.RO2026

EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild

Timur Akhtyamov, Mohamad Al Mdfaa, Javier Antonio Ramirez Benavides +11

Data-driven navigation algorithms are critically dependent on large-scale, high-quality real-world data collection for successful training and robust performance in realistic and u…