Retrieval-augmented generation and structured agent memory are commonly treated as competing ways to ground large language models. Our results show that their relative value changes with the task and that neither retrieved similarity nor graph association is sufficient evidence for an answer. We present a task-adaptive...
A. Volkov, Taras Pustovoy, Dmitriy Istomin et al.· Machine Learning and Knowled...· 0 citations
Machine learning models for steel property prediction routinely report high-quality metrics with R² > 0.85, yet these results rely on random splits that allow similar grades in both train and test sets. We present SteelAgent, an interactive system that exposes a critical generalization gap: the same models drop from R²...
A. Volkov, R. Sultimov, Mikhail Kuzin et al.· Proceedings of the Thirty-Fi...· 0 citations
UrbanMix is presented, an interactive simulation platform that embeds cognitively diverse agents (human drivers, cautious AVs, aggressive AVs, and delivery robots) with distinct behavioral profiles inside a Simulation of Urban MObility (SUMO) framework of real urban road networks.
R. Sultimov, D. Efimov, I. Novikov et al.· Proceedings of the Thirty-Fi...· 0 citations
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