Daniil Medyakov

Spectral optimization finally bridges compressed communication and local steps in federated learning, achieving convergence without bounded heterogeneity.

Riemannian optimization enables a new level of control over LLM refusal behavior, outperforming traditional methods that depend on auxiliary constructs.

Associative context retrieval can dramatically amplify the effectiveness of white-box attacks on LLMs without compromising their general performance.