Alois Knoll

Large language models can automate complex model transformations in automotive engineering, cutting manual effort significantly while ensuring structural validity.

D-SafeMPC achieves safer and more efficient robotic planning by seamlessly integrating diffusion models with model predictive control, overcoming key limitations of both approaches.

Force-aware evaluation metrics reveal hidden performance gaps in humanoid robot control that conventional benchmarks miss.

A fragmented landscape of LLM agent communication protocols reveals a surprising trend toward hybrid payloads and runtime schema negotiation, but no single protocol can achieve all desired efficiencies.

Task knowledge can be efficiently reused across heterogeneous agents, slashing tracking errors by up to 99.79% while using significantly less data.

Energy consumption in NLP could plummet by up to 93% with the SpikeDecoder, a fully spiking neural network implementation of the Transformer decoder.

$\omega$-EVA's innovative Envision--Verify--Act loop allows policies to foresee the consequences of their actions, enhancing decision-making in embodied AI.

LLMs can intelligently route sensor modalities in autonomous vehicles, slashing computation by 6% without sacrificing driving performance.

Automating SDV testing is now closer to reality: a GenAI pipeline turns natural language requirements into runnable test scripts with 89% success.