As LLMs reshape software development, integrating LLM-augmented practices into SE education has become imperative. While existing studies explore LLMs' educatio...
High-capacity kernel Hopfield networks exhibit a 'Ridge of Optimization' characterized by extreme stability. While previously linked to 'Spectral Concentration,...
We present Areon, a family of latency-friendly, stake-weighted, multi-proposer proof-of-stake consensus protocols. By allowing multiple proposers per slot and o...
Biological neurons exhibit remarkable intelligence: they maintain internal states, communicate selectively with other neurons, and self-organize into complex gr...
Fall detection for elderly care using non-invasive vision-based systems remains an important yet unsolved problem. Driven by strict privacy requirements, infere...
Reservoir computing (RC) is a powerful framework for predicting nonlinear dynamical systems, yet the role of reservoir topology$-$particularly symmetry in conne...
We liberate Equilibrium Propagation (EP) from the limit of infinitesimal perturbations by establishing a finite-nudge foundation for local credit assignment. By...
We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. E...
While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, ...
Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments - human...
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually ...
Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We att...
