A mechanism-first reading of AgenticSciML, a multi-agent framework that turns scientific machine learning design into an evaluated evolutionary search rather than a one-shot prompting exercise.
A new reject-option framework separates noisy data from insufficient data, giving AI systems a sharper way to decide when prediction should become deferral.
A mechanism-first reading of why urban-planning AI may need verifiable reasoning agents, not just better prediction models.
A new benchmark shows where LLM agents can clean predictive-maintenance logs today, and where industrial deployment still needs rules, temporal logic, and human discipline.
A mechanism-first look at Dynamic Memory Alignment, a RAG framework that turns live human feedback into retrieval updates without retraining the generator.
A sharp look at why real-time AI agents need latency-aware architectures, not merely bigger models or longer reasoning traces.
A mechanism-first reading of a battlefield decision-support prototype, and what it teaches business leaders about simulation, option generation, and human control.
ORCHID shows that legal AI becomes useful when it is engineered as an auditable workflow, not a confident oracle.
CORE shows how digital pathology can align multi-stain whole-slide images by separating fast tissue-level registration from slower nuclei-level precision.
A mechanism-first look at how prompt difficulty prediction can route reasoning tasks to cheaper LLMs without simply dumbing down the system.