A mechanism-first reading of how higher-order action regularization can make reinforcement learning policies smoother, less switch-happy, and more practical for HVAC and other physical-control systems.
Project Ariadne shows how counterfactual interventions can audit whether an LLM’s reasoning trace actually causes its answer, or merely decorates it.
Falcon-H1R shows that the economics of reasoning depends less on parameter count alone and more on architecture, curated training, verifiable rewards, and confidence-aware inference.
A mechanism-first reading of why long chain-of-thought hallucinations behave like evolving states, and how streaming hidden-state probes could turn reasoning reliability into an operational signal.
A practical reading of simulated reasoning: why reasoning models are no longer mere stochastic parrots, but still not grounded human reasoners.
A mechanism-first reading of ACCD, a memory-guided framework that makes coordinated behavior detection more adaptive, label-efficient, and operationally useful.
A mechanism-first reading of PedX-LLM, a vision-and-knowledge-enhanced local LLM for generalizable pedestrian crossing behavior inference.
A mechanism-first reading of DA-DPO, showing why multimodal preference tuning fails when easy preference pairs dominate the learning signal.
A mechanism-first reading of a semantic-distance football DSS: how tactical intuition becomes an auditable recommender, and why feasibility is not yet proof of better match outcomes.
How AgenticDomiKnowS turns low-resource neuro-symbolic programming from expert-only craft into a staged, reviewable workflow.