A mechanism-first reading of Prompt-to-Parts, where language models become useful for physical design not by imagining perfect 3D objects, but by compiling intent into constrained, inspectable part assemblies.
A mechanism-first reading of state-augmented disassembly graphs and why circular-economy triage is a sequential decision problem, not a green ranking exercise.
A mechanism-first reading of proportional duty: why uncertainty should shift responsibility toward verification instead of becoming an excuse for inaction.
A mechanism-first reading of GAR, an adversarial reinforcement learning framework that teaches LLMs through slice-level criticism rather than final-answer applause.
A mechanism-first reading of patchwork AGI: why collective agent systems may become the real control surface for safety, governance, and enterprise deployment.
CitySeeker shows why urban AI agents fail not because they cannot see streets, but because they cannot reliably translate vague human needs into grounded city actions.
A case-first reading of a hybrid ML and RAG-LLM framework for forecasting lung-cancer pain episodes before the ward has to react.
A control-theoretic reading of the Social Responsibility Stack, and why responsible AI needs monitors, thresholds, rollback paths, and governance authority—not just principles.
A mechanism-first reading of TOGGLE, a framework that turns LLM compression into a constrained engineering problem using temporal logic, Bayesian optimization, and explicit behavioral thresholds.
A case-first reading of PCML, a method for turning black-box agent behavior into interpretable probabilistic capability maps.