The Ethics of Not Knowing: When Uncertainty Becomes an Obligation
A mechanism-first reading of proportional duty: why uncertainty should shift responsibility toward verification instead of becoming an excuse for inaction.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Adversaries, Slices, and the Art of Teaching LLMs to Think
A mechanism-first reading of GAR, an adversarial reinforcement learning framework that teaches LLMs through slice-level criticism rather than final-answer applause.
AGI by Committee: Why the First General Intelligence Won’t Arrive Alone
A mechanism-first reading of patchwork AGI: why collective agent systems may become the real control surface for safety, governance, and enterprise deployment.
CitySeeker: Lost in Translation, Found in the City
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.
Painkillers with Foresight: Teaching Machines to Anticipate Cancer Pain
A case-first reading of a hybrid ML and RAG-LLM framework for forecasting lung-cancer pain episodes before the ward has to react.
Stack Overflow for Ethics: Governing AI with Feedback, Not Faith
A control-theoretic reading of the Social Responsibility Stack, and why responsible AI needs monitors, thresholds, rollback paths, and governance authority—not just principles.
TOGGLE or Die Trying: Giving LLM Compression a Spine
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.
When Black Boxes Grow Teeth: Mapping What AI Can *Actually* Do
A case-first reading of PCML, a method for turning black-box agent behavior into interpretable probabilistic capability maps.
Artism, or How AI Learned to Critique Itself
A mechanism-first reading of Artism, a dual-engine AI framework that turns generative art into a self-critical loop rather than another novelty machine.
Delegating to the Almost-Aligned: When Misaligned AI Is Still the Rational Choice
A decision-theoretic guide to deciding when imperfectly aligned AI systems are still worth delegating to.