When AI Reviews AI: Turning Foundation Models into Safety Inspectors
A mechanism-first reading of how REACT and SemaLens use LLMs and VLMs to make safety-critical AI systems more inspectable without pretending that AI can certify itself.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Who Owns Your Words? Copyright, LLMs, and the Quiet Arms Race Over Training Data
A mechanism-first look at how copyright-detection pipelines turn LLM memorization into an operational audit signal, without pretending it is courtroom proof.
Benchmarks Without Borders: Inside the Moduli Space of AI Psychometrics
A mechanism-first guide to why AI-agent evaluation should measure structured coverage across benchmark families, not worship individual benchmark scores.
Consciousness, Capabilities, and Catastrophe: Why Your Future AI Overlord Might Feel Nothing
A mechanism-first reading of why AI existential risk depends more on capability and objectives than on whether a machine has inner experience.
Diffusion Unchained: How SimDiff Turns Chaos Into Forecasting Clarity
A mechanism-first reading of SimDiff, showing how a simpler diffusion model turns probabilistic sample diversity into stronger time-series point forecasts.
Dreams Decoded: When Vision–Language Models Learn to Read Your Brain Waves
A careful look at why generic vision–language models fail on EEG sleep staging, and what task-specific visual alignment changes for clinical AI.
Enviro-Mental Gymnastics: Why Cross-Environment Agents Still Trip Over Their Own Feet
AutoEnv shows why agent learning needs diverse environment testing, adaptive learning methods, and fewer victory laps from single-demo performance.
How to Make Neural Networks Talk: Register Automata as Their Unexpected Interpreters
A mechanism-first reading of how deterministic register automata can turn black-box sequence models into interpretable, robustness-checkable surrogates.
Prints Charming: How Reward Models Finally Got Serious About Long-Horizon Reasoning
A mechanism-first reading of PRInTS, a reward-modeling approach that improves long-horizon information-seeking agents by scoring information gain and compressing trajectory history.
Agents Behaving Badly: Why 'Agentic AI' Needs Adult Supervision
A mechanism-first reading of why LLM-based agents need explicit architectures, communication rules, incentives, norms, trust models, and institutional supervision before they can become reliable business systems.