Cognaptus Insights

A close reading of LEAFE, a reflective-experience training framework that shifts AI agents from blind retry loops toward internalized recovery behavior.

How SurgΣ turns fragmented surgical videos, labels, and reasoning traces into a reusable data infrastructure for surgical foundation models.

SocialOmni shows why audio-visual AI needs to be tested not only for what it understands, but for who it tracks, when it enters, and how it responds.

A mechanism-first reading of how inverse specification rewards train slide-generation agents to preserve intent, not merely produce prettier decks.

A close look at why conformal factuality can make RAG systems statistically safer while making their answers less useful, less robust, and more expensive unless teams measure the right things.

A mechanism-first reading of TED, a framework for evaluating whether AI agents actually complete workflows across different user behaviors, not merely sound helpful while wandering through them.

A human-centered reading of why standard counterfactual-explanation metrics fail as proxies for what users actually judge as good explanations.

A business-focused reading of ALTK, showing why reliable AI agents need lifecycle middleware around tool calls, JSON outputs, silent failures, and final responses—not just a stronger model.

A mechanism-first reading of a proposed artificial psyche architecture, and why its practical value lies less in human-like emotions than in need-aware control for autonomous agents.

OpenSeeker shows why the next moat in deep-search agents may be data synthesis pipelines rather than model size or reinforcement-learning theater.