The Sealed Score: Why AI Evaluation Needs an Exam Day
A mechanism-first reading of the LLM Olympiad proposal, and why sealed, frozen, centrally run evaluations may become useful evidence for AI procurement and governance.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Thinking in Libraries: Why Humans (and AI) Solve Hard Problems by Rewriting the Search Space
A mechanism-first reading of online library learning: why reusable abstractions reduce search cost more than they shorten final answers.
When Agents Go Off-Script: The Quiet Collapse of Prompted Identity
A mechanism-first reading of why multi-agent systems can drift from prompted roles, form endogenous stances, and rebuild social order through language.
Braiding the Future: Why Autonomous Systems Need Topology, Not Just Trajectories
A mechanism-first reading of braid prediction shows why autonomous systems need to model future interaction structure, not merely forecast coordinates.
From Prompts to Policies: How Digital Twins Are Quietly Rewiring Enterprise AI Agents
A mechanism-first reading of DT-MDP-CE, a framework that turns messy enterprise agent traces into offline-learned policies for more controllable context engineering.
From Tacit to Fragmented: When Knowledge Stops Behaving
A mechanism-first reading of the GenAI SECI model, and why enterprise knowledge systems may need to stop demanding perfect manuals before they can learn.
Seeing Is Believing: Why Visual RAG Might Be the Missing Layer in Clinical AI
A visual RAG system for ophthalmology guidelines shows why clinical AI needs controlled evidence selection, not just more retrieved text.
The Cardiologist’s Copilot: Why Agentic AI Finally Understands the Human Body
A mechanism-first reading of MARCUS shows why clinical AI progress depends less on generic model scale than on domain perception, orchestration, and grounding checks.
The Mask Matters: Teaching AI What Not to See
A mechanism-first reading of SpecTM, a physics-informed masking strategy that shows why trustworthy domain AI may depend less on seeing more data and more on hiding the right signals.
The Memory That Thinks: When AI Stops Remembering and Starts Reasoning
A case-first reading of GSEM, a graph-based self-evolving memory framework that shows why useful agent memory depends less on storing more experience and more on knowing when an experience applies.