CQ or Consequences: What This LLM Benchmark Reveals About AI Requirements Work
A comparison-based reading of CompCQ shows why LLM-generated requirements work needs model portfolios, not one-model faith.
Cognaptus Insights
Industry thoughts, trends, and commentary.
CQ, AI & The Question of Questions
A controlled comparison of human, template, and LLM-generated competency questions shows why AI can accelerate requirements elicitation without replacing expert judgment.
Graph RAG, No Smoke: Why Explainable AI in Manufacturing Needs a Memory
A mechanism-first reading of how knowledge graphs and LLM-guided retrieval can make machine learning explanations in manufacturing more contextual, useful, and governable.
Lost in the Grid: Why AI Agents Still Can’t Spot the Impostor
SocialGrid shows why agent reliability depends less on model eloquence than on separating navigation, execution, and behavioral inference failures.
MARCH Orders: When AI Holds a CT Case Conference
A mechanism-first reading of MARCH, a multi-agent CT report-generation system, and what its hierarchy teaches enterprise AI about review, grounding, and controlled disagreement.
Silent Errors, Loud Consequences: ASMR-Bench and the Coming Era of AI Auditors
A research-sabotage benchmark shows why AI auditability is not a code-review feature, but an operating model for trustworthy AI work.
When AI Learns the Trick First: Why Insight Beats Brute Force in Theorem Proving
A mechanism-first reading of why explicit technique recognition may matter more than longer reasoning traces for informal theorem proving and enterprise AI workflows.
Blue Data Intelligence Layer: When SQL Meets Agents and Reality
A mechanism-first reading of Blue's Data Intelligence Layer and why enterprise AI needs data planning, registries, and fewer fantasies about one-model answers.
Scan You Believe It? Why RadAgent Makes Medical AI Show Its Work
RadAgent shows why medical AI needs auditable workflows, not just stronger black-box report generators.
Turning Heads: Why AI Still Gets Lost When It Turns Around
A mechanism-first reading of VRUBench: why models can parse viewpoint rotations yet still fail to bind spatial state to the right observation.