When the Muse Has a GPU: Teaching a Machine to Write Poetry
A mechanism-first reading of a seven-month GPT-4 poetry workshop—and why the real business lesson is workflow design, not instant synthetic genius.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Do They Mean It? Testing Whether AI Actually ‘Reasons’ Behind the Wheel
CARE-Drive turns AI driving explanations into a testable question: do model decisions actually respond to human-relevant reasons, or merely sound as if they do?
From Guesswork to Generative Foresight: Why Diffusion Models May Fix Multi-Agent Blind Spots
GlobeDiff shows why partial observability in multi-agent systems is less a memory problem than a generative state-inference problem.
From Scaling to Steering: Operationalizing Control in Frontier Models
A practical reading of risk-aware alignment research: why frontier AI control is becoming an engineering layer, not a slogan.
One-Hot Walls, LLaMA Doors: Teaching AI the Language of Buildings
What BIM subtype classification reveals about using LLM embeddings as a semantic label space instead of one-hot targets.
Sim2Realpolitik: Why Your AI Needs a Twin Before It Faces Reality
A mechanism-first reading of why simulated data and digital twins are becoming the rehearsal infrastructure for AI systems that must survive the real world.
Thinking in New Directions: When LLMs Learn to Evolve Their Own Concepts
A mechanism-first reading of Recursive Concept Evolution, a proposed way for frozen language models to add reusable concept subspaces instead of merely searching harder through tokens.
Cause & Effect, But Make It Continuous: Rethinking Primary Causation in Hybrid AI Systems
A mechanism-first reading of how primary causation can be formalized when discrete actions trigger continuous change.
Cut the Loops: When Web Agents Learn to Think in DAGs
A mechanism-first reading of WebClipper, showing how graph-based trajectory pruning can make deep research web agents cheaper, faster, and sometimes more accurate.
Double Lift-Off: Learning to Reason Without Ever Building the Model
A mechanism-first reading of how implicit learning and lifted SOS inference can answer relational probabilistic queries from partial observations without constructing a full probabilistic model.