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?
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
Flow, Don’t Hallucinate: Turning Agent Workflows into Reusable Enterprise Assets
ReusStdFlow shows how enterprises can turn scattered agent workflows into reusable, retrieval-backed automation assets instead of asking LLMs to regenerate fragile workflow graphs from scratch.