Measure Twice, Generate, Then Look Again
IterCAD shows why reliable CAD automation depends less on one-shot generation and more on closed-loop execution, visual feedback, and survivor-bias-free evaluation.
Cognaptus Insights
Industry thoughts, trends, and commentary.
No CIG, Still Checking: When Medical Guidelines Become Executable
A mechanism-first reading of an LLM-orchestrated stroke-care conformance pipeline, and what it teaches operators about turning unstructured policy into auditable process checks.
No Structure, No Glory: Why AI Cognition Has to Be Shown, Not Named
Two recent papers show why serious claims about AI cognition require evidence of internal organization, not just fluent behavior or attractive labels.
Stage Before You Shoot: Why Reliable AI Needs a Middle Game
Two very different AI papers show the same operational lesson: reliable systems work when each stage uses only the signal it can actually trust.
Stop Scaling the Wrong Thing
A practical framework for diagnosing whether AI performance failures come from data, structure, context, architecture, or adaptation calibration.
The Assistant Should Not Stop Watching to Speak
A mechanism-first reading of LyraV: why real-time video assistants need synchrony control, not just stronger video QA.
Bigger Ears Still Need a Budget
A compute-allocation reading of audio-model scaling: when to buy model capacity, when to buy context, and when to stop pretending LoRA fixes everything.
Borrowed Hands Still Need a Grip
GLAM shows how heterogeneous robot demonstrations become useful only when their effects are grounded into a target-executable latent action space.
Learning Has a Supply Chain
Why the next phase of AI learning depends on objective alignment, world feedback, action control, and the infrastructure that keeps the loop alive.
Memory Has to Earn Its Keep
A mechanism-first reading of MemOp, a closed-loop framework that treats coding-agent memory as an evaluated and optimized operational asset rather than a bigger scrapbook.