Measure Twice, Quantize Once
A mechanism-first reading of joint neural architecture search and mixed-precision quantization for compressing LLMs without pretending the deployment pipeline is tidy.
Cognaptus Insights
Industry thoughts, trends, and commentary.
The Forecast Can Be Wrong and Still Save the Charge
A decision-focused EV charging paper shows why forecasting should be trained for downstream control quality, not prediction accuracy alone.
The Harness Wants a Promotion
HarnessX argues that agent performance is not only a model problem; the runtime scaffold around the model can be composed, evolved, gated, and even co-trained.
The Prompt Is Not the Boss
Why LLM annotation fails when the model’s internal concept boundary does not match the business definition it is supposed to apply.
Think Twice, Halt Once
Why enterprise reasoning systems need expert-grounded evaluation and adaptive compute control, not just larger models with longer answers.
Typechecked and Still Wrong
A mechanism-first read of Bidirectional Provability Fingerprinting, and why semantic certification matters when AI turns human intent into formal artifacts.
Veto Later, Repair First
A mechanism-first reading of RACL, a repair-aware decision framework that turns fixable violations into structured options instead of wasted rejections.
Design Patterns Are Not Prompt Decorations
A practical reading of how LLMs respond when asked, corrected, and shown examples while generating Java code with the Singleton pattern.
Fold Me Once: When the Demonstration Becomes the Robot Interface
Instant-Fold shows why, for deformable physical work, a single demonstration can carry more operational detail than a written instruction.
Mind the Loss Gap
Why three new AI papers point to the same operating lesson: deployment failures often live in the directions your objective forgot to supervise.