Cognaptus Insights

PrivacyBench reveals why personalized RAG assistants can recognize secrets yet still expose them—and why reliable privacy controls must begin before retrieval.

How selective reuse of validated deployment traces can quietly turn ordinary supervised fine-tuning into an implicit reinforcement-learning loop.

GenZ reverses the usual LLM feature-discovery workflow by letting proprietary data identify useful distinctions before asking a foundation model to explain them.

A practical reading of the Correction Acceleration Ratio, which exposes why the most accurate 3D detector is not always the cheapest annotation assistant.

Historical schedules contain both operating rules and emergency compromises; this paper shows how to extract the former without institutionalizing the latter.

ROME shows that competitive agent performance depends less on possessing the largest model than on operating a disciplined learning loop around execution, verification, training, and control.

STAgent shows how a stable tool sandbox, aggressive log curation, and model-relative training can turn operational data into a specialized planning agent.

A smart-building benchmark shows why LLM agents are already useful for grounded device operations—and why financial reasoning still belongs behind deterministic controls.

NestBrowse shows that better browser agents may depend less on larger models or longer contexts than on controlling which information reaches the reasoning loop.

BOAD shows that coding-agent performance depends less on assembling more agents than on discovering a small team, assigning individual credit, and controlling what each agent needs to remember.