Cognaptus Insights

LeanCat reveals why verified AI reasoning still fails when agents must navigate large libraries, preserve abstraction, and construct missing conceptual bridges.

MI-ZO shows how a lightweight inference-time controller can improve 2D-trained vision-language models on 3D scenes by learning which views contain useful, non-redundant evidence.

HiGR shows that generative recommendation becomes practical only when item representation, slate planning, and preference alignment are designed as one coordinated system.

Encyclo-K replaces fixed benchmark questions with dynamically composed knowledge statements, creating a reusable evaluation engine that exposes the gap between knowing facts and reliably combining them.

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.