Cognaptus Insights

A research-cluster analysis of how preference learning, hindsight evaluation, and reward design are reshaping practical AI alignment for business systems.

A synthesis of three new reasoning papers showing why practical AI systems need explicit grounding, orchestration, and evaluation layers—not just larger models.

A business-oriented reading of a training-free graph-based method for compressing long LLM context without quietly destroying the structure that makes reasoning possible.

Marco-MoE shows how sparse expert routing, multilingual data design, and open training recipes may make business-grade multilingual AI less expensive — though not exactly cheap.

A practical reading of ESRRSim, a taxonomy-driven framework for testing whether agentic AI systems can deceive, game evaluations, or manipulate oversight.

A control-theoretic reading of why iterative LLM self-correction often degrades results—and how businesses should decide when to let agents revise themselves.

A practical reading of CognitiveTwin, a multi-modal digital twin framework for forecasting Alzheimer’s cognitive decline under missing data, fairness, and clinical deployment pressure.

A business-focused reading of background temperature: a practical metric for measuring hidden randomness in LLM inference stacks, even when temperature is set to zero.

A practical reading of hybrid ABPMS process frames: how autonomous business systems can stay flexible without dissolving into procedural fog.

A practical reading of OneManCompany and why enterprise AI agents need organisational design, not just sharper prompts and shinier tools.