Cognaptus Insights

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.

AgentSearchBench shows why finding the right AI agent requires execution evidence, not just pretty descriptions.