The Self-Driving Portfolio: When Your CIO Becomes an API
A mechanism-first reading of agentic strategic asset allocation: what becomes programmable, what remains governance, and why the paper is not a simple performance claim.
Cognaptus Insights
Industry thoughts, trends, and commentary.
The Token Trial: Putting Words on the Stand in LLMs
A mechanism-first reading of VISTA, a lightweight token-attribution method that helps teams audit prompt semantics without mistaking embedding disruption for true LLM reasoning.
When AI Answers the Wrong Question — And Why That Matters More Than Being Wrong
A mechanism-first reading of Trace Inversion, a new abstention method that treats hallucination as query misalignment rather than mere answer error.
When AI Grades Itself: The Quiet Failure of LLM-as-a-Judge in Clinical Translation
A comparative reading of why fluent LLM-generated clinical translations can look excellent to AI judges while remaining misaligned with radiologist judgment.
When Language Models Ask for Help: The Curious Case of Uncertain AI
A comparison-based reading of ASK, an uncertainty-gated RL-LM architecture that shows why language models are useful in agentic systems only when routed carefully.
Agents That Remember: Why HERA Turns RAG into a System, Not a Trick
A mechanism-first reading of HERA, a training-free multi-agent RAG framework that turns past execution experience into orchestration policy, prompt evolution, and practical lessons for enterprise AI systems.
Autonomous Memory: When AI Starts Debugging Itself
A closer look at how Omni-SimpleMem shows that autonomous research pipelines can improve agent memory by finding the boring system failures humans usually miss.
From Static Scripts to Self-Evolving Minds: The Rise of Experience-Driven AI Counselors
A mechanism-first reading of PsychAgent and what its experience-driven learning loop implies for enterprise AI systems beyond psychological counseling.
Pre-Decision Intelligence: When AI Decides Before It Thinks
A mechanism-first reading of new evidence that reasoning models may encode tool-use decisions before visible chain-of-thought begins.
The Ethics Stress Test: When AI Morality Cracks Under Pressure
A mechanism-first reading of AMST, a multi-round framework for testing whether LLM safety survives accumulated adversarial pressure rather than merely passing isolated prompts.