Belief Is a Graph: Why LLM Agents Need Structured Minds
A mechanism-first reading of dynamic belief graphs, and why enterprise LLM agents need structured, auditable mental states rather than longer prompts.
Cognaptus Insights
Industry thoughts, trends, and commentary.
DIAL-KG: When Knowledge Graphs Finally Learn Like Humans
A mechanism-first reading of DIAL-KG, showing why incremental knowledge graphs need memory, governance, and soft deprecation—not just better extraction.
From One Shot to Many: Why AI Should Stop Guessing and Start Exploring
FormalEvolve shows why some AI systems should stop searching for one perfect answer and start building verified repertoires of usable alternatives.
Learning from Failure: When LLMs Finally Pay Attention
A mechanism-first reading of HeRL, a reinforcement learning framework that turns failed LLM outputs and unmet rubrics into guided exploration signals.
The Cost of Thinking Twice: Why Agentic AI Needs a CFO
A mechanism-first reading of utility-guided LLM agent orchestration, and why production agents need cost control as much as tool access.
The Mirage of Understanding: When AI Explains Without Knowing
A business-focused reading of why agentic interpretability systems can look successful under replication metrics while still failing the harder test of trustworthy evaluation.
Act While Thinking: When AI Agents Learn to Multitask (Finally)
A mechanism-first reading of PASTE, a speculative tool-execution system that reduces agent latency by predicting not only which tool comes next, but also how its arguments can be derived safely.
Agents Without Borders: When AI Stops Asking and Starts Acting
A mechanism-first reading of why agentic AI turns EU privacy and security compliance from a model checklist into an operational governance problem.
Seeing the Invisible: When MRI Learns to Think Like PET
A mechanism-first reading of PASTA, a pathology-aware diffusion framework that translates MRI into synthetic FDG-PET while keeping Alzheimer’s-relevant signals in view.
The Likelihood Illusion: When Gaussian Comfort Meets Reality
A comparison-based reading of why Gaussian likelihoods can make scientific AI confidently wrong, and how simulation-based inference changes the uncertainty workflow.