Cognaptus Insights

A business-focused reading of why agentic interpretability systems can look successful under replication metrics while still failing the harder test of trustworthy evaluation.

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.

A mechanism-first reading of why agentic AI turns EU privacy and security compliance from a model checklist into an operational governance problem.

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.

A comparison-based reading of why Gaussian likelihoods can make scientific AI confidently wrong, and how simulation-based inference changes the uncertainty workflow.

A mechanism-first reading of PRIOR, a single-stage Isaac Lab framework that makes humanoid locomotion more robust by simplifying the training stack rather than adding more machinery.

A practical reading of why model accuracy, trust surveys, and explanation interfaces are weak substitutes for measuring whether human–AI teams are actually ready to work safely.

A mechanism-first reading of citation-grounded dialogue training, showing why zero hallucination can still leave enterprises with a trust problem.

A mechanism-first reading of why joint model compression depends not only on pruning, quantization, and tuning choices, but on the order in which they disturb the model.

A mechanism-first reading of how temporal co-occurrence and compression can reveal what passages do in sequence, not merely what they mean.