Cognaptus Insights

OpenRad shows that the bottleneck in radiology AI is no longer only model invention, but the messy infrastructure needed to discover, verify, compare, and reuse models.

A mechanism-first reading of T3RL, showing why self-consensus can collapse into confident error and how tool-verified voting offers a more stable reward signal for test-time reinforcement learning.

A mechanism-first reading of Conformal Policy Control, and why calibrated deviation from a safe policy may matter more for enterprise autonomy than another round of post-training bravado.

A mechanism-first reading of how LLM agents can make formal planning systems easier to question, revise, and trust without pretending to replace the planner.

A comparison-based reading of Pencil Puzzle Bench, showing why verifiable feedback loops may matter as much as raw reasoning effort for enterprise AI agents.

A mechanism-first reading of the Artificial Agency Program, and why business AI should be evaluated by how it spends observation, action, compute, and communication budgets.

DARE-bench shows why AI data-science agents need verifiable workflow discipline, not just better final-answer accuracy.

LemmaBench shows why research-level AI evaluation depends less on harder problem lists than on turning live expert work into fair, self-contained, contamination-resistant tests.

Why longer LLM context windows can still weaken reasoning, and how businesses should design retrieval, memory, and evaluation around usable context rather than raw token capacity.

A mechanism-first reading of how limited pallets and material-kitting rules turn flexible job-shop scheduling into a shared-resource learning problem.