A mechanism-first reading of how tensor factorization turns noisy autorater outputs into human-aligned, fine-grained AI evaluation under limited annotation budgets.
Nano-EmoX shows why emotional AI should be designed as a perception-to-understanding-to-interaction system, not as a pile of sentiment classifiers wearing a lab coat.
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.