A new explainability framework argues that validated explanations can improve tabular prediction when they are diversified, tested for faithfulness, and revised from prediction errors.
A Prolog case study shows how AI coding agents can generate code and proofs while deterministic verification remains the acceptance gate—and specification remains the human bottleneck.
A frontier-AI treaty needs more than an expiry date: its exit rules must balance genuine risk reduction against verification, sovereignty, and geopolitical change.
A controlled comparison shows that post-training changes where confidence is useful across routing, early stopping, and completed-answer selection.
RLVP shows that scientific-code post-training improves when executable programs are graded by numerical and physical accuracy rather than rewarded for validity alone.
A financial-audit assistance system shows strong potential for ranking suspicious statements, but much weaker evidence that it can reliably explain what auditors should investigate.
BusinessCaseBench shows that frontier models already cover most expected elements of business-case analysis, while still missing the completeness needed for review-free decision support.
A controlled heart-sound experiment shows that spectrogram design matters most when model capacity is scarce, with richer front-ends preserving accuracy and reducing false alarms.
Annual satellite embeddings can improve forest-biomass monitoring not by out-sensing LiDAR, but by keeping far more field observations usable across time and geography.
A large controlled CBRN study shows why expert-like harmful output should trigger investigation, not automatically determine a model-release decision.