Category
Clear interpretation of AI evaluations, methods, limitations, and security relevance.
How to read frontier model evaluations as dated evidence about capabilities, safeguards, and limits—and turn the results into bounded security decisions.
A practical framework for reporting test-time compute, samples, search, tools, retries, quality, latency, and cost in reasoning benchmarks without hiding the budget.
A practical framework for detecting benchmark saturation and choosing whether to keep, complement, refresh, or replace an AI evaluation.
A practical audit for benchmark contamination, data leakage, tool exposure, and evidence-based LLM evaluation claims.
A practical method for validating LLM judges with human reference evidence, bias testing, calibration, slice analysis, and drift monitoring.
A practical guide to measuring agentic systems as evaluated workflows, including tools, environments, recovery, safety constraints, and reproducibility.
How to interpret AI security benchmark attack-success rates with clear denominators, attacker budgets, defenses, utility, uncertainty, and evidence.
A practical guide to interpreting coding AI benchmarks: compare function completion, repository repair, agent scaffolds, test quality, and reproducibility.
A practical guide to evaluation harness reproducibility: identify hidden variables, report the full evaluated system, and label benchmark comparisons honestly.
A practical architecture for building private LLM evaluation data that measures real tasks without leaking the holdout into development.
A workload-first method for turning production requirements into a defensible AI model selection decision.
A practical framework for interpreting AI benchmark evidence across datasets, metrics, harnesses, model settings, reliability, cost, and security.