Research area 04
Safety, alignment, and evaluation
Making sure systems do what their operators intend, fail in ways that are detectable, and are measured by benchmarks that cannot be gamed by accident.
- safety
- alignment
- benchmarks
- leakage
- uncertainty
Most AI failures in practice are quiet: a model that scored well because the test set leaked into training, a metric that stopped measuring what it was meant to, a confident prediction with no uncertainty attached. Safety and alignment work at LAISER starts with these ordinary failures and builds outward to the harder questions of intent, misuse, and robustness.
We hold our own research to the same standard: baselines first, point-in-time correctness, adversarial review before promotion, and failed hypotheses recorded as evidence.
What we work on
- Leakage and point-in-time discipline. Taxonomies of leakage and tooling that enforces “only what was knowable at the time”.
- Evaluation protocols. Regression evals that gate every change to a system’s core contract.
- Red-teaming our own claims. Independent review agents and humans whose job is to break the result before anyone relies on it.