Adversarial Generators and Failure Analysis
Use saved training trajectories and a controlled intervention to explain a failure, separating equilibrium theory from observed empirical stability.
Use saved training trajectories and a controlled intervention to explain a failure, separating equilibrium theory from observed empirical stability.
Demonstrate all 3 repaired bugs and defend why a passed gradient check is necessary but not sufficient for generalization.
Use the tractable fixture and posterior predictive results to separate approximation, sampling and model errors; justify one revised modeling assumption.
Map each implemented component to the original paper and explain 2 reduced-model failure modes without claiming full-scale reproduction.
Use calibration and shift measurements to justify a monitoring or abstention threshold, and give an explicit confounding counterexample to causal feature-attribution claims.
Defend a causal claim or refusal using the DAG, numerical estimates and a counterexample showing why predictive model fit cannot establish identification.
Defend a threshold using decision costs and explain why accuracy alone can reward a useless classifier.
Defend a clustering or anomaly decision while stating what cannot be inferred from an unlabeled dataset.
Defend the architecture comparison using learning curves, error categories and the cost of each improvement.
Deliver an operational runbook linking alerts to investigation, intervention and rollback criteria without promising automatic drift correction.
Defend an OLS-versus-ridge choice on a tabular dataset, including a case where extrapolation is unsafe.
Submit a 6–10 page research-style report for external criticism, record the response and revise one claim or experiment based on substantive feedback.
Diagnose an unseen broken configuration, propose one intervention and justify it from measured evidence.
Explain one sampler failure with numerical or statistical evidence and delimit reduced-scale claims.
Defend a metric or kernel choice and explain why distance concentration can undermine the method.
Defend a deployment or no-deployment recommendation in a 6–10 page report with evidence, limitations, rollback and unresolved risks.
Write a bounded conclusion that identifies the strongest baseline, weakest evidence and one untested deployment assumption.
Write a model-use recommendation with stakeholder questions, measurement limitations and conditions for declining deployment.
Deliver a dataset card and defend one ambiguous labeling or exclusion decision with its impact on the target population.
Apply the bound to a measured overparameterized-model example, identify each unmet assumption and explain why any vacuous value cannot support a generalization claim.
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