I study how AI is changing knowledge work.

Shows that language models can agree and still be wrong, so truthfulness needs real verification rather than voting or self-consistency.

Studies how duplicated training data affects models differently as they scale, showing that repetition can change performance in ways averages hide.

Shows that repeating data inside a training set can damage language models, even when the repeated examples look harmless at first.

Studies an enterprise AI-coding mandate across developers and pull requests, finding throughput gains alongside much heavier review load.

Explains how generative benchmarks can be contaminated by test data, making models look better than they really are.

Tests whether coding agents can turn Python programs into Lean 4 specifications and proofs that verify end to end.

Builds a protocol for testing whether LLM judges of Lean 4 specs behave sensibly, turning monotonicity and stability checks into a trust signal.

Argues that machine learning conferences need a formal place for critiques and corrections, so important mistakes can be reviewed openly.

Measures how much LLM code-review outputs vary across runs, which matters when teams want reliable and repeatable review results.

Reexamines min-p sampling and finds that its claimed benefits are not supported by the available evaluation evidence.

Shows that models can predict expert code-review scores, making it cheaper to estimate code quality across large datasets.