Measuring Coding Challenge Competence With APPS

Abstract

Abstract: While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importance, there has been surprisingly little work on evaluating code generation, and it can be difficult to accurately assess code generation performance rigorously. To meet this challenge, we introduce APPS, a benchmark for code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate satisfactory Python code. S

Research Question

Does the adversarial robustness gap between DeepSeek-R1 and o1-preview on legal reasoning tasks under negation-based token perturbations replicate across code generation benchmarks like Codeforces or APPS when using the S* selection mechanism?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance