Ze Xu
Publications
Surrogate-Based Prevalence Measurement for Large-Scale A/B Testing
Online media platforms often need to measure how frequently users are exposed to specific content attributes in order to evaluate trade-offs in A/B experiments. A direct approach is to sample content, label it using a high-quality rubric (e.g., an expert-reviewed LLM prompt), and estimate impression-weighted prevalence. However, repeatedly running such labeling for every experiment arm and segment is too costly and slow to serve as a default measurement at scale. We present a scalable \emph{surrogate-based prevalence measurement} framework that decouples expensive labeling from per-experiment evaluation. The framework calibrates a surrogate signal to reference labels offline and then uses only impression logs to estimate prevalence for arbitrary experiment arms and segments. We instantiate this framework using \emph{score bucketing} as the surrogate: we discretize a model score into buckets, estimate bucket-level prevalences from an offline labeled sample, and combine these calibrated bucket level prevalences with the bucket distribution of impressions in each arm to obtain fast, log-based estimates. Across multiple large-scale A/B tests, we validate that the surrogate estimates closely match the reference estimates for both arm-level prevalence and treatment--control deltas. This enables scalable, low-latency prevalence measurement in experimentation without requiring per-experiment labeling jobs.
Unmasking Reasoning Processes: A Process-aware Benchmark for Evaluating Structural Mathematical Reasoning in LLMs
Recent large language models (LLMs) achieve near-saturation accuracy on many established mathematical reasoning benchmarks, raising concerns about their ability to diagnose genuine reasoning competence. This saturation largely stems from the dominance of template-based computation and shallow arithmetic decomposition in existing datasets, which underrepresent reasoning skills such as multi-constraint coordination, constructive logical synthesis, and spatial inference. To address this gap, we introduce ReasoningMath-Plus, a benchmark of 150 carefully curated problems explicitly designed to evaluate structural reasoning. Each problem emphasizes reasoning under interacting constraints, constructive solution formation, or non-trivial structural insight, and is annotated with a minimal reasoning skeleton to support fine-grained process-level evaluation. Alongside the dataset, we introduce HCRS (Hazard-aware Chain-based Rule Score), a deterministic step-level scoring function, and train a Process Reward Model (PRM) on the annotated reasoning traces. Empirically, while leading models attain relatively high final-answer accuracy (up to 5.8/10), HCRS-based holistic evaluation yields substantially lower scores (average 4.36/10, best 5.14/10), showing that answer-only metrics can overestimate reasoning robustness.