Yiqing Xie

As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, serving two roles: generating user turns and providing evaluation signals. Yet, these simulations are frequently assumed to be faithful to real human behaviors, often without rigorous verification. We formalize the Sim2Real gap in user simulation and present the systematic study running the full $\tau$-bench protocol with real humans (451 participants, 165 tasks), benchmarking 31 LLM simulators across proprietary, open-source, and specialized families using the User-Sim Index (USI), a metric we introduce to quantify how well LLM simulators resemble real user interactive behaviors and feedback. Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity, creating an "easy mode" that inflates agent success rates above the human baseline. In evaluations, real humans provide nuanced judgments across eight quality dimensions while simulated users produce uniformly more positive feedback; rule-based rewards are failing to capture rich feedback signals generated by human users. Overall, higher general model capability does not necessarily yield more faithful user simulation. These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle and motivate improved models for user simulation.

Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during training. We introduce MetaLint, a meta-learning framework that formulates code linting as an instruction-following task, where a model evaluates whether code adheres to a natural language specification of best practices. In contrast to prior work that trains models to detect violations from a fixed set of best practices, MetaLint evaluates code against a provided natural language specification, enabling test-time control over which practices to enforce and generalization to unseen or evolving rules without retraining. We demonstrate that models trained solely on synthetic data generated from automatic linters still generalize to harder, context-dependent best practices for which such linters are not available. To evaluate generalization beyond such easy signals, we introduce a human-curated benchmark of hard best practices inspired by Python Enhancement Proposals (PEPs). On this benchmark, MetaLint substantially improves performance without explicit fine-tuning on target best practices and exhibits strong easy-to-hard generalization. Qwen3-4B achieves a 2.7x detection F-score gain (25.9% → 70.4%), the highest recall, and a 26.7% localization F-score, matching larger models such as o3-mini. These gains generalize across programming languages, model families, scales, reasoning settings, and linter sources. We release the code and benchmark to support reproducibility and future work.