14 papers
Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
Evgenii Kuriabov, David Miller, Jia Li
In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our method is an optimization framewo…
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…
Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
Bowen Liu, Zhi Wu, Runquan Xie +2
Reinforcement Learning from Verifiable Rewards (RLVR) is bottlenecked by data: existing synthesis pipelines rely on expert-written code or fixed templates, confining growth to inst…
PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
Xiting Liu, Yuetong Liu, Yitong Zhang +2
As Large Language Models (LLMs) are increasingly integrated into software development workflows, their trustworthiness has become a critical concern. However, in dependency recomme…
Learning Performance Maximizing Ensembles with Explainability Guarantees
Vincent Pisztora, Jia Li
In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being…
FeatBench: Towards More Realistic Evaluation of Feature-level Code Generation
Haorui Chen, Chengze Li, Jia Li
Evaluating Large Language Models (LLMs) on repository-level feature implementation is a critical frontier in software engineering. However, establishing a benchmark that faithfully…