2 citations · 3 across the 15 of their papers we have counts for
16 papers
Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
Evgenii Kuriabov, Zhaoxue Tong, Jia Li
Identifying patient subgroups with heterogeneous treatment effects is central to precision medicine, yet existing approaches face a tension. Black-box methods estimate individualiz…
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…
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…
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang +18
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-iso…
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…
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…
Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks
Kevin Wang, Subre Abdoul Moktar, Jia Li +2
Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramou…
Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention
Yinbo Sun, Yuchen Fang, Zhibo Zhu +7
The rapid advancement of time series foundation models (TSFMs) has been propelled by migrating architectures from language models. While existing TSFMs demonstrate impressive perfo…
T3 Planner: A Self-Correcting LLM Framework for Robotic Motion Planning with Temporal Logic
Jia Li, Guoxiang Zhao
Translating natural language instructions into executable motion plans is a fundamental challenge in robotics. Traditional approaches are typically constrained by their reliance on…
Conda: Column-Normalized Adam for Training Large Language Models Faster
Junjie Wang, Pan Zhou, Yiming Dong +6
Large language models (LLMs) have demonstrated impressive generalization and emergent capabilities, yet their pre-training remains computationally expensive and sensitive to optimi…
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…
Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
Boyuan Zheng, Zerui Fang, Zhe Xu +13
While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use…
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
Yong Lin, Shange Tang, Bohan Lyu +8
We introduce Goedel-Prover, an open-source language model that achieves state-of-the-art (as of April 5 2025) performance in automated formal proof generation for mathematical prob…
SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction
Evgenii Kuriabov, Jia Li
Explainable machine learning (XML) has emerged as a major challenge in artificial intelligence (AI). Although black-box models such as Deep Neural Networks and Gradient Boosting of…
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…