10 citations · 11 across the 6 of their papers we have counts for
7 papers
From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization
Ying Chang, Jiahang Xu, Xuan Feng +3
The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and i…
VeruSAGE: A Study of Agent-Based Verification for Rust Systems
Chenyuan Yang, Natalie Neamtu, Chris Hawblitzel +2
Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason about and prove code correctness remain…
Lookahead Tree-Based Rollouts for Enhanced Trajectory-Level Exploration in Reinforcement Learning with Verifiable Rewards
Shangyu Xing, Siyuan Wang, Chenyuan Yang +2
Reinforcement Learning with Verifiable Rewards (RLVR), particularly with algorithms like Group Relative Policy Optimization (GRPO), has proven highly effective in enhancing the rea…
Multilingual Routing in Mixture-of-Experts
Lucas Bandarkar, Chenyuan Yang, Mohsen Fayyaz +2
Mixture-of-Experts (MoE) architectures have become the key to scaling modern LLMs, yet little is understood about how their sparse routing dynamics respond to multilingual data. In…
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models
Junjie Ye, Caishuang Huang, Zhuohan Chen +12
Instruction following refers to the ability of large language models (LLMs) to generate outputs that satisfy all specified constraints. Existing research has primarily focused on c…
Automated Proof Generation for Rust Code via Self-Evolution
Tianyu Chen, Shuai Lu, Shan Lu +11
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction an…
AutoVerus: Automated Proof Generation for Rust Code
Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu +10
Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generatio…