6 citations · 6 across the 13 of their papers we have counts for
13 papers
Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective
Chenye Ke, Zirui Liu, Qi Liu +4
Detecting pretraining data in large language models is challenging because high likelihood can reflect either training exposure or strong generalization. In the joint space of pred…
Framework and Benchmark for Code-Driven Agentic Testing in Web Development
Bin Hong, Zhenchao Zhang, Jiyuan He +2
End-to-end GUI testing is essential for verifying web applications, yet existing evaluations rely on predefined checklists and are confined to the data and frameworks of web genera…
Learning What to Remember and What to Internalize in LLM Self-Evolution via Adaptive Memory-Parameter Coordination
Tianyun Ji, Zhenya Huang, Jiayu Liu +3
Large language model agents increasingly operate in dynamic environments where tool interfaces, APIs, and user requirements change after deployment. Existing self-evolution methods…
MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research
Junjie Zhang, Jiayu Liu, Wenbin Liu +11
Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated pr…
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?
Yuxuan Sun, Yuze Zhao, Yufeng Wang +6
Evaluating software engineering capabilities has become a core component of modern large language models (LLMs); however, the key bottleneck hindering further scaling lies not in t…
What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
Yuze Zhao, Junpeng Fang, Lu Yu +6
Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the claim that code improves rea…
VERDICT: Verifiable Evolving Reasoning with Directive-Informed Collegial Teams for Legal Judgment Prediction
Hui Liao, Chuan Qin, Yongwen Ren +4
Legal Judgment Prediction (LJP) predicts applicable law articles, charges, and penalty terms from case facts. Beyond accuracy, LJP calls for intrinsically interpretable and legally…
Step-Level Sparse Autoencoder for Reasoning Process Interpretation
Xuan Yang, Jiayu Liu, Yuhang Lai +3
Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning. However, their reasoning patterns remain too complicated…
UniCog: Uncovering Cognitive Abilities of LLMs through Latent Mind Space Analysis
Jiayu Liu, Yinhe Long, Zhenya Huang +1
A growing body of research suggests that the cognitive processes of large language models (LLMs) differ fundamentally from those of humans. However, existing interpretability metho…
Verifying Large Language Models' Reasoning Paths via Correlation Matrix Rank
Jiayu Liu, Wei Dai, Zhenya Huang +2
Despite the strong reasoning ability of large language models~(LLMs), they are prone to errors and hallucinations. As a result, how to check their outputs effectively and efficient…
Foundation of Intelligence: Review of Math Word Problems from Human Cognition Perspective
Zhenya Huang, Jiayu Liu, Xin Lin +6
Math word problem (MWP) serves as a fundamental research topic in artificial intelligence (AI) dating back to 1960s. This research aims to advance the reasoning abilities of AI by…
Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Bin Hong, Jiayu Liu, Kai Zhang +3
Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs…
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
Zhiyuan Ma, Jiayu Liu, Xianzhen Luo +3
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems. However, current models face two m…