37 citations · 57 across the 17 of their papers we have counts for
15 papers · 1 filter
Exploring Autonomous Agentic Data Engineering for Model Specialization
Yujie Luo, Xiangyuan Ru, Jingsheng Zheng +10
Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data…
StructMem: Structured Memory for Long-Horizon Behavior in LLMs
Buqiang Xu, Yijun Chen, Jizhan Fang +5
Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answe…
LightThinker++: From Reasoning Compression to Memory Management
Yuqi Zhu, Jintian Zhang, Zhenjie Wan +7
Large language models (LLMs) excel at complex reasoning, yet their efficiency is limited by the surging cognitive overhead of long thought traces. In this paper, we propose LightTh…
Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
Zhisong Qiu, Shuofei Qiao, Kewei Xu +4
Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. Ho…
InnoGym: Benchmarking the Innovation Potential of AI Agents
Jintian Zhang, Kewei Xu, Jingsheng Zheng +10
LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness,…
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations
Yujie Luo, Zhuoyun Yu, Xuehai Wang +6
Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to in…
OceanGym: A Benchmark Environment for Underwater Embodied Agents
Yida Xue, Mingjun Mao, Xiangyuan Ru +9
We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike t…
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
Yuqi Zhu, Yi Zhong, Jintian Zhang +7
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In…
LightThinker: Thinking Step-by-Step Compression
Jintian Zhang, Yuqi Zhu, Mengshu Sun +6
Large language models (LLMs) have shown remarkable performance in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associ…
RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment
Xiaohan Wang, Xiaoyan Yang, Yuqi Zhu +7
Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks. However, they still face…
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Jia Li, Ge Li, Yunfei Zhao +15
How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories…
Agent Planning with World Knowledge Model
Shuofei Qiao, Runnan Fang, Ningyu Zhang +7
Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievement…
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Yuqi Zhu, Shuofei Qiao, Yixin Ou +7
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interact…
How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
Xin Xu, Yuqi Zhu, Xiaohan Wang +1
Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we inv…
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
Yuqi Zhu, Xiaohan Wang, Jing Chen +6
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experime…