19 citations · 22 across the 7 of their papers we have counts for
9 papers
PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agents
Huayi Lai, Shichao Song, Qingchen Yu +4
Large language model (LLM) agents are evolving from tool-calling systems that execute isolated instructions into task-oriented agents that pursue user goals through sustained, mult…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning
Qingchen Yu, Zifan Zheng, Ding Chen +4
The evaluation of large language models (LLMs) has traditionally relied on static benchmarks, a paradigm that poses two major limitations: (1) predefined test sets lack adaptabilit…
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
Zhiyu Li, Shichao Song, Hanyu Wang +19
Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…
xVerify: Efficient Answer Verifier for Reasoning Model Evaluations
Ding Chen, Qingchen Yu, Pengyuan Wang +9
With the release of OpenAI's o1 model, reasoning models that adopt slow-thinking strategies have become increasingly common. Their outputs often contain complex reasoning, intermed…
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
Qingchen Yu, Shichao Song, Ke Fang +5
As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Xun Liang, Shichao Song, Zifan Zheng +8
Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations. To address these, studies prefixed with "Self-" such as Self-Consistency, Self-Improve, a…
xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
Qingchen Yu, Zifan Zheng, Shichao Song +4
The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their perfo…
Grimoire is All You Need for Enhancing Large Language Models
Ding Chen, Shichao Song, Qingchen Yu +4
In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the I…