56 citations · 97 across the 26 of their papers we have counts for
26 papers
When Your Agent Opens the Chat App: Agent-Controlled Search over Raw Chat Logs Rivals Structured Memory
Ruizhe Li, Licheng Zhang, Benfeng Xu +3
Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or knowledge graphs before any qu…
Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation
Yuhang Zhu, Mingxuan Du, Benfeng Xu +3
Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversations with RPAs for experiences…
BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms
Pengyu Wang, Benfeng Xu, Shaohan Wang +5
Retrieval-augmented generation (RAG) spans lexical and dense retrieval, graph-based indexing, and agentic search, but these paradigms are usually evaluated on different benchmarks…
MPIE-Bench: Benchmarking Anatomically Plausible Multi-Person Interaction Editing
Jiajia Lin, Mingxuan Du, Tuowen Zhou +2
Text-to-image and personalized editing models now synthesize high-fidelity single-subject images with ease. Yet placing multiple named people into shared contact actions such as em…
Keep It InMind: Benchmarking the Implicit-Association Blind Spot in Agent Memory
Ruizhe Li, Mingxuan Du, Benfeng Xu +1
Long-term memory systems store what a user says in an external store and retrieve it when a related query arrives. This interface rests on an assumption so natural that it is rarel…
SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search
Yu Yan, Sheng Sun, Mingfeng Li +6
Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they hav…
A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
Mingxuan Du, Benfeng Xu, Chiwei Zhu +4
Frontier language models have demonstrated strong reasoning and long-horizon tool-use capabilities. However, existing RAG systems fail to leverage these capabilities. They still re…
WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora
Pengyu Wang, Benfeng Xu, Licheng Zhang +4
Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence acros…
Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles
Shaohan Wang, Benfeng Xu, Licheng Zhang +5
Deep Research Agents (DRAs) have demonstrated remarkable capabilities in autonomous information retrieval and report generation, showing great potential to assist humans in complex…
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents
Chiwei Zhu, Benfeng Xu, Mingxuan Du +4
Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits…
DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports
Ruizhe Li, Mingxuan Du, Benfeng Xu +3
Deep Research Agents (DRA) aim to help users search the web, synthesize information, and deliver comprehensive investigative reports. Prior benchmarks often either under-evaluate a…
An Index-based Approach for Efficient and Effective Web Content Extraction
Yihan Chen, Benfeng Xu, Xiaorui Wang +1
As web agents (e.g., Deep Research) routinely consume massive volumes of web pages to gather and analyze information, LLM context management -- under large token budgets and low si…
MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools
Zikang Guo, Benfeng Xu, Chiwei Zhu +3
The Model Context Protocol (MCP) is rapidly emerging as a pivotal open standard, designed to enhance agent-tool integration and interoperability, and is positioned to unlock a new…
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Mingxuan Du, Benfeng Xu, Chiwei Zhu +2
Deep Research Agents are a prominent category of LLM-based agents. By autonomously orchestrating multistep web exploration, targeted retrieval, and higher-order synthesis, they tra…
MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning
Zikang Guo, Benfeng Xu, Xiaorui Wang +1
Complex tasks involving tool integration pose significant challenges for Large Language Models (LLMs), leading to the emergence of multi-agent workflows as a promising solution. Re…
From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding
Chiwei Zhu, Benfeng Xu, Xiaorui Wang +1
The pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models (LLMs). While there are methods capable of generating…
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
Chiwei Zhu, Benfeng Xu, An Yang +4
Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold c…
Training LLM-Based Agents with Synthetic Self-Reflected Trajectories and Partial Masking
Yihan Chen, Benfeng Xu, Xiaorui Wang +2
Autonomous agents, which perceive environments and take actions to achieve goals, have become increasingly feasible with the advancements in large language models (LLMs). However,…
Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach
Ruizhe Li, Chiwei Zhu, Benfeng Xu +2
Creative writing is a key capability of Large Language Models (LLMs), with potential applications in literature, storytelling, and various creative domains. However, evaluating the…
Benchmarking Large Language Models on Controllable Generation under Diversified Instructions
Yihan Chen, Benfeng Xu, Quan Wang +2
While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constra…
On the Calibration of Large Language Models and Alignment
Chiwei Zhu, Benfeng Xu, Quan Wang +2
As large language models attract increasing attention and find widespread application, concurrent challenges of reliability also arise at the same time. Confidence calibration, an…
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
Benfeng Xu, An Yang, Junyang Lin +4
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to…
NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
Benfeng Xu, Quan Wang, Zhendong Mao +3
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs. In this paper…
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Wei Tang, Benfeng Xu, Yuyue Zhao +4
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representa…