1 citations · 1 across the 11 of their papers we have counts for
11 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…
MAVEN: A Macro-Societal Value Evaluation Framework of Multimodal Content with Compact Aligned Evaluators
Zijuan Zhao, Zheren Fu, Hou Xia +3
Assessing whether multimodal content aligns with macro-societal values, such as peace, justice, and freedom, has become an increasingly urgent challenge. Existing frameworks are la…
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…
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
Jingxuan Han, Wei Liu, Mingyang Zhu +8
Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information…
Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement
Xin Wang, Liangtai Sun, Yaoming Zhu +8
Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec a…
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…
FineRef: Fine-Grained Error Reflection and Correction for Long-Form Generation with Citations
Yixing Peng, Licheng Zhang, Shancheng Fang +3
Generating with citations is crucial for trustworthy Large Language Models (LLMs), yet even advanced LLMs often produce mismatched or irrelevant citations. Existing methods over-op…
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
Shaohan Wang, Licheng Zhang, Zheren Fu +2
Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of large language models (LLMs). Existing methods typically optimize the retriever or the ge…
Feature-Adaptive and Data-Scalable In-Context Learning
Jiahao Li, Quan Wang, Licheng Zhang +2
In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context…
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Jiaang Li, Quan Wang, Yi Liu +2
Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and fac…