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20242026
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cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…