5 citations · 8 across the 15 of their papers we have counts for
18 papers
ATTUNER: Recomputation-Free Agentic Context KV Reuse
Xinghao Chen, Junnan Dong, Cai Ke +8
Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every requ…
Latent Multimodal Memory Reasoning
Cai Ke, Xinghao Chen, Yuanchen Bei +7
Long-term personalized agents are required to reason over distributed history across modalities and evolving dialogue states. While existing flat cross-modal methods compress visua…
LADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning
Senlei Zhang, Linhao Luo, Qian-Wen Zhang +4
Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topologies. However, existing fra…
WFM: Wiki Foundation Model for Complex Agentic Reasoning
Junnan Dong, Linhao Luo, Senlei Zhang +9
Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs have show…
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Zheng Yuan, Chuang Zhou, Linhao Luo +4
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably intro…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
Deep Tabular Research via Continual Experience-Driven Execution
Junnan Dong, Chuang Zhou, Zheng Yuan +7
Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-can…
Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
Junru Lu, Jiarui Qin, Lingfeng Qiao +35
We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that r…
Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Junnan Dong, Siyu An, Yifei Yu +6
Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured gr…
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Junru Lu, Jiazheng Li, Guodong Shen +5
Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-p…
CJEval: A Benchmark for Assessing Large Language Models Using Chinese Junior High School Exam Data
Qian-Wen Zhang, Haochen Wang, Fang Li +5
Online education platforms have significantly transformed the dissemination of educational resources by providing a dynamic and digital infrastructure. With the further enhancement…
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
Wenhao Liu, Siyu An, Junru Lu +8
Role-Playing Agents (RPAs) have shown remarkable performance in various applications, yet they often struggle to recognize and appropriately respond to hard queries that conflict w…
FinVerse: An Autonomous Agent System for Versatile Financial Analysis
Siyu An, Qin Li, Junru Lu +2
With the significant advancements in cognitive intelligence driven by LLMs, autonomous agent systems have attracted extensive attention. Despite this growing interest, the developm…
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
Junru Lu, Jiazheng Li, Siyu An +4
Direct Preference Optimization (DPO) has emerged as a prominent algorithm for the direct and robust alignment of Large Language Models (LLMs) with human preferences, offering a mor…
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
Junru Lu, Siyu An, Min Zhang +3
When the quality of naive prompts is carefully optimized by human experts, the task performance of large language models (LLMs) can be significantly improved. However, expert-based…
VKIE: The Application of Key Information Extraction on Video Text
Siyu An, Ye Liu, Haoyuan Peng +1
Extracting structured information from videos is critical for numerous downstream applications in the industry. In this paper, we define a significant task of extracting hierarchic…
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
Junru Lu, Siyu An, Mingbao Lin +5
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range…
Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document
Chen Lin, Ye Liu, Siyu An +1
In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous…