2 citations · 4 across the 10 of their papers we have counts for
11 papers
InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization
Hanya Sun, Chen Zhang, Sheng Liang +2
Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user…
ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents
Peng Xu, Zuyu Zhang, Yuze Sun +3
Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when to compact history under a…
DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
Jiahui tang, Kuicai Dong, Dexun Li +7
Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurat…
AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs
Sheng Liang, Yongyue Zhang, Nathanael Brian +4
Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control latency, deployments routinely comp…
Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation
Fangda Ye, Zhifei Xie, Yuxin Hu +5
Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centr…
Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Jiahao Wu, Ning Lu, Shengcai Liu +6
Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks. While scaling rollouts can stabilize training and enhance perfor…
SRR-Judge: Step-Level Rating and Refinement for Enhancing Search-Integrated Reasoning in Search Agents
Chen Zhang, Kuicai Dong, Dexun Li +4
Recent deep search agents built on large reasoning models (LRMs) excel at complex question answering by iteratively planning, acting, and gathering evidence, a capability known as…
Doc-Researcher: A Unified System for Multimodal Document Parsing and Deep Research
Kuicai Dong, Shurui Huang, Fangda Ye +9
Deep Research systems have revolutionized how LLMs solve complex questions through iterative reasoning and evidence gathering. However, current systems remain fundamentally constra…
Reinforcement Learning Foundations for Deep Research Systems: A Survey
Wenjun Li, Zhi Chen, Jingru Lin +8
Deep research systems, agentic AI that solve complex, multi-step tasks by coordinating reasoning, search across the open web and user files, and tool use, are moving toward hierarc…
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
Yaxiong Wu, Sheng Liang, Chen Zhang +5
Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation…
A Survey on Multi-Turn Interaction Capabilities of Large Language Models
Chen Zhang, Xinyi Dai, Yaxiong Wu +4
Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and context…