1 citations · 2 across the 8 of their papers we have counts for
10 papers
TokenCast: Forecasting Token Consumption During LLM Agent Execution
Chaoqian Ouyang, Ling Yue, Libin Zheng +8
When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next steps based on tool…
FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills
Zeyu Ren, Ling Yue, Ran Li +5
Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution.…
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
Patrick Emami, Sameera Horawalavithana, Truc Nguyen +11
Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists"…
EvoFlux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Kushal Raj Bhandari, Ling Yue, Ching-Yun Ko +4
Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to unavailable tools, missing argum…
A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems
Titu Ranjan Sarker, Muhammed Jawaad Zulqernine, Ling Yue +3
Finite element analysis (FEA) is the most important numerical approach for solid mechanics. Challenges of FEA include a steep learning curve for entry-level users and potential fal…
DiagramBank: A Quality-Audited Dataset of Scientific Schematic Diagrams with Multi-Level Document Context
Ling Yue, Tingwen Zhang, Jiaying Wang +2
Scientific papers use schematic diagrams to communicate methods, workflows, and system structure, yet existing scientific-figure corpora often mix them with plots, screenshots, and…
FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
Ling Yue, Chaoqian Ouyang, Hang Xu +8
AI is accelerating scientific research, but providing timely and trustworthy feedback remains challenging. Such feedback requires evaluating specific claims against verified eviden…
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko +6
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval,…
ToolRosella: Translating Code Repositories into Standardized Tools for Scientific Agents
Shimin Di, Xujie Yuan, Hanghui Guo +10
Large Language Model (LLM)-based agent systems are increasingly used for scientific tasks, yet their practical capability remains constrained by the narrow scope of manually curate…
Code2MCP: Transforming Code Repositories into MCP Services
Chaoqian Ouyang, Ling Yue, Shimin Di +5
The Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools. However, most current research focuses on selecting tools from an existing pool.…