1 citations · 1 across the 7 of their papers we have counts for
6 papers · 1 filter
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
Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover…
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…
FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
Ling Yue, Chaoqian Ouyang, Hang Xu +7
Large language model (LLM)-based reviewing systems typically assess manuscripts in isolation, leaving literature- and code-dependent claims difficult to verify. We present FactRevi…
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.…
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,…