20 citations · 51 across the 14 of their papers we have counts for
16 papers
Ideation Arena: Evaluating LLM Generated Research Ideas with Battle-style Human Expert Assessment
Zhiyu Chen, Keyu Zhao, Jigao Fu +8
Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies w…
DF-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation
Jiaoyang Li, Junhao Ruan, Shengwei Tang +4
Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge. Retrieval-augmented generation (RAG) addresses this limitation by…
Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses
Xiangjie Luo, Yulun Zhang, Miyuki Koshimura +2
We study the problem of optimizing physical layouts for automated warehouses, where hundreds to thousands of robots are coordinated to transport packages. Previous works have shown…
OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
Chenyang Shao, Dehao Huang, Yu Li +18
With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimen…
SUBQRAG: Sub-Question Driven Dynamic Graph RAG
Jiaoyang Li, Junhao Ruan, Shengwei Tang +5
Graph Retrieval-Augmented Generation (Graph RAG) effectively builds a knowledge graph (KG) to connect disparate facts across a large document corpus. However, this broad-view appro…
Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems
Yulun Zhang, Alexandre O. G. Barbosa, Federico Pecora +1
We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transp…
MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation
Yutong Wang, Pengliang Ji, Chaoqun Yang +4
The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as programming. Inspired by recent advances…
Speedup Techniques for Switchable Temporal Plan Graph Optimization
He Jiang, Muhan Lin, Jiaoyang Li
Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays,…
Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Hongzhi Zang, Yulun Zhang, He Jiang +4
We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agen…
QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps
Cheng Qian, Yulun Zhang, Varun Bhatt +3
We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to automatically evaluate Multi-Agent Path Finding (MAPF) algorithms by generating diverse maps. Pre…
Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities
Jiaqi Tan, Yudong Luo, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighborhood search (LNS) have gained…
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
He Jiang, Yulun Zhang, Rishi Veerapaneni +1
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new g…
A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution
Ying Feng, Adittyo Paul, Zhe Chen +1
One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution. One option is to r…
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Yulun Zhang, He Jiang, Varun Bhatt +2
We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as h…
Scalable Mechanism Design for Multi-Agent Path Finding
Paul Friedrich, Yulun Zhang, Michael Curry +5
Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This p…
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
Yulun Zhang, Matthew C. Fontaine, Varun Bhatt +2
We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effe…