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Ruochen Li

6 accepted papers

2026

Cycle Clustering: An Algorithm for Multi-Depot Multi-Agent Collaborative Coverage in Structured Road Network

RA-L 2026

Multi-depot multi-agent collaborative coverage is a representative problem in swarm intelligence, with broad applications in real-world scenarios. In this problem, multiple agents are initially located at different depots, which differs from the traditional problem setting, and are required to colla

Cited by 0SourceScholar
2026

ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction

AAAI 2026technical

Pedestrian trajectory prediction is critical for ensuring safety in autonomous driving, surveillance systems, and urban planning applications. While early approaches primarily focus on one-hop pairwise relationships, recent studies attempt to capture high-order interactions by stacking multiple Grap

Cited by 0SourcePDFScholar
2025

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots

IROS 2025

Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strat

Cited by 1SourceScholar
2025

LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research

EMNLP 2025

Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reproducing code from research papers, especially in the NLP domain, remains underexplored. This task includes unique complex r

2025

ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment

EMNLP 2025

Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust. This gap reveals fundamental flaws in how current metrics assess the quality of generated reports. We rethink the design and evaluation of these metrics and propos

2024

IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering

NeurIPS 2024poster

To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on directly assessing the immediate responses generated by the models based on the given question and context. In the common use case of humans seeking AI assistant’s help in finding information…

Cited by 5SourcePDFScholar