33 citations · 42 across the 26 of their papers we have counts for
27 papers
Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
Ferdous Al Rafi, Susrik Mukherjee, Latika Liladhar Dekate +6
Reinforcement learning achieves strong traffic signal control performance in simulation, yet policies trained in simulators often fail once deployed in the real world, a failure kn…
MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
Huaiyuan Yao, Xiaoou Liu, Charles Fleming +2
LLM-based multi-agent systems have shown strong performance on complex tasks, yet continual improvement from interaction experience remains challenging. Existing self-reflection me…
AndroidReality: How Far Are Mobile Agents from the Real World?
Xiaoou Liu, Longchao Da, Hanyang Chen +2
Mobile agents have achieved promising results on clean online benchmarks such as AndroidWorld, yet their performance often degrades sharply in real-world deployment due to environm…
DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning
Hanyang Chen, Anirudh Satheesh, Longchao Da +1
Transferring policies across domains poses a vital challenge in reinforcement learning, due to the dynamics mismatch between the source and target domains. In this paper, we consid…
Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
Xiaoou Liu, Tiejin Chen, Dengjia Zhang +3
Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing where a multi-step reasoning t…
ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society
Longchao Da, Mithun Shivakoti, Xiangrui Liu +3
Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine-grained shade patterns, especially those induced by urban…
Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
Tiejin Chen, Longchao Da, Xiaoou Liu +1
Uncertainty Quantification (UQ) is widely regarded as the primary safeguard for deploying Large Language Models (LLMs) in high-stakes domains. However, we argue that the field suff…
CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
Haolin Chen, Deon Metelski, Leon Qi +30
End-to-end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: policy density, decisions must be grounded in a large l…
LangMARL: Natural Language Multi-Agent Reinforcement Learning
Huaiyuan Yao, Longchao Da, Xiaoou Liu +3
Large language model (LLM) agents struggle to autonomously evolve coordination strategies in dynamic environments, largely because coarse global outcomes obscure the causal signals…
Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving
Longchao Da, David Isele, Hua Wei +1
Being able to anticipate the motion of surrounding agents is essential for the safe operation of autonomous driving systems in dynamic situations. While various methods have been p…
FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land Cover Identification Models
Xin Hong, Longchao Da, Hua Wei
Urban flooding in arid regions poses severe risks to infrastructure and communities. Accurate, fine-scale mapping of flood extents and recovery trajectories is therefore essential…
Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
Justin Turnau, Longchao Da, Khoa Vo +4
Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dy…
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs
Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou +2
Large Language Models are now key assistants in human decision-making processes. However, a common note always seems to follow: "LLMs can make mistakes. Be careful with important i…
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam +3
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender s…
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Xiaoou Liu, Tiejin Chen, Longchao Da +3
Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. H…
Generative AI in Transportation Planning: A Survey
Longchao Da, Tiejin Chen, Zhuoheng Li +14
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure desig…
Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses
Tiejin Chen, Xiaoou Liu, Longchao Da +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks due to large training datasets and powerful transformer architecture. However, the relia…
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels
Xiaoou Liu, Zhen Lin, Longchao Da +3
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequ…
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Longchao Da, Xiaoou Liu, Jiaxin Dai +3
Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into t…
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
Huaiyuan Yao, Longchao Da, Vishnu Nandam +4
The integration of autonomous vehicles into urban traffic has great potential to improve efficiency by reducing congestion and optimizing traffic flow systematically. In this paper…
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images
Longchao Da, Rui Wang, Xiaojian Xu +4
Medical imaging is crucial for diagnosing a patient's health condition, and accurate segmentation of these images is essential for isolating regions of interest to ensure precise d…
Shaded Route Planning Using Active Segmentation and Identification of Satellite Images
Longchao Da, Rohan Chhibba, Rushabh Jaiswal +2
Heatwaves pose significant health risks, particularly due to prolonged exposure to high summer temperatures. Vulnerable groups, especially pedestrians and cyclists on sun-exposed s…
LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation
Longchao Da, Tiejin Chen, Lu Cheng +1
The Large language models (LLMs) have showcased superior capabilities in sophisticated tasks across various domains, stemming from basic question-answer (QA), they are nowadays use…
Privacy-preserving Fine-tuning of Large Language Models through Flatness
Tiejin Chen, Longchao Da, Huixue Zhou +4
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT. Differential Privacy (DP) techniques…