33 citations · 42 across the 26 of their papers we have counts for
9 papers · 1 filter
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…