1 citations · 1 across the 7 of their papers we have counts for
7 papers
MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents
Vernon Toh, Navonil Majumder, Zhengyuan Liu +2
AI agents in partially observable environments need to coordinate active sensing with working memory to maintain an evolving perceptual state. However, existing benchmarks struggle…
ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
Vernon Toh, Navonil Majumder, Zhengyuan Liu +2
To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone, even in the absence of docum…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan +1
We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaborat…
Programming over Thinking: Efficient and Robust Multi-Constraint Planning
Derrick Goh Xin Deik, Quanyu Long, Zhengyuan Liu +2
Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language mod…
The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation
Zhengyuan Liu, Stella Xin Yin, Bryan Chen Zhengyu Tan +5
User simulation is important for developing and evaluating human-centered AI, yet current student simulation in educational applications has significant limitations. Existing appro…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…