8 papers
Reinforcement Learning with Comparative Evidence for Social Intelligence
Keane Ong, Yuriel Ryan, Sabri Boughorbel +9
Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive…
When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
AI Collaboration, Jiaying Wu, Caleb Ziems +19
We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public…
How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactions
Fernanda Mansilla, Aloysius Tok, Bahia Guellaï +2
As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical. Humans possess moral agency, the capacity to make ethically guided dec…
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…
Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment
Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi +4
Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging ta…
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency
Vernon Toh Yan Han, Ratish Puduppully, Nancy F. Chen
Large Language Models (LLMs), combined with program-based solving techniques, are increasingly demonstrating proficiency in mathematical reasoning. For example, closed-source model…