From the 1 of 7 linked papers with an AI index.
7 papers
From Prompt to Harness: Coderlet from Scratch
Mengfan Li
A model alone does not determine how a programming agent acts. What the model sees, how actions enter the environment, how feedback returns, and how one run affects the next all de…
Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution
Haotian Lin, Silin Chen, Xiaodong Gu +8
The paper introduces ACQUIRE, a QA-driven framework that lets a language model ask targeted questions about a code repository to acquire explicit knowledge before generating patche…
CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models
Mengfan Li, Xuanhua Shi, Yang Deng
Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demonstrate promising performance o…
EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li +4
Code efficiency is a fundamental aspect of software quality, yet how to harness large language models (LLMs) to optimize programs remains challenging. Prior approaches have sought…
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions
Venkatesh Sivaraman, Eric P. Mason, Mengfan Ellen Li +4
Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-m…
RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems
Mengfan Li, Xuanhua Shi, Yang Deng
Large Language models are revolutionizing the conversational recommender systems through their impressive capabilities in instruction comprehension, reasoning, and human interactio…