5 papers
Supplement Generation Training for Enhancing Agentic Task Performance
Young Min Cho, Daniele Bonadiman, Divya Bhargavi +8
Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are…
SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection
Yubin Ge, Salvatore Romeo, Jason Cai +2
Despite the rapid advancements in LLM agents, they still face the challenge of generating meaningful reflections due to inadequate error analysis and a reliance on rare successful…
TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues
Yubin Ge, Salvatore Romeo, Jason Cai +4
Temporal reasoning in multi-session dialogues presents a significant challenge which has been under-studied in previous temporal reasoning benchmarks. To bridge this gap, we propos…
Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development
Ming Shen, Raphael Shu, Anurag Pratik +4
We have seen remarkable progress in large language models (LLMs) empowered multi-agent systems solving complex tasks necessitating cooperation among experts with diverse skills. Ho…
LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language
Yubin Ge, Neeraja Kirtane, Hao Peng +1
As large language models (LLMs) have been deployed in various real-world settings, concerns about the harm they may propagate have grown. Various jailbreaking techniques have been…