1 citations · 1 across the 10 of their papers we have counts for
11 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…
Explicit Trait Inference for Multi-Agent Coordination
Suhaib Abdurahman, Etsuko Ishii, Katerina Margatina +3
LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose…
Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection
Michelle Yuan, Khushbu Pahwa, Shuaichen Chang +5
Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments. Most existing methods…
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…
CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions
Tamer Alkhouli, Katerina Margatina, James Gung +4
We introduce Conversational Function-Calling Evaluation Through Turn-Level Interactions (CONFETTI), a conversational benchmark1 designed to evaluate the function-calling capabiliti…
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…
MemInsight: Autonomous Memory Augmentation for LLM Agents
Rana Salama, Jason Cai, Michelle Yuan +4
Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-t…
A Study on Leveraging Search and Self-Feedback for Agent Reasoning
Karthikeyan K, Michelle Yuan, Elman Mansimov +6
Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the g…
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…
Inference time LLM alignment in single and multidomain preference spectrum
Sadat Shahriar, Zheng Qi, Nikolaos Pappas +5
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-con…
CERET: Cost-Effective Extrinsic Refinement for Text Generation
Jason Cai, Hang Su, Monica Sunkara +2
Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing…