6 papers
Declarative by Design, Assistable Only by Convention: Benchmarking Multi-Agent Frameworks for AI-Assistability
Shafiuddin Rehan Ahmed, Sourabh Deshpande
Multi-agent frameworks (MAFs) promise to simplify LLM-driven software development, yet no principled metric captures how well AI coding assistants can generate correct, framework-s…
MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms
Isabelle Diana May-Xin Ng, Tharindu Cyril Weerasooriya, Haitao Zhu +1
In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampl…
ProRefine: Inference-Time Prompt Refinement with Textual Feedback
Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Parag Shah +3
Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications…
Learning a Canonical Basis of Human Preferences from Binary Ratings
Kailas Vodrahalli, Wei Wei, James Zou
Recent advances in generative AI have been driven by alignment techniques such as reinforcement learning from human feedback (RLHF). RLHF and related techniques typically involve c…
VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Zhongchun Zheng, Kan Wu, Long Cheng +7
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patt…
Incorporating Causal Analysis into Diversified and Logical Response Generation
Jiayi Liu, Wei Wei, Zhixuan Chu +4
Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance wit…