MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs

LLMs have shown the capacity to improve their performance on reasoning tasks through reflecting on their mistakes, and acting with these reflections in mind. However, continual reflections of the same LLM onto itself exhibit degeneration of thought, where the LLM continues to repeat the same errors again and again even with the knowledge that its wrong. To address this problem, we instead introduce multi-agent with multi-persona debators as the method to generate reflections. Through out extensive experimentation, we've found that the leads to better diversity of in the reflections generated by the llm agent. We demonstrate an accuracy of 47% EM HotPot QA (question answering) and 82.7% on HumanEval (programming), both performances surpassing reflection with a single llm.

Image-based reconstruction for strong-nonlinear transient problems by using an enhanced ReConNN

With the improvement of pattern recognition and feature extraction of Deep Neural Networks (DNNs), more and more problems are attempted to solve from the view of images. Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model, which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision. However, due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network (CNN) and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower. In this study, an improved ReConNN model is proposed to address the mentioned weaknesses. Through experiments, comparisons and analyses, the improved one is demonstrated to outperform in accuracy, efficiency and cost.