Prompt Optimization, Generative Interfaces, and Differentiable Robotics

Welcome to today's edition of State of AI 👋

This edition covers a range of fascinating topics, from optimizing prompts for large language models to generating dynamic user interfaces and building differentiable physics simulators for robotics. These advancements showcase the growing sophistication and versatility of AI systems.

Here's what caught our attention:

• Automatic Prompt Optimization with Prompt Distillation: A novel non-gradient-based method that leverages prompt distillation, compression, and aggregation to automatically generate high-performing prompts for language models.

• Generative Interfaces for Language Models: A new paradigm that enables language models to dynamically generate interactive user interfaces, adapting to user goals and requirements beyond static text responses.

• Dojo: A Differentiable Physics Engine for Robotics: A physics engine that prioritizes stable simulation, accurate contact physics, and differentiability, enabling improved trajectory optimization, policy learning, and system identification for robotic systems.

• Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL: A self-supervised approach that disentangles agent and environment representations, leading to more sample-efficient and flexible reinforcement learning, especially in real-world robotic tasks.

• Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images: A novel prompt-driven framework that selectively reduces the processing of irrelevant tokens in vision transformers, improving the efficiency of medical image segmentation.

Let's get into it 👇

Bi-Weekly AI Research Roundup

Latest research summaries in ML, Robotics, CV, NLP and AI

Contents

1. GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration

2. Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot

3. Interpolating Speaker Identities in Embedding Space for Data Expansion

4. mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation

5. Prompt-based Dynamic Token Pruning for Efficient Segmentation of Medical Images

6. Autoregressive Universal Video Segmentation Model

7. UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials

8. APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration

9. Predicting the Order of Upcoming Tokens Improves Language Modeling

10. Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications

11. TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use

12. Automatic Prompt Optimization with Prompt Distillation

13. Generative Interfaces for Language Models

14. Dojo: A Differentiable Physics Engine for Robotics

15. Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL

GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration