Basically building a VLLM that aims at scalability (multi-agent) in distributed system

Abstract

Recent advancements in large language models (LLMs) have significantly enhanced their ability to perform human-like tasks, sparking increased interest in their application within socially intelligent agents. However, traditional simulation frameworks struggle with scalability and real-time interactions among diverse models. To address these challenges, we propose HONKAI, a novel framework that overcomes the limitations of existing systems involving multi-agent interaction. HONKAI features a decoupled, parallel architecture supporting simultaneous agent interactions with centralized environmental control, enhancing scalability, privacy, and integration of diverse models and human participants.

Problem Statement

Current frameworks for simulating multi-agent interactions using LLMs face several intrinsic challenges:

  1. Scalability: Existing methods typically structure interactions sequentially, limiting the number of agents that can be realistically simulated.
  2. Integration: Combining multiple models within a single simulated environment is complex due to the conflation of agents and environments, which hinders multi-model interactions.
  3. Real-Time Interaction: Real-time interactions and dynamic responses are limited by the existing sequential frameworks.

Proposed Solution

We introduce HONKAI, a Heterogenous Operational NetworK for multi-Agent Interaction, designed to address the limitations of existing frameworks. HONKAI features:

  1. Decoupled, Parallel Architecture: Supports simultaneous agent interactions with centralized environmental control, improving scalability and real-time processing.
  2. Integration of Diverse Models: Allows seamless integration of various models, enabling robust and comprehensive simulations that mirror real-world social systems.
  3. Enhanced Privacy: Ensures that only instructions and agent setup are shared, maintaining the privacy of the underlying models.
  4. Human Integration: Facilitates straightforward incorporation of human participants, enhancing experimental repeatability and utility for research.

Benefits

  1. Scalability: Supports larger simulations by separating agents and environments into different computational nodes, ensuring efficiency.
  2. Real-Time Updates: Improves data handling and enables immediate feedback, crucial for dynamic simulations.
  3. Democratization: Allows marginalized communities to conduct simulations by distributing workloads across multiple devices, fostering inclusive research.

Inclusive simulations, transforming how we understand and develop socially intelligent agents.