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Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks

Moein E. Samadi, Andreas Schuppert

TL;DR

The paper argues that monolithic foundation models struggle to adapt quickly and reliably in high-dimensional, dynamically changing environments such as critical-care medicine. It proposes decentralized networks of small specialized agents (SANs) operating in a swarm-learning framework (SLSAN) to achieve rapid, data-efficient self-adaptation through knowledge integration and structured inter-agent communication. The SAHA-Net architecture exemplifies this approach in oxygenation forecasting, showing faster post-transition adaptation compared with a monolithic baseline and highlighting the benefits of diverse agent topologies despite a trade-off in reproducibility. Overall, the work suggests that swarm-based, knowledge-informed architectures can outperform foundation-models in dynamic real-world tasks while offering tunable complexity and data-efficiency.

Abstract

Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so far; a prominent case is diagnosing and treating dynamically evolving disease in intensive care. The common challenge is adapting complex systems to dynamic environments. Effective strategies must optimize outcomes in systems composed of strongly interacting functions while avoiding shared side effects; this requires reliable, self-adaptive modeling. These tasks align with building digital twins of highly complex systems whose mechanisms are not fully or quantitatively understood. It is therefore essential to develop methods for self-adapting AI models with minimal data and limited mechanistic knowledge. As this challenge extends beyond medicine, AI should demonstrate clear superiority in these settings before assuming broader decision-making roles. We identify the curse of dimensionality as a fundamental barrier to efficient self-adaptation and argue that monolithic foundation models face conceptual limits in overcoming it. As an alternative, we propose a decentralized architecture of interacting small agent networks (SANs). We focus on agents representing the specialized substructure of the system, where each agent covers only a subset of the full system functions. Drawing on mathematical results on the learning behavior of SANs and evidence from existing applications, we argue that swarm-learning in diverse swarms can enable self-adaptive SANs to deliver superior decision-making in dynamic environments compared with monolithic foundation models, though at the cost of reduced reproducibility in detail.

Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks

TL;DR

The paper argues that monolithic foundation models struggle to adapt quickly and reliably in high-dimensional, dynamically changing environments such as critical-care medicine. It proposes decentralized networks of small specialized agents (SANs) operating in a swarm-learning framework (SLSAN) to achieve rapid, data-efficient self-adaptation through knowledge integration and structured inter-agent communication. The SAHA-Net architecture exemplifies this approach in oxygenation forecasting, showing faster post-transition adaptation compared with a monolithic baseline and highlighting the benefits of diverse agent topologies despite a trade-off in reproducibility. Overall, the work suggests that swarm-based, knowledge-informed architectures can outperform foundation-models in dynamic real-world tasks while offering tunable complexity and data-efficiency.

Abstract

Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so far; a prominent case is diagnosing and treating dynamically evolving disease in intensive care. The common challenge is adapting complex systems to dynamic environments. Effective strategies must optimize outcomes in systems composed of strongly interacting functions while avoiding shared side effects; this requires reliable, self-adaptive modeling. These tasks align with building digital twins of highly complex systems whose mechanisms are not fully or quantitatively understood. It is therefore essential to develop methods for self-adapting AI models with minimal data and limited mechanistic knowledge. As this challenge extends beyond medicine, AI should demonstrate clear superiority in these settings before assuming broader decision-making roles. We identify the curse of dimensionality as a fundamental barrier to efficient self-adaptation and argue that monolithic foundation models face conceptual limits in overcoming it. As an alternative, we propose a decentralized architecture of interacting small agent networks (SANs). We focus on agents representing the specialized substructure of the system, where each agent covers only a subset of the full system functions. Drawing on mathematical results on the learning behavior of SANs and evidence from existing applications, we argue that swarm-learning in diverse swarms can enable self-adaptive SANs to deliver superior decision-making in dynamic environments compared with monolithic foundation models, though at the cost of reduced reproducibility in detail.

Paper Structure

This paper contains 26 sections, 26 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Background and problem setting
  3. Learning from data with foundation models
  4. Foundation models and adaption to dynamic environments
  5. Knowledge-integrated modeling for self-adaptation
  6. Swarm Learning Strategies for Self-Adaptation
  7. Self-adaptive hierarchical agent networks enable robust oxygenation forecasting
  8. Architecture of SAHA-Net
  9. Self-adaptation through swarm learning
  10. Conclusion
  11. Digital Twin of Oxygenation Dynamics: Synthetic Physiology and Observation
  12. Regime schedule.
  13. Alveolar ventilation and PaCO$_2$.
  14. Alveolar O$_2$ and A--a gradient.
  15. Shunt and arterial saturation.
  16. ...and 11 more sections

Figures (1)

  • Figure 1: One-step-ahead $\text{SpO}_2$ forecasts across a phase transition. Each panel uses a different adaptation horizon after the transition ($T_{\mathrm{adapt}}\in\{5,10,50,100,150,200\}$ minutes). SAHA-Net rapidly aligns with post-transition oxygenation dynamics, while the monolithic model adapts more slowly, requiring longer horizons.