Continual Harness: Online Adaptation for Self-Improving Foundation Agents [R]

Continual Harness proposes an approach to online adaptation for foundation agents that moves beyond traditional gradient-based retraining by introducing a dual-agent architecture (Teacher/Student) with persistent learning memory that enables systems to dynamically adjust operational strategies at inference time. The method achieves gradient-free continual learning by shifting adaptation from model parameters to system-level orchestration. When evaluated on cybersecurity tasks, the system achieves 54.1% success with smaller models while reducing computational cost by 86% compared to larger baselines.

Source: r/MachineLearning | 2026-05-14