Frozen Model Weights, Evolving Agents: ModularRSI Enables Harness Self-Improvement

ModularRSI freezes base model weights and evolves the agent's harness — system mechanisms like loop control, observation handling, tool use, context management, and task completion detection — through execution experience, achieving a 4.86-point gain on Terminal-Bench 2.0 that transfers across tasks, domains, and different base models.

Agent ArchitectureAgent Self-ImprovementFrozen Model Weights

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Frozen Model Weights, Evolving Agents: ModularRSI Enables Harness Self-Improvement

Machine Heart

Sep 24, 2026 · Artificial Intelligence

ModularRSI: Agents Self-Improve by Evolving Harness, Not Model Weights

ModularRSI demonstrates that AI agents can continuously self-improve by evolving their harness—the surrounding system mechanisms like tool use, context management, and task completion detection—while keeping the base model frozen, achieving performance gains on Terminal-Bench and SWE-Bench that transfer across domains and models.

Agent Self-ImprovementCross-Domain TransferCross-Model Transfer

0 likes · 18 min read

ModularRSI: Agents Self-Improve by Evolving Harness, Not Model Weights