'RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning' arXiv paper proposes structured control-plane architecture for long-horizon agent reasoning
The mid-June 2026 arXiv paper 'RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning' proposes a structured control-plane architecture separating reasoning, planning, and execution layers in long-horizon agent workflows. The contribution sits in the emerging agent-architecture-vs-model-scale research direction that questions whether the agent loop or the model is the higher-leverage capability investment.
The substantive piece is the structured-control-plane architecture proposal. Through 2025 most agent architectures used unified loops — single LLM call with tool access, retry-on-failure, no explicit separation between reasoning and execution. RACL proposes splitting these into distinct control layers with separate optimization targets. The architecture parallels traditional control-systems engineering and may transfer those engineering disciplines into agent system design.
The competitive read against the 'End of Software Engineering' paper's scaffolding-over-scale claim is that both papers point to the same direction: agent-architecture investment increasingly outweighs model-scale investment as the higher-leverage capability lever. RACL is the architectural-formalization track; the End-of-SWE paper is the empirical-results track.
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