MSIL: Multi Stage Inference Layering

Multi Stage Inference Layering (MSIL) is a reasoning architecture designed to stabilize and deepen multi step inference inside autonomous research agents. MSIL introduces a structured hierarchy of inference stages: generation, expansion, challenge, repair, stabilization, and synthesis, with explicit roles and error checking behavior at each step. Integrated with Reparodynamics, the Targeted Gradient Repair Mechanism (TGRM), and the Repair Yield per Energy (RYE) metric, MSIL produces higher quality hypotheses, lower noise, improved contradiction handling, and greater long horizon reliability in multi agent scientific swarms. This paper defines MSIL formally, describes its integration into Reparodynamic research agents, and explains how MSIL supports eight hour to multi day continuous runs with stability properties that are not available in standard prompt or chain of thought systems. MSIL is presented as an original contribution to autonomous research methodology and as a core component of the Reparodynamics framework.