A controlled experiment demonstrates that using a prefixed anchor prompt mitigates "silent failure"—where outputs remain semantically consistent but drift from physical reality—in long-chain agent iterations. The study compared 80 rounds of recipe generation with and without the anchor constraint on mainstream Transformer models.
- Control group outputs defied physical boundaries (e.g., 7°C oil, nanometer slices) after 80 iterations.
- Experimental group outputs remained within physical rule boundaries, converging toward an industrial-grade standardized solution.
- The control group's token magnitude increased by ~1x with invalid content, while the experimental group's expanded by ~7x with deployable engineering content.
- The anchor prompt is a pure external solution requiring no model architecture changes or additional inference pipelines.
This approach reduces post-hoc fact verification costs and enables reliable deployment of production-grade long-chain agents without measurable compute overhead.