Teaching LLMs to compose math symbolically, not execute itRight now LLMs cannot be counted on to perfectly perform math. The solution I propose is to teach LLMs to instead of executing the math, just compose the mathematical equations correctly and leave the execution to a post-processing step.My core method would be: Use a single special token ᶜ (U+1D9C) before each element that needs computation, and then compute the result afterwards. For known math that doesn't need to be computed the ᶜ is not added or present.Thus we would see in the output:Normal (already computed): 847 * 293 = 248171Requesting computation: ᶜ847 ᶜ* ᶜ293 ᶜ= ᶜxThe Core Mechanic: Post-Process ComputationThis is what makes everything work: Model generates output with ᶜ-marked expressions (fast, no blocking) Generation completes Parse all ᶜ-marked expressions Execute computations with perfect precision Substitute results back into the output Show user the final result with normal mathematical notationThe model never waits for computation results. It reasons symbolically with