Refinement

README.md

@@ -26,7 +26,7 @@ Further the Unified Turing also accomplishes what has been considered to be an i
 
 As this pattern of halting is designed to be an analog to the inner workings of some graph network that eventually has some output. This is noting that previous to 2023, one of the major problem behind LLMs is whether they would have an output due to some input. That we may compare the runtime of an ActionStrategy to a Neural Network, would represents a series of weighted sums that fails to halt and bears no output. In addition this likewise demonstrates a method of proving safe functionality of any new Ai systems in their ability to halt. As if we task some Ai to create paperclips, how would we analyze their strategies to demonstrate that they would not paperclip the entire universe? That their strategies should be proven to be able to halt once some condition is met.
 
-Likewise the unfortunate truth of a Unified Turing Machine due to its recursive functionality. Is that it requires the ability to halt to function as a hard requirement. Otherwise the developer will run into unexpected behavior in their applications. This would be due to strategies and/or the supporting framework are halting incomplete and experiencing action overflow. As our general good enough computers and their branch prediction will generate ghost actions and other unexpected behaviors during this condition. Such as the thrashing the applications memory, and the inability to receive some output akin to operable Neural Networks. So by strange effect the solution to solve the halting problem, was a method of programming that went beyond data entry of classic. Utilizing logic over mathematics to create the scope of this framework, to afford for the dynamic functionality of data transformation versus data entry.
+Likewise the unfortunate truth of a Unified Turing Machine due to its recursive functionality. Is that it requires the ability to halt to function as a hard requirement. Otherwise the developer will run into unexpected behavior in their applications. This would be due to strategies and/or the supporting framework are halting incomplete and experiencing action overflow. As our general good enough computers and their branch prediction will generate ghost actions and other unexpected behaviors during this condition. Such as the thrashing the applications memory, and the inability to receive some output akin to an unresponsive Neural Network. So by strange effect the solution to solve the halting problem, was a method of programming that went beyond data entry of classic. Utilizing logic over mathematics to create the scope of this framework, to afford for the dynamic functionality of data transformation versus data entry.
 
 Or simply, due to the recursive functionality of STRX, that requires the ability to halt by design. Is accomplished via ActionStrategies, that perform higher order logic within the finite state machine that is the axium. That describes the exact steps to accomplish something. As every ActionStrategy has a conclusion, and can represent any calculation. This is the advent of "Logical Determinism," is the logical ability to disclude calculations and their associated symbols/qualities that do not halt. As ActionStrategies represent a finite symbol selection of symbols that in branching sequence can be tested to be halting complete. And are chosen not by symbol selection, but determined via the positional load of said symbols within this system.
 ### The Testable Proof