Reframing symbolic reasoning problems as string matching and search—rather than arithmetic simulation—helps LLMs avoid hallucinations and handle combinatorially complex tasks more reliably.
This paper tackles bit manipulation puzzles by teaching LLMs to deduce hidden logical rules transforming binary strings. Instead of forcing models to simulate complex boolean logic (which causes hallucinations), the authors reframe the problem as string similarity matching and structured search with backtracking.