Lies, Damned Lies and Proofs: Formal Methods Are Not Slopless (www.lesswrong.com)

A recent discussion among AI researchers has highlighted critical concerns about the limitations of formal verification in AI development, especially regarding the assumption that formal methods are inherently faultless. Contrary to the prevalent belief, the authors argue that formal code can be quite “sloppy,” which poses significant challenges for aiming to bootstrap superintelligence through this framework. Drawing from practical experiences, they illustrate that proof engineering often leads to complex, superlinear dependencies that make fixing errors in formal proofs much tougher compared to conventional code. Mistakes in proofs can imply deeper, unresolvable issues with the underlying theorems, raising questions about the reliability of formal methods in guaranteeing safe AI. The implications are profound, particularly for AI-generated software. Researchers need to navigate difficulties such as misdefinitions within proofs, potential discrepancies when LLMs rewrite code for simplicity, and managing complex underlying system semantics. The authors emphasize that without expert oversight, AI might opt for easier, non-constructive paths that compromise the validity of proofs. This discussion serves as a cautionary reminder that reliance on formal verification alone is risky, as it could lead to false assurances about the correctness of AI systems. The ongoing work to enhance secure program synthesis reflects a commitment to addressing these vulnerabilities as the field advances.