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Modern fuzzing techniques (e.g., Directed Fuzzing) heavily rely on static analysis information to guide their fuzzing campaigns. However, the success of these campaigns can be significantly limited when static analysis produces imprecise results, often due to its dependence on human-designed heuristics. The key insight is that fuzzing results provide ground truth about program behavior. This information could help refine static analysis in two ways: recovering information lost due to unsound analysis and eliminating spurious results from overly conservative analysis.

The evolution of technology follows a pattern of high expectations, setbacks, and improvements. The development of compilers and high-level languages exemplifies this pattern. When high-level languages emerged, many believed they would simplify programming and reduce the need for technical expertise. However, these technologies have created new opportunities for programming language researchers. The era of natural language programming has arrived, blurring the boundaries between programming languages and human communication. Does this transformation render programming language research obsolete?

Artificial General Intelligence (AGI) remains an inherently ambiguous and ill-defined concept. To date, there exists no official consensus on AGI, with researchers defining it according to their individual interpretations. However, a precise definition is essential to elevate AGI into rigorous scientific discourse. How, then, can we define AGI with clarity and in a formally precise way? In this article, I propose defining AGI as an artificial intelligence capable of solving all problems that humanity has solved thus far.