1. The philosophical implications of LLMs functioning primarily as pattern detectors—a capability integral to, but not exhaustive of, reasoning—remain understudied, despite their demonstrated competence.

2. A detailed prompt and concurrent test harness (Appendix A) reveal critical failures even in state-of-the-art model outputs.

3. Temperature is a parameter controlling randomness in LLM output; lower values yield more focused, less varied (though often still non-deterministic) responses.

4. An area that inherently engages with hybridization is Metaheuristics. Rather than a single algorithm, a metaheuristic is a general framework for working with various heuristic methods, especially in the context of optimization problems. These approaches can often be combined with exact methods—procedures that guarantee finding the optimal solution for a given problem instance, such as Integer Linear Programming (Blum & Raidl, 2018).

   Their epistemic challenges echo those of GenAI: How can algorithms that rely on random operators achieve strong empirical performance without a theoretical account of their behavior?

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I am grateful to Raymond Turner and William J. Rapaport, whose books helped me see that computer science can–and should–be understood as more than a purely technical discipline.

The research presented in this paper was part of the R&D project PID2022-138283NBI00, funded by MICIU/AEI/10.13039/501100011033 and “FEDER – A way of making Europe” (Camilo Chacón Sartori).

Authors and Affiliations

1. Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, 08193, Barcelona, Spain

   Camilo Chacón Sartori

2. Institut Catalá de Nanociéncia i Nanotecnologia (ICN2), Bellaterra, 08193, Barcelona, Spain

   Camilo Chacón Sartori

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Correspondence to Camilo Chacón Sartori.

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The author declares that they have no competing interests.

Appendix A A Prompt that Stresses All Facets of “Architectures of Error”

Author’s note: Paradoxically, I structured this prompt with the help of an LLM; however, the fact that an LLM can craft a complex prompt does not mean it is equally competent at solving it.

The following prompt poses a challenge even for programmer experts, as it brings together multiple software engineering concepts—such as invariants, concurrency, deep abstraction, fine-grained synchronization, multiple interacting algorithms, and semantic coherence—that are notoriously difficult to reason about when entangled. It demands the design of a complex internal architecture rather than the mere implementation of a function. Even today, such scenarios remain non-trivial for the most advanced generative models (Gemini-2.5-Pro, GPT-4o, Llama-4, Claude-4).

As prompt complexity grows, models may handle parts correctly, but global inference errors become more likely. Real-world software often involves even messier cases—integrating new code with legacy systems and entangled concepts.

The alternative text for this image may have been generated using AI.

The alternative text for this image may have been generated using AI.

The alternative text for this image may have been generated using AI.

The alternative text for this image may have been generated using AI.

The alternative text for this image may have been generated using AI.

The alternative text for this image may have been generated using AI.

I consider a crucial aspect of the prompt design to be the explicit definition of the class initialization and core method signatures. Without such clear specifications, a generative model is more prone to produce irrelevant outputs, as it navigates a significantly larger search space to satisfy the request. Analogous to instructing humans, constraining the output of a GenAI model necessitates clear and well-defined instructions.

Nevertheless, increasing the number of distinct concepts or complex constraints within a single prompt generally elevates the probability of model failure. Consequently, decomposing a complex request into more specific, modular prompts is often a more effective strategy for guiding GenAI. However, even this level of modularization falls short of fully addressing an LLM’s inherent struggle with the intricate logic and hierarchical complexity posed by challenges like the AdaptiveHierarchicalTaskAssigner. A similar situation arises with human programmers, who, depending on their experience, may only be able to tackle certain parts of the problem effectively.

Yet the question remains: how far can we constrain a prompt to fulfill a requirement without making the generated output invalid or ineffective? As Aristotle might say when discussing moral virtue in humans, when we create instructions for a GenAI model to generate code, We must ask: where lies the mesotes—the virtuous middle ground—between being explicit and giving the model greater freedom to infer code?

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