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Autor principal: Head, Hank
Format: Recurso digital
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Publicat: Zenodo 2026
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Accés en línia:https://doi.org/10.5281/zenodo.19788392
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  • This paper applies Hypothesis-Driven Development (HDD) -- a lean approach where every architectural idea is expressed as falsifiable hypotheses with measurable targets before implementation -- to evaluate predictive processing as a cognitive enhancement for neurosymbolic AI agents. Five hypotheses were formulated with quantitative targets and tested against a live being with 66,163 knowledge triples. None of the five hypotheses met their targets. The HDD methodology delivered its core value: fast identification of the root cause within weeks rather than months, enabling rapid redirect of engineering effort.