Logic Packets and the Limits of Derivation: A Structural Critique of LLM Optimism

Large language models exhibit remarkable capabilities alongside persistent, characteristic failures. Optimists interpret this pattern as evidence that scaling, architectural refinement, or improved training will eventually yield systems capable of genuine cognition. This paper argues that such optimism is structurally unfounded. The limitations of current LLM architectures are not contingent engineering problems but necessary consequences of representational misalignment between token-level primitives and what I term logic packets: the unified, norm-governed contents that serve as the minimal units of reasoning. Tokens, as compression-optimized discretizations of text, lack the semantic unity, intrinsic normativity, and inferential potency that logic packets require. Processing over such primitives cannot generate what the primitives themselves lack. Crucially, this argument does not presuppose anti-functionalism. Even granting that mental states are functional roles, LLMs fail to instantiate the right roles because their representational building blocks lack the structural features those roles require. The paper develops this argument through engagement with recent empirical findings on hallucination, reasoning failures, and creativity ceilings, and responds to the objection that pragmatic norms suffice to ground LLM semantics. The methodological contribution is logic packet analysis as a structural diagnostic framework: a set of criteria for assessing whether any proposed representational primitive can serve as a vehicle of cognitive content.