Beyond the Middle: Large Language Models, Framework Opacity, and Philosophers' Responsibility for Audit

As large language models rapidly improve at argument generation, literature review, formal reasoning, counterexample search, and framework switching, an increasingly difficult question to avoid is whether AI will ultimately replace philosophers if it becomes faster, broader, and more consistent than any single researcher across a wide range of philosophical tasks. This report begins from a premise deliberately favorable to the replacement thesis: even if advanced large language models can make extensive use of existing philosophical and logical resources and can match or surpass most individual researchers in well-defined tasks, the fact that AI can participate in philosophical work does not by itself imply that it can take over philosophical judgment. What is missing between these two claims is a layer of epistemic authorization and responsibility. The report proposes a three-part model of the reasoning chain. The front end concerns problem formulation, framework selection, and the organization of premises; the middle concerns reasoning, retrieval, comparison, and objection under given constraints; and the back end concerns judgments about the importance of conclusions, the sufficiency of evidence, whether a framework should be retained or revised, and who bears responsibility for the claims ultimately accepted. These three parts are not a linear pipeline but a recursive functional division of labor: back-end evaluation often forces the researcher to return to the front end and reformulate the question. Unlike an earlier version of this argument, the present report no longer claims that AI can exist only in the ``middle.'' Contemporary AI can already participate in all three parts, including proposing questions, suggesting frameworks, generating alternative explanations, and recommending positions. The real distinction lies in different degrees of delegability: execution can be extensively outsourced, whereas final framework authorization, normative commitment, and epistemic responsibility cannot be completed automatically merely because a model has produced an output. On this basis, the report defines a \emph{framework} as a set of conceptual categories, background assumptions, evidential standards, norms of inference, and evaluative priorities that jointly determine what counts as a problem, what counts as a reason, and what kinds of answer may be accepted. The risks of AI output therefore should not be reduced to whether a model ``hallucinates.'' More structural problems include framework opacity created when sources and evidential structures are flattened in synthesis; the amplification of false or contested premises through fluent reasoning; and the possibility that a model's explanation of its own reasoning basis or framework provenance is not faithful to the factors that actually produced the output. Accordingly, the purpose of audit is not to prove that machines ``lack a concept of truth,'' but to require that any output affecting knowledge acceptance receive justification independent of fluency, confident tone, or the model's own self-report. The report therefore revises the earlier idea of ``framework attribution'' into \emph{framework reconstruction and audit}. Rather than assuming that a model output can be traced precisely to a single training source or intellectual origin, the method reconstructs the framework on which an answer functionally depends by examining conceptual vocabulary, argumentative structure, evidential standards, treatment of counterexamples, evaluative priorities, and verifiable sources. It then asks three questions: whose framework is this in functional terms, from what epistemic time point does it arise, and on what grounds does it acquire default authority in the present problem? The report further proposes a four-step workflow---audit the input, exploit AI's strengths, audit the output, and retain final authorization and responsibility---together with an evidence hierarchy clarifying that directly asking an AI which framework it used can generate hypotheses for audit but cannot constitute final evidence. The conclusion is not that humans are necessarily more accurate than AI. Human auditors also fail. Responsible use therefore requires source verification, cross-checking, adversarial testing, and, where appropriate, review by a relevant community. What must remain with human agents and institutions is not an exclusive right to generate frameworks, but the locus of final framework authorization and accountability. The report's meta-rule is therefore reformulated as follows: \textbf{never outsource the final authorization of a framework}.