arXiv cs.CL· Yiling Wu (BridgeM, Inc.)·· 12 小时前AI 评分21
LLM 表征生成中的预设问题:一篇哲学诊断
The Presupposition Problem in Representation Genesis
AI 导读
论文指出 LLM 是首个未清晰经历"表征生成"却具备高认知表现的系统,由此提出表征生成问题。作者认为思维语言假说、目的语义学、预测加工、生成现象学等框架在解释该问题时都预设系统已是表征者,形成"表征预设结构"并导致解释无限后退。论文只做概念诊断,给出任何规避该模式的理论需满足的两项最低充分条件。
正文 · 原文
Abstract:Large language models are the first systems to achieve high cognitive performance without clearly undergoing representation genesis: the transition from a non-representing physical system to one whose states guide behavior in a content-sensitive way. Prior cognitive systems had already made this transition before we could examine it, and philosophy of mind treated genesis as a background condition rather than an explanatory target. LLMs provide a case that does not clearly involve this transition, making the genesis question newly urgent: if genesis did not occur, which cognitive capacities are affected, and why? We currently lack the conceptual resources to answer this. The reason, this paper argues, is structural. Major frameworks in philosophy of mind, including the Language of Thought hypothesis, teleosemantics, predictive processing, enactivism, and genetic phenomenology, share a common feature when applied to the genesis question: at some explanatory step, each deploys concepts whose explanatory purchase depends on the system already being organized as a representer. This pattern, which we call the Representation Presupposition structure, generates systematic explanatory deferral. Attempts to explain the first acquisition of content-manipulable representation within the existing categorical vocabulary import resources from the representational side of the transition itself. We call this the Representation Regress. The paper offers a conceptual diagnosis rather than a new theory, establishing the structure of the problem and deriving two minimum adequacy conditions for any account that avoids this pattern. LLMs make the absence of such a theory consequential rather than merely theoretical.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.21745 [cs.AI] |
| (or arXiv:2603.21745v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.21745 arXiv-issued DOI via DataCite |
Submission history
From: Yiling Wu [view email]
[v1]
Mon, 23 Mar 2026 09:37:33 UTC (28 KB)
[v2]
Thu, 2 Apr 2026 01:17:05 UTC (28 KB)
[v3]
Sun, 1 Nov 2026 07:27:07 UTC (28 KB)
来源:arXiv cs.CL · arxiv.org