Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science
Not on the map. Why?
Abstract (arXiv)
We argue that tacit knowing is better modelled as an operating regime of one memory substrate than as a kind of content. The substrate is VaCoAl, an algebro-deterministic XOR-and-shift hyperdimensional memory over GF(2), realized as CASRAM (commercial SRAM-CAM) and PyVaCoAl (DRAM-CAM).In VaCoAl/PyVaCoAl a directed acyclic graph (DAG) reads two ways. Repairing every collision (Rescue mode) makes the trace exactly auditable but ranks no path higher; tolerating collisions (Don't Care mode) yields a path-integrated confidence, CR2, that silently favours direct routes. This audit-valuation complementarity is a formal counterpart of Polanyi's claim that tacit and explicit knowledge are not sharply divided. It separates contingent tacitness, which a new query brings out (AlphaGo's game tree), from regime-bound tacitness, statable only in a regime where it is gone (AlphaGo's hunches). (1) Polanyi's from-to structure reads as non-commutative binding, the Identikit as role-filler composition, recognition as majority-vote coincidence. (2) Explicating the valuation destroys it, as attending to his fingers can make Polanyi's pianist falter, yet a closed-form law states it. Polanyi, Nonaka, Tsoukas and Collins each capture part of this but narrowly; even Nonaka and von Krogh's continuum cannot tell contingent from regime-bound tacitness. (3) Collins's three tacit knowledges become strata, one outside the substrate. (4) Our mentor-student DAG is a tacit-transmission graph. (5) Terence Tao's remark that AI-solved problems spawn few follow-ups is diagnosed as rootless explicit knowledge: checkable results whose route no one can follow and that no one yet understands well enough to extend, teach or judge. To re-root them, LLMs generate, VaCoAl/PyVaCoAl keeps an auditable trace with its confidence, and people value, a role virtue epistemology shows cannot be offloaded. Testable predictions follow.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 12 Oct 2026.
Builds on
10 of 41 references.
References · 10
- 2023The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"cited by 407
- 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 363
- 2025What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structurecited by 6
- 2025Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architecturescited by 3
- 2026Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEMcited by 2
- 2026How to Build Marcus's Algebraic Mind: Algebro-Deterministic Substrate over Galois Fieldscited by 1
- 2026Institutions and the transmission of upper-tail human capital: scientific lineages across a millenniumcited by 1
- 2026How to Build Marcus's Algebraic Mind: From Thagard's Brain--Mind Viewpointcited by 1
- 2026How to Build Marcus's Algebraic Mind: From Minsky's Emotion-Machine Viewpointcited by 1
- 2026Computational Prosopography across a Millennium: Mathematically Oriented Lineages Traced from the Fields Medalistscited by 1
Built on by
Not cited yet.