How to Build Marcus's Algebraic Mind: Algebro-Deterministic Substrate over Galois Fields
- URL: http://arxiv.org/abs/2605.21379v2
- Date: Thu, 21 May 2026 17:23:56 GMT
- Title: How to Build Marcus's Algebraic Mind: Algebro-Deterministic Substrate over Galois Fields
- Abstract summary: PyVaCoAl/VaCoAl is a hyperdimensional computing architecture organized end-to-end around a single algebraic primitive.<n>In this paper, we map the correspondence between Marcus's three pillars and the operational commitments of PyVaCoAl/VaCoAl.
- Score: 18.56157183294801
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In The Algebraic Mind, Gary Marcus identified three components essential for any adequate cognitive architecture: operations over variables, recursively structured representations, and a distinction between mental representations of individuals and kinds. He argued that standard multilayer perceptrons supported none of these, acknowledging that a neural implementation using registers and treelets, constructed via developmental programs rather than gradient descent, remained a programmatic conjecture. Twenty-five years later, the required substrate is now available. Our newly developed PyVaCoAl/VaCoAl is a hyperdimensional computing architecture organized end-to-end around a single algebraic primitive: XOR-and-shift over GF(2), implemented by primitive-polynomial linear-feedback shift registers. The architecture supports reversible variable binding via Bind(R,F) = R XOR shift(F), non-commutative compositional bundling that distinguishes "the dog bites the man" from "the man bites the dog," and address-space individual/kind separation under the same algebra. A companion perspective argues that the dentate gyrus-CA3 circuit is a biological homologue of this same engine, with developmentally specified mossy-fiber targeting supplying the innate microcircuitry Marcus anticipated. In this paper, we map the correspondence between Marcus's three pillars and the operational commitments of PyVaCoAl/VaCoAl. We reinterpret the treelet as an algebraic register set indexed by a primitive generator polynomial, arguing that this architecture provides a functional neural substrate meeting Marcus's specifications far more closely than the tensor products, circular convolution, or temporal synchrony available in 2001. We also demonstrate how this substrate naturally extends to Pearl's rung-3 counterfactual reasoning, a capability the original treelet program did not directly target.
Related papers
- Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling [55.36837305276515]
We show that the complete bilinear operator over content-geometry outer products--the sufficient statistic of all second-order interactions--is the expressive ceiling.<n>We then introduce Hyper-Fold, a rank-K separable convolutional backbone approaching this ceiling at message-passing cost.
arXiv Detail & Related papers (2026-08-29T11:43:25Z) - Unifying Graph Neural Networks Through a Common Layer Equation [75.88127999406392]
We introduce a common layer equation that represents covered architectures through seven components.<n>The central factorization separates where information moves, encoded by the propagation bank, from what moves, encoded by the message maps.<n>We make this unification explicit and checkable through worked reductions of canonical layers and component assignments.
arXiv Detail & Related papers (2026-08-17T04:37:59Z) - How to Build Marcus's Algebraic Mind: From Minsky's Emotion-Machine Viewpoint [18.56157183294801]
In The Algebraic Mind, Marcus identified three cognitive components.<n>A companion paper solves this with VaCoAl, an architecture built on GF(2) XOR-and-shift.<n>A collision-avoidance scheme ensures exact recovery within a Frontier Size.
arXiv Detail & Related papers (2026-07-18T03:48:00Z) - How to Build Marcus's Algebraic Mind: From Thagard's Brain--Mind Viewpoint [18.56157183294801]
Two critiques of connectionist cognition converge on one missing capacity.<n>We argue one substrate answers both.<n>VaCoAl is a hyperdimensional architecture built on one primitive, XOR-and-shift over GF(2), on primitive-polynomial linear-feedback shift registers.
arXiv Detail & Related papers (2026-07-18T01:12:54Z) - Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEM [18.56157183294801]
We show VaCoAl, an algebro-deterministic hyperdimensional memory built from Galois-field linear-feedback shift registers.<n>We map two distinct VaCoAl regimes to the EC-CA3 direct and EC-DG-CA3 trisynaptic pathways.<n>We prove formal correspondences and derive testable iEEG predictions.
arXiv Detail & Related papers (2026-05-15T06:13:58Z) - Geometric Factual Recall in Transformers [57.48371649045765]
A common view casts internal weight matrices as associative memories over pairs of embeddings, requiring memorization counts that scale linearly with the number of facts.<n>We develop a theoretical and empirical account of an alternative, emphgeometric form of memorization in which learned embeddings encode structure directly.<n>In a controlled setting where a single-layer transformer must memorize random vectors from subjects to a shared attribute set, we prove that a logarithmic embedding dimension suffices.<n>We extend these results to the multi-hop setting, providing constructions with and without chain-of-thought that exhibit a provable
arXiv Detail & Related papers (2026-05-12T17:22:22Z) - Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM "VaCoAl" for Ultra-High Speed, Ultra-Low Power, and Low Cost> [18.56157183294801]
We propose VaCoAl on ultra-high-dimensional binary spaces on ultra-high-dimensional RAM/DRAM-CAM.<n>It embeds a cognitive bound -- the Frontier Size -- into its architecture, ranking candidates by path-integral confidence.<n>We evaluate multi-hop reasoning on about 470k mentor-student relations from Wikidata, tracing up to 57 generations.
arXiv Detail & Related papers (2026-04-13T16:13:17Z) - Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning [73.18052192964349]
We develop a theoretical framework that explains how discrete symbolic structures can emerge naturally from continuous neural network training dynamics.<n>By lifting neural parameters to a measure space and modeling training as Wasserstein gradient flow, we show that under geometric constraints, the parameter measure $mu_t$ undergoes two concurrent phenomena.
arXiv Detail & Related papers (2025-06-26T22:40:30Z) - ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search [77.55575655986252]
ProtInvTree is a reward-guided tree-search framework for protein inverse folding.<n>It reformulates sequence generation as a deliberate, step-wise decision-making process.<n>It supports flexible test-time scaling by expanding the search depth and breadth without retraining.
arXiv Detail & Related papers (2025-06-01T09:34:20Z) - Self-Attention as a Parametric Endofunctor: A Categorical Framework for Transformer Architectures [0.0]
We develop a category-theoretic framework focusing on the linear components of self-attention.<n>We show that the query, key, and value maps naturally define a parametric 1-morphism in the 2-category $mathbfPara(Vect)$.<n> stacking multiple self-attention layers corresponds to constructing the free monad on this endofunctor.
arXiv Detail & Related papers (2025-01-06T11:14:18Z) - X-volution: On the unification of convolution and self-attention [52.80459687846842]
We propose a multi-branch elementary module composed of both convolution and self-attention operation.
The proposed X-volution achieves highly competitive visual understanding improvements.
arXiv Detail & Related papers (2021-06-04T04:32:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.