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Beyond the Parameter Monolith: Reconstructive Memories, Executable Skills, and Residual Assembly for Language Models

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 04:18:25 UTC.

Briefings

This article proposes FEM-ASM, a system architecture separating language-model computation, persistent storage, and execution. It introduces a finite-element-method-inspired organization where document states and executable skills contribute typed proposals to a shared state, reconciled by an explicit residual operator. The system evaluates this through controlled experiments using a versioned store with 52,809 reconstructive memory elements near a 1.7-billion-floating-value budget, achieving approximately 75% token accuracy. Key findings include attention-free multi-mesh prototypes learning causal language modeling but failing to achieve competitive general capability, and positional result observations improving neural rendering over repeated global vectors. The research supports a separation of storage, execution, and neural coordination while highlighting unresolved limitations in question-only retrieval, unrestricted answer generation, and end-to-end efficiency.

Key points

• FEM-ASM is a system architecture separating language-model computation, persistent storage, and execution using finite-element-method-inspired organization.
• A versioned store contains 52,809 reconstructive memory elements near a 1.7-billion-floating-value budget, achieving approximately 75% token accuracy.

Context and limitations

The system evaluates architecture through controlled experiments and negative results rather than claiming a physical finite-element formulation of language. Attention-free multi-mesh prototypes learn causal language modeling but do not establish competitive general capability. Coverage is limited to supported evidence and does not establish utility of loading an entire multi-billion-value store.

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