What does this research mean for the field?

Maintaining a testable chain that links code-side repository objects to model-side neural sites enhances the utility and interpretability of an LLM atlas beyond a static neuron representation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To formalize the linking of code-side objects with model-side neuron sites in a testable framework.

June 21, 2026Open Access

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Mercury Repository-to-Neuron Addressability

HCHo Yiing ChenTiGenix (Spain)

Key Points

To formalize the linking of code-side objects with model-side neuron sites in a testable framework.
Introduced a methodology for linking various elements such as files, modules, layers, and dimensions in the Mercury context.
Developed a framework to visualize the connections within a large language model (LLM) atlas.
Proposed LLM atlas framework enables a more complex understanding of neuron relationships.
Demonstrated enhanced addressability of neurons through linked components, improving testability in neural modeling.

Abstract

A Mercury methods paper formalizing an address lift from code-side objects to model-side sites. The paper argues that an LLM atlas becomes more than a neuron picture when files, modules, layers, dimensions, heads, and measured sites remain linked in a testable chain. Copyright (c) 2026 Chen, Ho Yiing (norika). Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

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Mercury Repository-to-Neuron Addressability

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