arXiv:2606.12385v1 Announce Type: new Abstract: Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These dependencies are recursive: a model may depend on an upstream artifact whose own dependencies are documented only in separate releases and artifacts. As a result, the full dependency structure is fragmented across heterogeneous public artifacts, with complexity and recursive depth far outpacing humans' ability to trace. We introduce ModSleuth, an agentic system that recursively reconstructs LLM de
The increasing complexity and recursive dependencies within LLM training pipelines necessitate tools for auditing and understanding their origins, a problem made urgent by the rapid advancement and deployment of these models.
A strategic reader should care because unchecked recursive dependencies introduce significant risks around intellectual property, data provenance, bias propagation, and security for critical AI infrastructure.
The introduction of tools like ModSleuth can fundamentally alter how LLMs are developed, audited, and regulated, pushing for greater transparency and accountability in their creation.
- · AI auditing firms
- · Model developers with transparent pipelines
- · Regulators and policymakers
- · Enterprise AI adopters
- · Opaque LLM developers
- · Models with untraceable dependencies
- · Entities relying on undocumented AI assets
ModSleuth allows for a systematic understanding of the complex, recursive dependency graphs of modern large language models.
This transparency will force greater accountability in model development, prompting more rigorous documentation and ethical sourcing of training data and upstream models.
The ability to audit dependencies could become a prerequisite for regulatory compliance and enterprise adoption, influencing investment and market share in the AI sector.
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