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This comprehensive explanation has been generated from 57 GitHub source documents. All source documents are searchable here.
Last updated: October 7, 2025
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Note: In rare cases it may contain LLM hallucinations.
For authoritative documentation, please consult the official GLEIF vLEI trainings and the ToIP Glossary.
A neural network-based language model with billions of parameters trained on large text corpora using self-supervised learning, capable of generating human-like text and performing various natural language tasks.
A Large Language Model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning. This definition is taken directly from Wikipedia and represents the canonical definition used within the KERI/GLEIF glossary system.
The LLM entry appears in the KERI Foundation's vLEI glossary as a minimal reference stub with no KERI-specific content or technical integration details. According to the source documents, this entry:
The inclusion of this term in the glossary appears to serve several purposes:
Terminology Completeness: As part of a comprehensive glossary system covering 459 canonical terms, the LLM entry ensures that common technical terminology is defined, even when not directly related to KERI protocols
Reference Navigation: The entry provides aliasing and navigation support within the glossary infrastructure (as evidenced by the separate "LLM.md" redirect stub)
Future-Proofing: The document states that "this definition may provide context for understanding how AI technologies could interact with or process credential data in future applications," suggesting the term is included for potential future relevance rather than current technical integration
When integrating LLMs with KERI/ACDC systems:
The source documents explicitly characterize this entry as a "glossary entry stub" and note its minimal nature:
The entry provides a Spec-Up-T link for viewing the term in the rendered glossary interface but contains no KERI-specific context, use cases, or technical integration details.
While LLMs are not part of KERI protocol specifications, they appear tangentially in the broader KERI documentation ecosystem:
The KERI community maintains extensive technical documentation across multiple repositories and formats. The glossary system itself represents a significant documentation management challenge, and the source documents reference various documentation tools and processes:
In this context, LLMs could theoretically be relevant to documentation processing, though the source documents provide no evidence of actual implementation or integration.
The KERI ecosystem includes educational resources such as the GLEIF vLEI training notebooks. These Jupyter notebook-based tutorials provide hands-on guidance for:
While these training materials represent substantial documentation that could theoretically be processed by LLMs, the source documents contain no indication that LLMs are actually used in creating, maintaining, or delivering this training content.
A thorough review of the 57 source documents reveals no technical specifications, implementations, or design documents describing how LLMs integrate with:
The extensive technical specifications for KERI, ACDC, CESR, SAID, OOBI, IPEX, and related protocols make no mention of LLM integration or interaction patterns.
To understand the context in which the LLM term exists within the glossary, it's useful to examine the surrounding KERI/ACDC concepts that are extensively documented:
ACDCs represent the credential technology within the KERI ecosystem, providing:
The ACDC specifications make no reference to LLM processing, generation, or verification of credentials.
KERI provides the foundational identity layer through:
The KERI protocol specifications contain no mechanisms for LLM interaction or integration.
CESR provides the encoding layer with:
The CESR specifications define encoding for cryptographic primitives, not for LLM-generated or LLM-processed content.
The TSP specifications define secure messaging with:
The TSP documentation makes no provision for LLM-mediated communications or LLM-generated message content.
The vLEI ecosystem operates under extensive governance frameworks maintained by GLEIF, including:
These governance documents specify human roles, organizational responsibilities, and legal requirements. They contain no policies, procedures, or considerations related to LLM involvement in credential issuance, verification, or management.
While the source documents contain no actual LLM integration with KERI/ACDC systems, some theoretical observations can be made based on the documented architectures:
KERI and ACDC systems are built on principles of end-verifiability and cryptographic proof. Any hypothetical LLM interaction would need to maintain these properties:
These requirements suggest that LLMs could not be integrated in ways that compromise the fundamental security properties of KERI-based systems.
The KERI ecosystem emphasizes privacy through mechanisms like:
Any theoretical LLM processing of credential data would need to respect these privacy-preserving design principles.
KERI systems provide secure attribution through:
These mechanisms ensure that all data can be cryptographically attributed to its source, a property that would need to extend to any LLM-generated content within such systems.
The KERI community maintains several documentation infrastructure components:
The Web-of-Trust GitHub repository includes KERISSE, described as a "Docusaurus self-education site" with "Typesense search facilities." This infrastructure:
While search infrastructure could theoretically benefit from LLM technologies, the source documents provide no evidence of such integration.
The glossary system itself demonstrates sophisticated information management:
This infrastructure represents significant documentation complexity that could theoretically be enhanced by LLM technologies for tasks like link validation, consistency checking, or automated summarization—but again, no evidence of such implementation exists in the source documents.
The Large Language Model entry in the KERI/GLEIF glossary is a minimal reference stub providing only a basic Wikipedia-sourced definition with no KERI-specific content, technical integration details, or practical applications. The entry serves primarily as:
The 57 source documents reveal no technical specifications, implementations, design documents, governance policies, or use cases involving LLMs in KERI protocol operations, ACDC credential processing, vLEI ecosystem operations, or related infrastructure. The extensive technical documentation for KERI, ACDC, CESR, TSP, and other protocols makes no provision for LLM integration.
While the KERI ecosystem maintains substantial documentation infrastructure that could theoretically benefit from LLM technologies, and while the cryptographic properties of KERI systems would impose specific constraints on any such integration, these remain purely theoretical considerations not reflected in current specifications or implementations.
The characterization of this entry as a "glossary entry stub" with "minimal substantive content" accurately reflects its current status: a basic terminological reference with no KERI-specific elaboration, included for completeness within the broader glossary system rather than to document any actual technical integration or application within the KERI/ACDC/vLEI ecosystem.