Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
Researchers propose a conceptual model to represent LLM workflows as persistent knowledge objects, enabling better tracking and reuse of workflow definitions and execution traces.
- Proposes a conceptual model to represent LLM workflows as persistent knowledge objects, not just execution traces.
- Introduces symbolic forms, object identity, and live-image thinking as explanatory lenses for workflow modeling.
- Aims to improve traceability, reuse, and cumulative learning in LLM-mediated workflows.
- Remains language-independent while drawing inspiration from Lisp's symbolic approach.
A new research paper titled 'Workflow as Knowledge' introduces a conceptual framework for representing LLM-mediated workflows as persistent knowledge objects. The model draws inspiration from Lisp but remains language-independent, focusing on symbolic forms, object identity, and live-image thinking as explanatory tools rather than implementation requirements.
The framework proposes representing workflow definitions, instances, inference records, context snapshots, and dependency relations as persistent knowledge objects within a shared knowledge base. This approach aims to address challenges in tracking, reusing, and reasoning about complex workflows that involve tool use, retrieval, branching, checkpointing, and human approval.
By treating workflows as knowledge rather than ephemeral execution traces, the model could enable better reproducibility, debugging, and optimization of LLM applications. The paper emphasizes the need for a shared representation that persists beyond individual workflow runs, allowing for cumulative learning and analysis across multiple executions.
Offers a new way to model and persist workflows, enabling better debugging and optimization of LLM applications.
Could improve the reliability and reusability of AI-driven processes in production environments.
Introduces a novel conceptual framework for understanding LLM workflows beyond traditional execution models.
Advances the idea of treating AI workflows as persistent knowledge, not just temporary processes.
- LLM-mediated workflows
- Workflows where large language models are used to perform tasks, often involving tool use, retrieval, and human interaction.
- Persistent knowledge objects
- Data structures that retain their state and relationships over time, enabling reuse and analysis across multiple executions.
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