PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
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Computer Science > Machine Learning
Title:PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
Abstract:Agentic workflows often operate over shared, structured state. Because LLM context windows are limited, each model invocation is typically shown only the state fragment needed for the current workflow step, a pattern commonly known as progressive disclosure. Modern systems construct such model-facing views using grep-like keyword search, retrieval-augmented generation (RAG), abstract-syntax-tree (AST) queries, and task-specific agent skills. These methods make the read side manageable, but they do not define when a locally proposed rewrite is valid after it is applied back to the full state. The missing piece is a contract between local updates and global validity. We introduce PatchOptic, an optic-inspired interface for shared-state LLM workflows. Optics are compositional bidirectional accessors that describe how views of structured data are read and updated. PatchOptic borrows this view/update intuition and realizes it through projected reads and verified structured patches. Each workflow step declares a projected read view, an authorized write region, and a patch-source region. Beyond runtime enforcement, the same declaration yields a path-level footprint that supports delegation, sub-workflow composition, and static certificates for reordering independent steps within the same phase. We evaluate this design with PatchBench, a benchmark with 46 cases across domains. The results show that projected reads reduce reported leakage and token cost while preserving accepted-output quality under the strong actor. Runtime verification blocks declared workflow-contract violations before commit, and patch-read enforcement rejects compromised patch artifacts that use hidden sources.
| Comments: | 24 pages, 13 figures, including appendix |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Multiagent Systems (cs.MA); Programming Languages (cs.PL) |
| Cite as: | arXiv:2607.05483 [cs.LG] |
| (or arXiv:2607.05483v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05483
arXiv-issued DOI via DataCite (pending registration)
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