PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates

• URL: http://arxiv.org/abs/2607.05483v1
• Date: Mon, 06 Jul 2026 16:13:56 GMT
• Title: PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
• Abstract summary: We introduce PatchOptic, a contract between local updates and global validity.<n>Each workflow step declares a projected read view, an authorized write region, and a patch-source region.<n>We evaluate this design with PatchBench, a benchmark with 46 cases across reported leakage and token cost.
• Score: 0.013714053458441644
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Don't Let the Model Write the YAML: Deterministic, Minimal-Diff GitOps Remediation from LLM-Proposed Field Changes [0.0]
  In GitOps, having the model author the edited file or a diff is what practitioners reach for first.<n>We present an alternative that separates the semantic decision (which resource, field, and value) from the syntactic act of editing the file.<n>Because the file is never re-serialized, the diff is minimal by construction, and the edit is correct and deterministic independent of the model.
  arXiv  Detail & Related papers  (2026-08-31T18:37:12Z)
• Natural-Language Workflows Are Not Software Yet: Artifact-Driven Compilation for Reliable Agent Execution [23.207195503032136]
  Artic is an artifact-driven workflow compiler that transforms a natural-language workflow into an artifact-driven workflow.<n>We show that compiled by Artic are 32 and 56 percentage points more consistent in cross-model and repeated-execution setups.
  arXiv  Detail & Related papers  (2026-08-21T17:47:47Z)
• RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models [54.53236295237077]
  We introduce RepoReasoner, a benchmark for evaluating repository-level code reasoning.<n> Output Prediction measures fine-grained, stateful execution reasoning across files.<n>Call Chain Prediction evaluates high-level architectural dependency understanding under noisy context.
  arXiv  Detail & Related papers  (2026-07-28T17:12:41Z)
• The Patchwork Problem in LLM-Generated Code [3.4562767039451674]
  This paper formalizes structural coherence as consistency invariants over graph representations of repository artifacts.<n> Empirical evaluation across two frontier models reveals that the vast majority of structural failures evade type checking, testing, and SAST entirely.
  arXiv  Detail & Related papers  (2026-07-09T23:01:54Z)
• Beyond Summaries: Structure-Aware Labeling of Code Changes with Large Language Models [0.0]
  We present a systematic study of using large language models (LLMs) for taxonomy-based labeling of code changes in a code patch.<n>Our approach employs few-shot prompting to produce language-agnostic and customizable labels, without the engineering overhead of traditional analysis pipelines.
  arXiv  Detail & Related papers  (2026-05-25T17:56:46Z)
• ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both [55.182037225013836]
  ATLAS is a framework in which a single discrete 'word', termed as a functional token, serves both as an agentic operation and a latent visual reasoning unit.<n> ATLAS achieves superior performance on challenging benchmarks while maintaining clear interpretability.
  arXiv  Detail & Related papers  (2026-05-14T17:59:55Z)
• DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion [0.0]
  DocSync is an agentic workflow that frames documentation maintenance as a structurally grounded, iterative generation task.<n>We empirically evaluate a resource-constrained implementation of DocSync-using a LoRA-adapted small language model.<n>Our findings demonstrate that this AST-aware agentic approach substantially outperforms standard encoder-decoder baselines.
  arXiv  Detail & Related papers  (2026-05-04T02:41:33Z)
• GALA: Multimodal Graph Alignment for Bug Localization in Automated Program Repair [40.228155198574356]
  We propose GALA, a framework that shifts multimodal Automated Program Repair (APR) from implicit semantic guessing to explicit structural reasoning.<n>GALA operates in four stages: it first constructs an Image UI Graph to capture visual elements and their structural relationships; then performs file-level alignment by cross-referencing this UI graph with repository-level structures.<n>By enforcing both semantic and relational consistency across modalities, GALA establishes a highly accurate visual-to-code mapping.
  arXiv  Detail & Related papers  (2026-04-09T11:06:25Z)
• PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing [35.392342990789324]
  Paper Debugger is an in-editor, multi-agent, and plugin-based academic writing assistant.<n>Our demo showcases a fully integrated workflow, including localized edits, structured reviews, parallel agent execution, and diff-based updates.
  arXiv  Detail & Related papers  (2025-12-02T10:00:37Z)
• Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs [88.68484904214142]
  We introduce Patch-as-Decodable Token (PaDT) to generate both textual and diverse visual outputs.<n>Central to PaDT are Visual Reference Tokens (VRTs), derived from visual patch embeddings of query images.<n>We show PaDT consistently achieves state-of-the-art performance, even compared with significantly larger MLLM models.
  arXiv  Detail & Related papers  (2025-10-02T12:23:57Z)
• Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking [51.417096446156926]
  We introduce a new task: detecting post-generation edits locally made to watermarked LLM outputs.<n>We propose a pattern-based watermarking framework, which partitions the vocabulary into disjoint subsets and embeds the watermark.<n>We evaluate our method on open-source LLMs across a variety of editing scenarios, demonstrating strong empirical performance in edit localization.
  arXiv  Detail & Related papers  (2025-10-02T03:33:12Z)
• SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
  We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
  arXiv  Detail & Related papers  (2025-05-29T18:28:02Z)
• TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning [16.881957688535557]
  TextRefiner is a plug-and-play method to refine the text prompts of existing methods.<n>It builds a novel local cache module to encapsulate fine-grained visual concepts.<n>It achieves state-of-the-art performance and is efficient in inference.
  arXiv  Detail & Related papers  (2024-12-11T08:07:12Z)
• Editing Arbitrary Propositions in LLMs without Subject Labels [88.67755930096966]
  We introduce a simple and fast localization method called Gradient Tracing (GT) GT allows editing arbitrary propositions instead of just binary ones, and does so without the need for subject labels. We show that our method -- without access to subject labels -- performs close to state-of-the-art L&E methods which has access subject labels.
  arXiv  Detail & Related papers  (2024-01-15T08:08:24Z)
• Context Autoencoder for Self-Supervised Representation Learning [64.63908944426224]
  We pretrain an encoder by making predictions in the encoded representation space. The network is an encoder-regressor-decoder architecture. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks.
  arXiv  Detail & Related papers  (2022-02-07T09:33:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.