paper-with-me

홈 › Papers

Opus: A Workflow Intention Framework for Complex Workflow Generation

2025-02-25 · Phillip Kingston, Théo Fagnoni, Mahsun Altın

This paper introduces Workflow Intention, a novel framework for identifying and encoding process objectives within complex business environments. Workflow Intention is the alignment of Input, Process and Output elements defining a Workflow's transformation objective interpreted from Workflow Signal inside Business Artefacts. It specifies how Input is processed to achieve desired Output, incorporating quality standards, business rules, compliance requirements and constraints. We adopt an end-to-end Business Artefact Encoder and Workflow Signal interpretation methodology involving four steps: Modality-Specific Encoding, Intra-Modality Attention, Inter-Modality Fusion Attention then Intention Decoding. We provide training procedures and critical loss function definitions. In this paper we introduce the concepts of Workflow Signal and Workflow Intention, where Workflow Signal decomposed into Input, Process and Output elements is interpreted from Business Artefacts, and Workflow Intention is a complete triple of these elements. We introduce a mathematical framework for representing Workflow Signal as a vector and Workflow Intention as a tensor, formalizing properties of these objects. Finally, we propose a modular, scalable, trainable, attention-based multimodal generative system to resolve Workflow Intention from Business Artefacts.

📄 PDF Abstract BibTeX arXiv:2502.19532

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Attention 설명 없음
ADOPT Please enter a description about the method here

Similar Papers 제목 키워드 기반

Opus: A Prompt Intention Framework for Complex Workflow Generation

2025-07-15 · Théo Fagnoni, Mahsun Altın, Chia En Chung, Phillip Kingston 외

This paper introduces the Opus Prompt Intention Framework, designed to improve complex Workflow Generation with instruction-tuned Large Language Models (LLMs). We propose an intermediate Intention Capture layer between u…

Opus: A Large Work Model for Complex Workflow Generation

2024-11-30 · Théo Fagnoni, Bellinda Mesbah, Mahsun Altın, Phillip Kingston

This paper introduces Opus, a novel framework for generating and optimizing Workflows tailored to complex Business Process Outsourcing (BPO) use cases, focusing on cost reduction and quality enhancement while adhering to…

Opus: A Quantitative Framework for Workflow Evaluation

2025-11-06 · Alan Seroul, Théo Fagnoni, Inès Adnani, Dana O. Mohamed 외 arxiv

This paper introduces the Opus Workflow Evaluation Framework, a probabilistic-normative formulation for quantifying Workflow quality and efficiency. It integrates notions of correctness, reliability, and cost into a cohe…

Reinforcement LearningWorkflow Discovery

Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture

2024-05-21 · Jinyi Deng, Xinru Tang, Zhiheng Yue, Guangyang Lu 외

Given the increasing complexity of AI applications, traditional spatial architectures frequently fall short. Our analysis identifies a pattern of interconnected, multi-faceted tasks encompassing both AI and general compu…

Scheduling

LongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks

2026-08-24 · Xiao Zhang, Qumeng Sun, Jihao Li, Yiming Ren 외 arxiv

Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet succes…

Mathematical ReasoningCode Generation