paper-with-me

홈 › Papers

From Task-Guided Conversational Graphs to Goal-Oriented Dialogue Runtimes

2026-06-22 · Mariano Garralda-Barrio arxiv

Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives. This conceptual systems paper focuses on the high-complexity end of that design space, where goals can be suspended, resumed, revised, and invalidated by actions in other goals. We introduce the Goal-Oriented Dialogue Runtime (GODR), a framework-neutral design pattern that treats goals, task frames, lifecycle state, invalidation rules, and resumption contracts as first-class runtime objects while delegating bounded execution to graph runtimes, agents, tools, or application programming interfaces (APIs). GODR is not proposed as a replacement for workflow graphs in simple guided processes; it is intended for complex, multi-domain, interruptible conversations where objective continuity cannot be recovered reliably from agent identity, chat history, or execution-graph position alone. The paper formalizes the problem, proposes runtime objects and architecture-selection criteria, and frames evaluation as an agenda for future empirical validation rather than as a measured performance claim.

📄 PDF Abstract BibTeX arXiv:2606.23797

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Target-Guided Open-Domain Conversation Planning

2022-09-20 · COLING 2022 10 · Yosuke Kishinami, Reina Akama, Shiki Sato, Ryoko Tokuhisa 외

Prior studies addressing target-oriented conversational tasks lack a crucial notion that has been intensively studied in the context of goal-oriented artificial intelligence agents, namely, planning. In this study, we pr…

Retrieval

Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation

2019-09-19 · CONLL 2019 11 · Yi-An Lai, Arshit Gupta, Yi Zhang

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns…

Data AugmentationDecoderDialogue GenerationResponse Generation

BoilerBot: A reliable task-oriented chatbot enhanced with large language models

2023-10-03 · Alexa Prize TaskBot Challenge 2 Proceedings 2023 10 · Yifei Hu, Jinen Setpal, Damin Zhang, Jacob Zietek 외

This paper outlines the design and deployment of BoilerBot: a task-oriented multi- modal conversational agent developed for the Alexa Prize TaskBot 2 competition. BoilerBot features flexible response generation, leveragi…

ChatbotResponse Generation

Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents

2021-03-18 · Ashish Shenoy, Sravan Bodapati, Katrin Kirchhoff

Goal-oriented conversational interfaces are designed to accomplish specific tasks and typically have interactions that tend to span multiple turns adhering to a pre-defined structure and a goal. However, conventional neu…

Automatic Speech RecognitionAutomatic Speech Recognition (ASR)ChatbotNatural Language Understanding+3

Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI

2025-01-10 · Yuya Asano, Sabit Hassan, Paras Sharma, Anthony Sicilia 외

General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named entities. We extend correction to tasks th…

Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+5