Pith. sign in

REVIEW 1 cited by

AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.05828 v1 pith:QCZNT36T submitted 2024-11-05 cs.AI cs.MA

classification cs.AIcs.MA
keywords agentsextensioninteroperabilityconceptsconvenerconversationsinteractionsmulti-agent
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper presents a novel extension to the existing Multi-Agent Interoperability specifications of the Open Voice Interoperability Initiative (originally also known as OVON from the Open Voice Network). This extension enables AI agents developed with different technologies to communicate using a universal, natural language-based API or NLP-based standard APIs. Focusing on the management of multiparty AI conversations, this work introduces new concepts such as the Convener Agent, Floor-Shared Conversational Space, Floor Manager, Multi-Conversant Support, and mechanisms for handling Interruptions and Uninvited Agents. Additionally, it explores the Convener's role as a message relay and controller of participant interactions, enhancing both scalability and security. These advancements are crucial for ensuring smooth, efficient, and secure interactions in scenarios where multiple AI agents need to collaborate, debate, or contribute to a discussion. The paper elaborates on these concepts and provides practical examples, illustrating their implementation within the conversation envelope structure.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks

    cs.CL 2025-01 reject novelty 5.0 of 10

    A three-agent pipeline with OVON JSON messages lowers the authors' disclaimer-based hallucination score on 310 prompts, but that score does not measure truth.

Pith tools