6 papers
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
Diego Gosmar, Deborah A. Dahl
This paper describes an approach to hallucination detection and mitigation using a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic simil…
Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
Diego Gosmar, Deborah A. Dahl
Prompt injection remains a central obstacle to the safe deployment of large language models, particularly in multi-agent settings where intermediate outputs can propagate or amplif…
Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems
Diego Gosmar, Deborah A. Dahl
This paper proposes a novel architectural framework aimed at enhancing security and reliability in multi-agent systems (MAS). A central component of this framework is a network of…
Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks
Diego Gosmar, Deborah A. Dahl, Dario Gosmar
Prompt injection constitutes a significant challenge for generative AI systems by inducing unintended outputs. We introduce a multi-agent NLP framework specifically designed to add…
Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks
Diego Gosmar, Deborah A. Dahl
Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating mult…
AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations
Diego Gosmar, Deborah A. Dahl, Emmett Coin +1
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…