arXiv:2512.21577v3 Announce Type: replace Abstract: Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. We argue that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which c
The paper addresses the persistent problem of AI hallucination, which remains a key challenge for current frontier LLMs despite significant development, necessitating a unified theoretical understanding.
A unified definition of hallucination could lead to more effective mitigation strategies and accelerate the development of reliable and deployable AI systems, impact commercial applications.
This unified definition reframes hallucination as inaccurate internal world modeling, providing a new conceptual framework for researchers and developers to address the issue more systematically.
- · AI researchers
- · LLM developers
- · Enterprises deploying AI
- · AI ethics and safety organizations
- · Developers of unreliable AI systems
- · Companies with high tolerance for AI inaccuracies
Improved understanding and metrics for AI hallucination become possible.
More robust and trustworthy AI applications gain wider adoption across industries.
Reduced societal skepticism towards AI, potentially accelerating its integration into critical infrastructure and decision-making processes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL