A Unified Definition of Hallucination: It's The World Model, Stupid!
On the map: World models → World models for language agents
Abstract (arXiv)
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 contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we also connect our framework to HalluWorld, a complementary benchmark that instantiates fully specified reference world models for stress-testing model hallucinations.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 69 of its 87 references that are in the map. First posted on arXiv 25 Dec 2025.
Builds on
69 of 87 references.
Closest ancestors · 2
Same direction, newest first
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Other references · 67
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Citing papers per year: 2026: 6.
6 papers.
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- 2026PhantomBench: Benchmarking the Non-existential Threat of Language Modelscited by 1
- 2026When Do Models Admit They Are Wrong? Failure Disclosure Is Unstable Under Reinforcement Learningcited by 0
- 2026When Do Models Admit They Are Wrong? Failure Disclosure Is Unstable Under Reinforcement Learning27 Sep 2026
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- 2026HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models19 May 2026
- 2026How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing25 Feb 2026
- 2026Self-Improving World Modelling with Latent Actions5 Feb 2026
- 2026Sparse Knowledge Distillation: A Mathematical Framework for Probability-Domain Temperature Scaling and Multi-Stage Compression6 Jan 2026
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