Thrilled to announce HalluWorld 😵‍💫 🌎 , a controlled benchmark + methodology to verifiably measure how LLM agents hallucinates while in an environment. Now accepted at #NeurIPS2026 Evals & Datasets!! We start with the thought that Reality is vast, dynamic, fractally unfolding. W/o a stable reference and controllable complexity, verifiably measuring and inducing situations that lead to Hallucination a.k.a deviation from observed Reality gets tough. This is even more so if we want to go from single turn Q/A setting --> Agents hallucinating while solving a task once placed in an environment. HalluWorld puts models inside environments where we know exactly what is true at every step: Gridworlds 🕹️ , Chess ♟️, and Terminal 👩‍💻 tasks. What's more? Having such environments makes things neatly modifiable to am up support different levers of complexity, think canyons, signboards and long tunnels (Gridworlds) ; arbitrarily complex game situations or mods (Chess) inter alia --> This gives us ways to craft situations that amp up cognitive pressure on the agent This is inspired and in vein of the bAbi methodology of creating questions with knowable answers from a world you can create by @jaseweston et al from 2015. Come check out more at halluworld.ai , or read our arXiv at arxiv.org/abs/2605.19341 ! It was enriching and fun working on this with @_emliu , @karanps, @stevenyfeng, @_michael_yu_, @ZhuofuTao and @shocheen from @LTIatCMU , @PatronusAI , @stanfordnlp and @OhioStateCSE over the past few months!