Mental World Modeling

Hugging Face Daily Papers Papers
world-modeling mental-state llm decision-making framework research

Summary

The paper introduces Mental World Modeling (MWM), a framework that integrates hidden mental states as core components of world models, and presents MENTIS, a training-free baseline. Experiments with 8 LLM-based world models show explicit mental-state modeling is essential for predicting human decisions in situated scenarios.

World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model rather than posthoc rationales: MWM aintains a coupled physical-mental world state, renders a target-specific partial observation, and simulates how candidate actions jointly update both components. We instantiate the framework in MENTIS, a training-free and fully inspectable baseline that decomposes the process into state parsing, target-observation generation, action decomposition, coupled physical and mental transition, and branch-level value evaluation. On a manually constructed, quality-controlled dataset of situated decision scenarios spanning text, image, and sounding-video stories, experiments with 8 modern LLM-based world models demonstrate that explicitly modeling the mental state is essential for predicting human decisions. Deeper analyses further expose the bottlenecks of current mental world modeling. We expect MWM as a next stage of world modeling, from simulating physical scenes to simulating the minds that act in them.
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Source: https://huggingface.co/papers/2607.27201

Abstract

Worldmodelsenableapredictivesubstrateforplanningandaction,yetexistingformulationsmerelyansweraphysicalquestion:what/whereitis,andhowwillitevolve.Humanbehavior,however,isdrivenbyhiddenmentalstate(whatapersonbelieves,wants,intends,feels,andconsiderssociallypermissible),soamodelthattracksthephysicalscenebutnotwhateachagentknowsandbelievesaboutitpredictsthewrongactionfortheright-lookingscene.WeformulateMentalWorldModeling(MWM),agenerictheoreticalframeworkthatmakesmentalvariablescorecomponentsofaworldmodelratherthanposthocrationales:MWMaintainsacoupledphysical-mentalworldstate,rendersatarget-specificpartialobservation,andsimulateshowcandidateactionsjointlyupdatebothcomponents.WeinstantiatetheframeworkinMENTIS,atraining-freeandfullyinspectablebaselinethatdecomposestheprocessintostateparsing,target-observationgeneration,actiondecomposition,coupledphysicalandmentaltransition,andbranch-levelvalueevaluation.Onamanuallyconstructed,quality-controlleddatasetofsituateddecisionscenariosspanningtext,image,andsounding-videostories,experimentswith8modernLLM-basedworldmodelsdemonstratethatexplicitlymodelingthementalstateisessentialforpredictinghumandecisions.Deeperanalysesfurtherexposethebottlenecksofcurrentmentalworldmodeling.WeexpectMWMasanextstageofworldmodeling,fromsimulatingphysicalscenestosimulatingthemindsthatactinthem.

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