JEPA-style objectives discard exogenous control-relevant features because they optimize temporal predictability; reward grounding recovers them with as little as 2% labeled data.
Critiques of world models
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence. There has been much discussion on what a world model really is, how to build it, how to use it, and how to evaluate it. In this essay, starting from the imagination in the famed Sci-Fi classic Dune, and drawing inspiration from the concept of ``hypothetical thinking'' in psychology literature, we argue the primary goal of a world model to be {\it simulating all actionable possibilities of the real world for purposeful reasoning and acting}. We examine the key design dimensions of world modeling: data, representation, architecture, learning objective, and usage, surveying existing approaches and analyzing their tradeoffs. Building on this examination, we propose a new Generative Latent Prediction (GLP) architecture for a general-purpose world model, based on stateful, hierarchical, multi-level, and mixed continuous/discrete representations, and a generative and self-supervised learning framework, with an outlook of a Physical, Agentic, and Nested (PAN) AGI system enabled by such a model.
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Counterfactual transport flows enable conservative, instance-specific trajectory refinement in offline RL by constructing local preference pairs in latent space from offline data and learning refinement directions controlled by a strength parameter.
SR²AM achieves competitive Pass@1 accuracy on diverse tasks with 25.8-95.3% fewer reasoning tokens than much larger models by using self-regulated simulative planning trained via supervised learning and RL.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
SiRA uses LLM world models for simulative reasoning to achieve up to 124% higher task completion and 32.2% navigation success versus reactive baselines in web environments.
A perspective article defining world models as finite-resource compression of physical state transitions and outlining a roadmap toward physical AGI via unified representations and interactive simulators.
Distinguishes agentic (externally scaffolded) from agentive (internally structured) AI systems and proposes the Goal-Identity-Configurator architecture for endogenous autonomy.
This paper introduces the Business World Model, a conceptual architecture that encodes business states, dynamics, and actions using semantic representations to support autonomous planning.
OpenWorldLib defines world models as perception-centered systems with interaction and long-term memory, and provides a modular inference codebase unifying interactive video, 3D, reasoning, and VLA tasks.
citing papers explorer
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Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study
JEPA-style objectives discard exogenous control-relevant features because they optimize temporal predictability; reward grounding recovers them with as little as 2% labeled data.
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Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Counterfactual transport flows enable conservative, instance-specific trajectory refinement in offline RL by constructing local preference pairs in latent space from offline data and learning refinement directions controlled by a strength parameter.
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Efficient Agentic Reasoning Through Self-Regulated Simulative Planning
SR²AM achieves competitive Pass@1 accuracy on diverse tasks with 25.8-95.3% fewer reasoning tokens than much larger models by using self-regulated simulative planning trained via supervised learning and RL.
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Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
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General Agentic Planning Through Simulative Reasoning with World Models
SiRA uses LLM world models for simulative reasoning to achieve up to 124% higher task completion and 32.2% navigation success versus reactive baselines in web environments.
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A Definition and Roadmap for World Models
A perspective article defining world models as finite-resource compression of physical state transitions and outlining a roadmap toward physical AGI via unified representations and interactive simulators.
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Critique of Agent Model
Distinguishes agentic (externally scaffolded) from agentive (internally structured) AI systems and proposes the Goal-Identity-Configurator architecture for endogenous autonomy.
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Business World Model
This paper introduces the Business World Model, a conceptual architecture that encodes business states, dynamics, and actions using semantic representations to support autonomous planning.
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OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
OpenWorldLib defines world models as perception-centered systems with interaction and long-term memory, and provides a modular inference codebase unifying interactive video, 3D, reasoning, and VLA tasks.