LCDrive unifies chain-of-thought reasoning and action selection for end-to-end driving by interleaving action-proposal tokens and latent world-model tokens that predict action outcomes, yielding faster inference and better trajectories than text-based or non-reasoning baselines.
Safedreamer: Safe reinforcement learn- ing with world models.arXiv preprint arXiv:2307.07176
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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.
World models enable efficient AI planning but create risks from adversarial corruption, goal misgeneralization, and human bias, demonstrated via attacks that amplify errors and reduce rewards on models like RSSM and DreamerV3.
Proposes hierarchical MARL framework enforcing safety via constraint manifold at low level with theoretical guarantees and stationary dynamics for stable training and generalization.
SHAPO adds a sharpness-aware adjustment to policy optimization that reweights gradients to favor conservative behavior in uncertain areas, yielding better safety-performance tradeoffs on continuous control tasks.
citing papers explorer
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Latent Chain-of-Thought World Modeling for End-to-End Driving
LCDrive unifies chain-of-thought reasoning and action selection for end-to-end driving by interleaving action-proposal tokens and latent world-model tokens that predict action outcomes, yielding faster inference and better trajectories than text-based or non-reasoning baselines.
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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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Safety, Security, and Cognitive Risks in World Models
World models enable efficient AI planning but create risks from adversarial corruption, goal misgeneralization, and human bias, demonstrated via attacks that amplify errors and reduce rewards on models like RSSM and DreamerV3.
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Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Proposes hierarchical MARL framework enforcing safety via constraint manifold at low level with theoretical guarantees and stationary dynamics for stable training and generalization.
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SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
SHAPO adds a sharpness-aware adjustment to policy optimization that reweights gradients to favor conservative behavior in uncertain areas, yielding better safety-performance tradeoffs on continuous control tasks.