arxivJul 21bullish

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607.16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments. Hand-curated environments with fixed task and reward difficulties become ineffective signals as model performance improves, and sparse rewa

LLMDLF5 models · +2 #reinforcement-learning #world-models

arxivJul 18bullish

DriftWorld: Fast World Modeling through Drifting

arXiv:2607.15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes eac

DR1 model #robotics #world-models #diffusion Read on arxiv →

arxivJul 3bullish

Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents

arXiv:2606.27806v2 Announce Type: replace Abstract: World models for language agents come in two useful forms. An agent-based world model calls an LLM API and reasons flexibly in language, but its errors appear as hallucinated state changes that are hard to score with ordinary regression losses. A p

GP1 model #planning #language-agents #world-models Read on arxiv →

arxivJun 26

Hallucination in World Models is Predictable and Preventable

arXiv:2606.27326v1 Announce Type: new Abstract: Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in

#world-models #machine-learning #computer-vision Read on arxiv →

arxivJun 25bullish

ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning

arXiv:2501.14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert demonstrations, which are expensive to collect. Additionally, they are not explicitly trained to understand

AC1 model #imitation-learning #self-supervised-learning #world-models Read on arxiv →

mit-tech-reviewMay 21bullish

Roundtables: Can AI Learn to Understand the World?

Listen to the session or watch below AI companies want to build systems that understand the external world and overcome the limitations of LLMs. Recent developments have brought world models to the forefront of the AI discussion. Watch a conversation with editor in chief Mat Honan, senior AI editor

#world-models #llms #ai-development Read on mit-tech-review →

arxivMay 11bullish

AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites

arXiv:2605.06841v1 Announce Type: new Abstract: In model-based learning, the agent learns behaviors by simulating trajectories based on world model predictions. Standard world models typically learn a stationary transition function that maps states and actions to next states, when an action and an o

AG1 model #model-based-learning #world-models #affordance Read on arxiv →

arxivApr 13

Toward World Models for Epidemiology

arXiv:2604.09519v1 Announce Type: new Abstract: World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and underdeveloped setting

#epidemiology #machine-learning #world-models Read on arxiv →