·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Retrieval Augmented Generation Compositionality1h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents1h◆TNODEV: Toolbox for Neural ODE Verification1h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1h◆On the Expressive Power and Limitations of Multi-Layer SSMs1h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale1h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression1h◆A Generalisation Signal Need Not Be a Model-Selection Signal1h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering1h◆ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning1h◆Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?1h◆AnnoCaseLaw: Legal Annotation Benchmark of Civil Negligence1h◆Improved Training of Strong Stochastic Flow Maps1h◆Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation1h◆HyperDrone: Simulation-to-Reality Transfer at Millions of Frames per Second1h◆Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection1h◆Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models1h◆Conditional Optimal Bridge for Riemannian Activation Steering1h◆RL-IF: Tracking Non-local Training Data Influence for Online Reinforcement Learning1h◆Unintended Side Effects of Value Training in LLMs1h◆Retrieval Augmented Generation Compositionality1h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents1h◆TNODEV: Toolbox for Neural ODE Verification1h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1h◆On the Expressive Power and Limitations of Multi-Layer SSMs1h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale1h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression1h◆A Generalisation Signal Need Not Be a Model-Selection Signal1h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering1h◆ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning1h◆Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?1h◆AnnoCaseLaw: Legal Annotation Benchmark of Civil Negligence1h◆Improved Training of Strong Stochastic Flow Maps1h◆Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation1h◆HyperDrone: Simulation-to-Reality Transfer at Millions of Frames per Second1h◆Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection1h◆Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models1h◆Conditional Optimal Bridge for Riemannian Activation Steering1h◆RL-IF: Tracking Non-local Training Data Influence for Online Reinforcement Learning1h◆Unintended Side Effects of Value Training in LLMs1h◆
News/AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites
arxiv
PublishedMay 11, 2026 at 4:00 AM
▲bullish

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

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

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

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    AGWM (Affordance-Grounded World Model)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#model-based-learning#world-models#affordance
Mentioned companies
01
Amazon

No replies yet. Be first.

Mentioned models
01
  • 01
    AGWM (Affordance-Grounded World Model)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#model-based-learning#world-models#affordance
Mentioned companies
01
Amazon

Related coverage

More from ARXIV
arxivRetrieval Augmented Generation Compositionality1harxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents1harxivTNODEV: Toolbox for Neural ODE Verification1harxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1h
The Bubble Brief
MON · WED · FRI

Read model-based-learning insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews