Author

Todd Y. Zhou

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#artificial intelligence Preprint Sep 2026

Learning Counterfactual World Models for Embodied Reasoning under Partial Observability

This work introduces Counterfactual Latent World Models (CLWM), which combine a recurrent belief-state encoder, action-conditioned latent dynamics, and a contrastive counterfactual objective that separates futures induced by distinct interventions even when their observations look alike.

Todd Y. Zhou, Daniella Zhang · 0 citations

Jul 2026

When RLVR Shrinks the Reasoning Boundary: Diagnosing Pass@k Inversion

Per-Problem Base Anchoring is a deliberately simple proof-of-concept that improves both high-budget coverage over matched GRPO and ECCV-relevant vision-language agents when repeated visual, spatial, or chart-reasoning attempts are checked by external tools or verifiers.

Todd Y. Zhou · arXiv.org · 0 citations