Continuous Control

Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose EmoVec, a lightweight framework for con…

Continuous Control

Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control

2026-08-21 · Xu Yang, Yiqin Yang, Qianchuan Zhao arxiv

World models for continuous control are commonly trained for a fixed physical system and can degrade when known morphology parameters such as link lengths, masses, damping, and actuation change. Existing approaches often…

Continuous Control

Orthogonal JEPA: Factorized Predictive States for Latent World Models

2026-08-20 · Taoyong Cui, Pheng Ann Heng, Wanli Ouyang arxiv

World models construct latent states that support prediction, planning, and reasoning about an underlying system. Joint-embedding predictive architectures (JEPAs) offer a direct way to learn such states by predicting tar…

Continuous Control

To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization

2026-08-19 · Taehyung Kim, Jongeun Choi arxiv

Learning a reward model from human feedback and optimizing a policy against it is one approach to aligning AI systems with individual users. From a fairness perspective, existing work improves such alignment by developin…

Continuous Control

An Omitted Mode Is a Rare Rule: The Sampling-Verification Danger Law in Continuous Code World Models

2026-08-18 · Javier Aguilar Martín arxiv

In the Code World Model paradigm an LLM synthesizes an executable world model that a classical planner searches, and the model is accepted when it reproduces sampled transitions. We ask what that acceptance certifies in …

Continuous Control

Observation-Grounded Self-Predictive Reinforcement Learning for Visual Continuous Control

2026-08-06 · Xinwei Liu, Junyuan Liang, Jianting Zhang, Wuhui Chen arxiv

Sample-efficient policy learning from pixels is a long-standing challenge in reinforcement learning (RL). Recent dynamics-based representation learning methods have significantly improved the sample efficiency of model-f…

Representation Learning Reinforcement Learning Continuous Control

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