Hugging Face Trending Papers

World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning

Read the original on Hugging Face Trending Papers →

World models and multimodal large language models (MLLMs) provide complementary capabilities for predicting future outcomes from static visual observations. World models can generate concrete visual rollouts of possible futures, while MLLMs can reason abstractly over questions, goals, and rules.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Computation and Language
Sep 1

World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning

The paper introduces a framework that combines world models, which generate concrete visual rollouts of possible futures, with multimodal large language models (MLLMs) that perform abstract reasoning. It proposes a controlled concrete reasoning approach and a new training method called Privileged‑Future On‑Policy Self‑Distillation (PF‑OPSD), which uses ground‑truth future videos as privileged teacher context during training while the student model never sees true futures at test time. Experiments on two human‑verified benchmarks, VRQABench and OpenWorldQA, show that PF‑OPSD improves performance by about 10–11% over baselines and enhances robustness to noisy or conflicting rollouts.

By Yucheng Zhou, Wei Tao, Yiwen Guo, Jianbing Shen