TS-WAM: Temporal Latent Actions and World-Action Coupling for Vision-Language Robot Control

arXiv 2026 25.2 method, application

TLDR

TS-WAM introduces a lightweight teacher-student world-action model that learns temporal latent actions and predicts future visual features to improve vision-language robot control, validated across benchmarks and real robots.

Reasoning

The paper's strengths include self-supervised temporal learning from heterogeneous data, an end-to-end architecture, and validation on both simulation benchmarks and real-world robots. Weaknesses are that the abstract provides limited detail on ablations or limitations, and the claimed world modeling is mainly future visual feature prediction rather than a full generative world simulator.

Read-first score

Read-first score 25.2, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 29.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Topical relevance 25%

41.4

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=experiment

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 199.

Keyword Scores

world dynamics prediction

world model

interactive world model

generative world model

world simulator

video world model

model-based reinforcement learning world model

Resources

Paper