Acting Through Registers

arXiv 2026 24.5 method, application

TLDR

Identifies spatial outliers in World Action Model representations and introduces RegisterWAM, adding learnable registers with flow-matching supervision to improve robot manipulation in simulation and real world.

Reasoning

The paper offers a clear analysis of representation anomalies in world action models and a novel architectural fix with strong empirical validation across multiple benchmarks and real-world tasks. However, the abstract provides limited detail on ablations, generalization limits, or comparisons to prior methods, which weakens the depth of the contribution as presented.

Read-first score

Read-first score 24.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 27.

Recency 5%

100

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

Topical relevance 25%

38.6

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=result

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: 195.

Keyword Scores

world model

generative world model

interactive world model

world simulator

world dynamics prediction

video world model

model-based reinforcement learning world model

Resources

Paper