CoDA: Co-Evolution of Data Engines and Action Policies

arXiv 2026 18.4 method

TLDR

CoDA co-evolves simulation data engines and robot action policies using shared memory and vision-language diagnoses, improving out-of-distribution manipulation success on benchmarks.

Reasoning

The paper presents a novel framework for co-evolving generative simulation and policy learning, with strong empirical results across manipulation benchmarks. However, the abstract does not explicitly frame the work as a world model, limiting relevance to world-model keywords.

Read-first score

Read-first score 18.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 10.

Recency 5%

100

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

Methodology quality 15%

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

Reproducibility 15%

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

Topical relevance 25%

14.3

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

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: 45.

Keyword Scores

world simulator

world model

generative world model

interactive world model

world dynamics prediction

model-based reinforcement learning world model

video world model

Resources

Paper