Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation

arXiv 2026 24.6 method

TLDR

Evaluates world model planners under sensing degradations, showing stage-wise reliability across representation, prediction, planner preference, and outcomes differs from final task success.

Reasoning

The paper's strength is its paired stage-wise evaluation across 50 tasks, revealing that degradation effects are not uniformly preserved through the pipeline. However, the abstract lacks details about real-world versus simulated settings, baselines, and metrics, limiting assessment of generalizability.

Read-first score

Read-first score 24.6, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 24.

Recency 5%

100

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

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=code

Topical relevance 25%

34.3

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

Methodology quality 15%

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

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: 148.

Keyword Scores

world model

world dynamics prediction

model-based reinforcement learning world model

world simulator

generative world model

interactive world model

video world model

Resources

Paper