IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

arXiv 2026 24 benchmark

TLDR

Introduces IRIS, a benchmark of 240 real-world videos for physical parameter estimation and equation identification, with standardized evaluation protocol and baselines.

Reasoning

The paper provides a valuable real-world benchmark with controlled conditions and multiple baselines, addressing a gap in the field. However, its focus on inverse parameter estimation and equation identification is only tangentially related to world models, limiting relevance to the specified keywords.

Read-first score

Read-first score 24, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 2.

Recency 6%

100

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

Methodology quality 18%

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

Reproducibility 18%

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

Topical relevance 29%

2.9

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

Citation impact 18%

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

Citation velocity 12%

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 91.

Keyword Scores

video world model

world dynamics prediction

world model

world simulator

generative world model

interactive world model

model-based reinforcement learning world model

Resources

Paper