PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop

arXiv 2026 23.3 benchmark

TLDR

PhysVista benchmarks VLMs' physical intelligence via a perception-reasoning-assessment loop, revealing gaps in physical reasoning across real and generated videos.

Reasoning

The paper introduces a holistic benchmark with a cognitive loop and includes real-world and AI-generated videos, which are strengths. However, it focuses on evaluating VLMs, not on building world models, so its direct relevance to world-model keywords is limited.

Read-first score

Read-first score 23.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 11.

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=analysis,benchmark,evaluation,experiment

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%

15.7

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: 114.

Keyword Scores

world dynamics prediction

world model

video world model

world simulator

generative world model

interactive world model

model-based reinforcement learning world model

Resources

Paper