Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence

arXiv 2026 23.9 method

TLDR

Introduces SIS-Bench, a benchmark evaluating self-awareness and spatial cognition in UAVs using real-world videos and multimodal LLMs.

Reasoning

The paper addresses a clear gap in UAV benchmarks by focusing on self-awareness alongside spatial cognition, using real-world data and expert-verified QA pairs. However, it does not directly engage with world model concepts, limiting relevance to the provided keywords.

Read-first score

Read-first score 23.9, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 5.

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=benchmark,evaluation,experiment,result

Reproducibility 18%

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

Topical relevance 29%

7.1

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: 45.

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