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Measuring (a Sufficient) World Model in LLMs: A Variance Decomposition Framework

arXiv 25.6 2025 35.9 method

TLDR

Introduces a variance decomposition framework to measure LLMs' intent comprehension by evaluating output consistency across semantically equivalent prompts.

Reasoning

The paper presents a formal, principled approach to assessing intent understanding, with empirical results on LLaMA and Gemma models. However, it does not address the listed world model keywords, and the abstract lacks explicit real-world experiments or datasets.

Read-first score

Read-first score 35.9, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 2.

Recency 8%
86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 25%
80

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

Reproducibility 25%
30

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

Topical relevance 42%
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

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 285.

Keyword Scores

world model
2
world simulator
0
generative world model
0
interactive world model
0
video world model
0
world dynamics prediction
0
model-based reinforcement learning world model
0

Deep Analysis

Innovations

  • Introduces a formal variance decomposition framework to assess intent comprehension in LLMs by separating variability due to user intent, user articulation, and model uncertainty.
  • Proposes moving beyond accuracy-only benchmarks toward semantic diagnostics that directly evaluate whether models understand user intent.

Methodology

The framework decomposes the variance of model responses across semantically equivalent prompts into three components: variability due to user intent, user articulation, and model uncertainty. The evaluation is applied across diverse domains using five models from the LLaMA and Gemma families, measuring the proportion of variance attributed to intent as an indicator of robust understanding.

Key Results

Larger models typically assign a greater share of variance to intent, indicating stronger comprehension, but the gains are uneven and often modest with increasing model size.

Limitations

  • Evaluation is limited to five models from only two model families (LLaMA and Gemma).
  • The observed improvements with model size are inconsistent and modest, suggesting scaling alone may not reliably enhance intent comprehension.
  • The framework's effectiveness depends on the construction of semantically equivalent prompt sets, which may not capture all nuances of user intent.

Resources

Paper

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