One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability

arXiv 2026 52.1 method

TLDR

Proposes WorldModelLens, a capability-typed interface for interpretability of diverse world models, unifying methods across architectures.

Reasoning

The paper identifies a genuine problem of fragmented interpretability tooling for world models and offers a principled interface solution. However, the abstract lacks empirical validation or real-world experiments, making it unclear how effective the proposed substrate is in practice.

Read-first score

Read-first score 52.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 44.

Recency 5%

100

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

Citation impact 15%

95.6

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.95596511

Methodology quality 15%

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

Topical relevance 25%

62.9

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

Reproducibility 15%

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

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation velocity 10%

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

Field roles

FoundationFrontierBridgeMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 318.

Keyword Scores

world model

model-based reinforcement learning world model

interactive world model

world dynamics prediction

generative world model

video world model

world simulator

Deep Analysis

Innovations

Capability-typed interface that unifies interpretability across diverse world model architectures (latent recurrent, token-based, joint-embedding).
WorldModelLens substrate with four required methods and optional heads, allowing RL and self-supervised models to be first-class without imitating each other.
Single hook and cache layer exposing time-indexed activations, imagination rollouts, and intervention replay over the interface.

Methodology

WorldModelLens defines a typed interface with four required methods (encode, transition, initial state, sample) and optional heads (decode, reward, continue, actor, critic) declared via a capability descriptor. A hook and cache layer is built over this interface to expose time-indexed activations, imagination rollouts, and intervention replay. This design allows interpretability methods to be implemented once and applied across different world model architectures.

Key Results

No experimental results are mentioned in the abstract; the paper presents the interface design and its intended benefits.

Resources

Paper