Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage

arXiv 2026 43.3 method, application

TLDR

Underwater C3-JEPA is a control-conditioned object-centric multi-view latent world model for ROV salvage, predicting task-object state for MPC and behavior-agent training.

Reasoning

The paper's strengths include a novel object-centric cross-view predictive architecture and real-world validation, with low annotation cost via weak binding. Weaknesses are limited visibility into quantitative comparisons, baseline details, and the fact that the latent-state focus may not fully justify the 'world model' framing.

Read-first score

Read-first score 43.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 47.

Methodology quality 15%

100

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

Recency 5%

100

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

Topical relevance 25%

67.1

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

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 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

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 751.

Keyword Scores

world model

world dynamics prediction

interactive world model

model-based reinforcement learning world model

video world model

world simulator

generative world model

Deep Analysis

Innovations

Object-centric multi-view predictive world model (Underwater C3-JEPA) for near-field heavy-load underwater ROV salvage that predicts task-object state evolution in latent space without contact sensors.
Cross-view, control-conditioned, context-extended architecture that encodes multi-camera observations into task-object and context tokens, fuses cross-camera evidence via held-out-view attention, and directly predicts future states conditioned on control.
Weak binding mechanism to anchor target and gripper at low annotation cost.
SIGReg to sharpen geometric representation.
Predictive interface supporting model-predictive-control candidate evaluation and imagined-rollout behavior-agent training.

Methodology

The model encodes synchronized multi-view RGB observations and vehicle control signals into task-object and context tokens, fuses cross-camera evidence using held-out-view attention, and directly predicts future latent states conditioned on control. Weak binding anchors target and gripper at low annotation cost, while SIGReg sharpens geometric representations. Evaluation compares downstream probe transfer against a reconstruction-free latent baseline and validates on real underwater video using withheld-camera object-state recovery and persistence benchmarks.

Key Results

The learned representation transfers substantially more task-relevant information to downstream probes than a reconstruction-free latent baseline while keeping the predictor lightweight. Validation on real underwater video shows the same architecture recovers a withheld camera's object state and stays ahead of persistence, indicating the recipe transfers beyond simulation.

Resources

Paper