Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv 26.1 2026 64.6 method, system

TLDR

WorldMind aligns LLM agents with physical laws by constructing a symbolic knowledge repository from environmental feedback, reducing physical hallucinations.

Reasoning

The paper introduces a novel framework that uses symbolic knowledge to address physical hallucinations without heavy retraining, showing strong cross-model transfer. However, experiments are limited to simulated benchmarks, and scalability to complex real-world dynamics remains unclear.

Read-first score

Read-first score 64.6, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 45.

Recency 8%

100

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

Methodology quality 25%

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

Topical relevance 42%

64.3

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 25%

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

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 177.

Keyword Scores

world model

world dynamics prediction

interactive world model

world simulator

model-based reinforcement learning world model

generative world model

video world model

Deep Analysis

Innovations

WorldMind framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback
Unification of Process Experience (enforcing physical feasibility via prediction errors) and Goal Experience (guiding task optimality through successful trajectories) for alignment
Demonstrated cross-model and cross-environment transferability without continuous retraining

Methodology

WorldMind constructs a symbolic World Knowledge Repository by synthesizing environmental feedback. It unifies Process Experience, which enforces physical feasibility via prediction errors, and Goal Experience, which guides task optimality through successful trajectories. The framework is evaluated on EB-ALFRED and EB-Habitat benchmarks against baselines.

Key Results

WorldMind achieves superior performance compared to baselines on EB-ALFRED and EB-Habitat, with remarkable cross-model and cross-environment transferability.

Resources

Paper