Memory as Plans: World-Action Modeling with Memory-Grounded Planning

arXiv 2026 15.1 method

TLDR

Introduces MaP-WAM, a memory-as-plans framework for robotic manipulation using episodic memory and World-Action-Progress model for adaptive planning and execution.

Reasoning

Strengths include addressing non-Markovian tasks with fixed context length and memory compression. Weaknesses: the abstract is truncated and offers no explicit links to world model literature; real-world validation is only hinted via RMBench.

Read-first score

Read-first score 15.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 5.

Recency 5%

100

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

Reproducibility 15%

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

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=none

Topical relevance 25%

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

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

Frontier

Rank sensitivity

Stability: volatile; rank range: 17.

Keyword Scores

world model

world dynamics prediction

world simulator

generative world model

interactive world model

video world model

model-based reinforcement learning world model

Resources

Paper