ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

arXiv 2026 25.7 method

TLDR

Introduces Combodied Agents, a human-centric AI paradigm modeling individual human-state trajectories via Personal World Models and closed-loop interventions across digital, physical, and human services.

Reasoning

The paper presents a novel conceptual framework that unifies digital and embodied agents around human-state modeling, with Personal World Models as a central component. However, the abstract provides no empirical evaluation, datasets, or implementation details, so the claims remain unsupported by visible evidence.

Read-first score

Read-first score 25.7, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 22.

Recency 5%

100

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

Methodology quality 15%

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

Topical relevance 25%

31.4

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=none

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: 144.

Keyword Scores

world model

world dynamics prediction

interactive world model

world simulator

model-based reinforcement learning world model

generative world model

video world model

Resources

Paper