Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2510.21219

World Models Should Prioritize the Unification of Physical and Social Dynamics

Xiaoyuan Zhang·Chengdong Ma·Yizhe Huang·Weidong Huang·Siyuan Qi·Song-Chun Zhu·Xue Feng·Yaodong Yang
View original source ↗Read source document ↗Export metadata ↓

Abstract

World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments and social constructs, rendering current models fundamentally incapable of adequately addressing the true complexity of real-world systems where physical and social realities are inextricably intertwined. This position paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development. We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics. Such unification is paramount for AI to robustly navigate complex real-world challenges and achieve more generalizable intelligence. This paper substantiates this imperative by analyzing core impediments to integration, proposing foundational guiding principles (ACE Principles), and outlining a conceptual framework alongside a research roadmap towards truly holistic world models.

Explore related subjects

cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiaoyuan Zhang, Chengdong Ma, Yizhe Huang, Weidong Huang, Siyuan Qi, Song-Chun Zhu, Xue Feng, Yaodong Yang. 2025-10-24. World Models Should Prioritize the Unification of Physical and Social Dynamics. https://arxiv.org/abs/2510.21219

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

An Investigation Into Secondary School Students' Debugging Behaviour in Python

Background and context: Debugging is a significant and often frustrating challenge for beginner programmers. Understanding students' debugging behaviours and strategies can help to identify common difficulties and inform approaches for alleviating these. Currently, there are limited studies of school students' debugging behaviour in a text-based programming language, a medium through which millions are learning to program. Objectives: In this paper, we investigate the debugging behaviour of 12-14-year-old students learning Python through a lesson-long classroom study. Method: We collected program snapshots from 73 students' attempts at a set of Python debugging exercises in an online code editor. Through qualitative content analysis of these snapshots, we developed a granular categorisation of the changes students made when debugging. Findings: A range of debugging behaviours were exhibited by students, many of which were ineffective. Students added errors through small-scale changes, reverted corrective changes, and repeatedly ran identical programs in quick succession. From the results, we identify four barriers to successful and reliable debugging for students learning a text-based programming language: fragile knowledge, a lack of systematicity and reflection, the syntax barrier, and dynamics of emotions and attitudes. Implications: This paper highlights some of the difficulties that secondary school students have when debugging in Python and the challenges of analysing program snapshots through manual inspection. We recommend that school teachers explicitly teach a systematic approach to debugging and discourage the use of ineffective debugging behaviours, and that programming environments should contain features that facilitate successful debugging.

cs.CY↗
arXiv2026

Printed but not benchmarkable: most building-decarbonisation disclosure cannot be matched to the pathways that stranding regulation assumes

Cities are beginning to enforce carbon limits on existing buildings. Science-based decarbonisation pathways set those limits one asset type and one jurisdiction at a time. Owners, however, report for the whole firm. We measure what that mismatch costs on two sets of public corporate reports: a census of 502 reports from the 119 listed built-environment firms with a collected report inside a 2,246-firm panel (2003-2023), and 519 real-estate reports from 101 firms (2007-2024). BeDA, a multimodal language-model tool whose reliability we test first, read them. Running the pathway frameworks' own entry tests over published disclosure: 16.5% of census reports (43.7% of real-estate reports) print an operational carbon intensity per square metre; 6.6% (25.0%) can be matched to a pathway for their property type in a covered jurisdiction; and only 5.0% (16.4%) disclose the floor area they divided by. Of the failures at the pathway test, 82-84% follow from reports lumping the portfolio together and 16-18% from a missing curve in the pathway library. The obstacle is the reporting unit, not missing data. The rate is roughly twice as high for European as for US listings (65-71% versus 35% in listed real estate). We also show that a US portfolio's carbon verdict cannot be worked out from disclosure at all. Within one climate zone, the pathway's carbon limit varies by up to 2.79-fold with the electricity subregion, which no report names; its energy limit does not move. Extraction is checked against the source PDFs (97.3% of extracted intensities appear verbatim) and repeats on a second extractor (kappa = 0.97). Recall of the non-disclosing class was 95.1% in a blinded hand audit of 122 reports. The fix follows from the measurement: split intensity by asset type and jurisdiction, and report floor area.

cs.CY↗
arXiv2026

Incipit: Axiom-Grounded Scaffolding for Human-AI Literary Creation

Large language models can produce fluent prose from short prompts, but a direct interaction gives writers little access to the assumptions that shape a long narrative. We present Incipit, an implemented research prototype that inserts an explicit planning layer between writer intent and generated prose. The layer is grounded in literary axioms, which are curated and reusable propositions about human experience and narrative craft. The prototype connects a knowledge base of 1455 axioms and 472 typed relationships to a five round direction dialogue, a retrieval and selection pipeline, and a three level blueprint covering creative premises, story beats, character arcs, and chapter outlines. Writers can inspect and edit the resulting structures before using them as context for scene generation. Additional modules support real event abstraction and five dimensional diagnostic feedback. We describe the design rationale, data flow, implementation boundaries, and a worked design example. Because no controlled user study or independently rated output study has yet been completed, we do not claim that the system improves literary quality. Instead, we outline a future preregistered comparison designed to distinguish the contribution of axiom grounding from that of hierarchical planning. The paper contributes a concrete architecture for making literary knowledge an inspectable coordination object in human AI writing.

cs.CY↗