Named for Wang Yangming’s Longchang awakening (龙场悟道, 1508).

LongChang

Can LLMs Uncover Hidden Physical Laws through Free Exploration?

Zhen Yang1 Wen Wang3 Xander Xu2* Ying-Cong Chen1,4*

1HKUST(GZ) 2Alibaba Group 3ZJU 4HKUST

*Co-corresponding authors

Abstract

Scientific discovery often begins with an unexpected observation that prompts a new research question. In a world with altered physical laws, can large language models (LLMs) notice unexpected behavior, such as heavier objects falling more slowly, and discover the law through experiments without being told what to investigate?

To evaluate this ability, we introduce LongChang, an automated framework that creates simulated worlds with altered physics, where one or more laws differ from ordinary physics. The evaluated LLMs act as explorers, manipulating objects and building tools to investigate their own questions without research tasks or hints about these laws.

Separate LLM agents generate and review scenes, review tool designs, and evaluate reports for correctness and experimental support using the altered physical laws and exploration records. These records guide scene evolution for further exploration, allowing the full cycle to run without human intervention.

Among 12 LLMs, GPT-6-Astra achieves a discovery rate of 16.7%, while the other 11 score 0%. The exploration records reveal uninvestigated anomalies and only partial identification of the altered physical laws.

Figure 1: a glass room with a pendulum, balance, red ball and blue ball
Drag an object up to lift; release to drop.

Explore the Worlds

READY TO EXPLORE
INTERACTIVE LAW ILLUSTRATION
0.0 / 8.0 s

Normal control

The hidden rule

THE DEMONSTRATION

WHAT TO WATCH

Full rule

01 / 15

Rule illustration

Framework

Click a node
WorkflowFeedbackHidden law

Build the world.

Define altered physics, then test and review the scene.

IN
Scene design
OUT
Validated world + hidden law

Construct

Explore

Evaluate

Main Results

Judge: GPT-6.1-Sol · 3 runs per model per scene.All records ↓Summaries ↓

Discovery in Practice

Comparisons

GPT-6-Astra

Case studies

The red ball moves on its own. What should be tested next?

In separate runs of the same scene, both models observe the red ball start moving again without a new push.

GPT-6-Astra
Gemini-3.7-Flash

Astra chooses to investigate why the red ball moved.The red ball starts moving while Astra holds the blue ball still. Astra sets aside its planned collision test to investigate what caused the motion.

Gemini observes the anomaly but keeps its plan.Its log also reports the red ball moving again, though movement at the wall is hard to see. Gemini proceeds with its planned tests.

What this comparison shows: Both models observe unexpected motion. Astra turns it into a research question and pursues it with experiments; Gemini continues its planned tests and leaves it unexplained.

Full results & setup

Scene Evolution

MotionForce

Generated from Scene 15 · Northward force

Repulsion with a pause

Replace red’s northward force with mutual red–blue repulsion; keep the 10 s pause after a push or throw.

Rule illustration

Repulsion pausedResumes in 10.0 s
0.0 s

Force timing; positions are schematic.

BibTeX

@article{yang2026longchang,
  title={Can LLMs Uncover Hidden Physical Laws through Free Exploration?},
  author={Yang, Zhen and Wang, Wen and Xu, Xander and Chen, Ying-Cong},
  journal={arXiv preprint arXiv:2610.37649},
  year={2026}
}

Original record

Recorded example · GPT-6-Astra

0.0 counts

LongChang.MOTION ATLAS / FOUR LAWS TO DISCOVER
SCENE 07LAW ILLUSTRATION

00:00 / 00:48
Idealized illustrations of the published laws. Recorded experiments are available on the project page.LongChang · Anonymous research