mlsanityData to 7 Oct 2026

Advances in apparent conceptual physics reasoning in GPT-4

arXiv 2303.17012 ↗First posted 29 Mar 2023physics.ed-ph cs.AIcited by 12

On the map: LLM reasoning → Spatial and embodied reasoning

Abstract (arXiv)

ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newtonian Mechanics on the Force Concept Inventory. This work replicates those results and also demonstrates that the latest version, GPT-4, has reached a much higher mark in the latter context. Indeed, its responses come quite close to perfectly demonstrating expert-level competence, with a few very notable exceptions and limitations. We briefly comment on the implications of this for the future of physics education and pedagogy.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 7 of its 35 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

7 of 35 references.

References · 7

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  6. 2022GPT Takes the Bar Examcited by 65
  7. 2023AI and the FCI: Can ChatGPT Project an Understanding of Introductory Physics?cited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 2; 2026: 1.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems
  2. 2025PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Level
  3. 2025UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models
  4. 2025Symbolic or Numerical? Understanding Physics Problem Solving in Reasoning LLMs
  5. 2025PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems
  6. 2026Decoupled Physical Modeling and Execution for Physics Reasoning
  7. 2025ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems
  8. 2025Scaling Physical Reasoning with the PHYSICS Dataset