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home / papers / 2026 / QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

Paper · 2026 · Songxin Qu, Tai-Ping Sun, Yun-Jie Wang et al.

Summary

LLMs reason well in general, but in quantum mechanics they often produce derivations that look convincing while breaking physical constraints. The authors trace this to two causes: too little verifiable training data, and reward signals that are too coarse or sparse. They address both. First, they build QuantumQA, a corpus of 92,749 quantum-mechanics samples. It grows from textbook seed problems through a Seed-Evolve pipeline, with answer length matched to task difficulty. Each sample is checked by symbolic tools, by an LLM critic, and in a human-in-the-loop audit. Second, they propose the verification-aware reward model (VRM) for RLVR. VRM blends deterministic checks from a scientific execution suite (SES) with LLM-as-a-Judge scores for correctness, physical consistency and instruction following. An adaptive reward fusion (ARF) mechanism weights these signals according to how verifiable each answer is. A VRM-trained 8B model beats SFT baselines and a generic preference reward model, and on some metrics it rivals proprietary systems. This suggests verifiable feedback can partly replace sheer model scale.

Key points

  • QuantumQA: 92,749 samples across five task types: Short Answer, Fill-in-the-Blank, True/False, Multiple Choice and multi-step Problem Solving. The held-out test set has about 4,675 items. Seeds come from Griffiths and Nielsen & Chuang.
  • Hybrid verification: SymPy and QuTiP check math and physical constraints, and DeepSeek-V3 acts as a semantic critic. In the expert audit, a rejection rate above 5% triggers prompt refinement and regeneration.
  • Benchmarks: evaluation covers the QuantumQA test split plus SuperGPQA and PHYSICS.
  • Ablation on Qwen3-8B: VRM beats Skywork RM on every task type, for example Problem Solving 0.680 vs 0.600 and Fill-in-the-Blank exact match 0.578 vs 0.451. ARF outperforms both fixed and heuristic weighting.
  • Stated limitations: the work covers text only and quantum mechanics only, and it lacks exploratory Long-CoT traces. The SES is described as swappable for other sciences.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore180 works · 34,304 citations🎓University of Science and Technology of ChinaHefei · China108 works · 112,698 citations🎓Anhui UniversityHefei · China7 works · 7 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo and Zhao-Yun Chen. 2026. QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1423

@inproceedings{qu2026quantumqa,
  title = {{QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning}},
  author = {Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo and Zhao-Yun Chen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1423},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1423}
}

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