[2607.02329] Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics

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Computer Science > Artificial Intelligence

arXiv:2607.02329 (cs)

[Submitted on 2 Jul 2026]

Title:Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics

Authors:Haonan Huang<br>View a PDF of the paper titled Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics, by Haonan Huang

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Abstract:Autonomous-research agents have demonstrated end-to-end LLM automation in machine-learning sandboxes where execution provides calibration. Frontier physical science differs categorically: physical reasoning underlies every methodology choice, toolchains are often underdocumented, and calibration must come from external literature anchors - which unscaffolded agents cite but do not confront, hallucinating plausible, unverifiable results from internal priors. We present a pipeline that runs end-to-end from a corpus of 11,083 recent condensed-matter physics arXiv papers to a publication-grade manuscript with three substantive physics findings (here on altermagnetic piezomagnetism): the agent autonomously conceives a research direction by mapping the corpus, calibrates methodology by reproducing published references, conducts novel first-principles computations, and writes the manuscript - grounded in literature throughout, across 47 fresh-context sessions in six phases sharing only on-disk state, with 2,162 literature-consultation events. Fault tolerance emerges from redundancy: fresh-context isolation, distributed grounding, and adversarial review catch what any single session misses; pre- and post-pilot stages are fully autonomous, and pilot requires bounded human intervention only at reproduction failures - operational knowledge curation, not scientific direction. Two paired failure modes - a pre-architecture baseline and a no-pilot ablation - isolate structurally enforced numerical confrontation at calibration checkpoints as the operative grounding mechanism. The primitives, characterized failure modes, and quantified intervention pattern lay a foundation for autonomous research in high-stakes scientific domains beyond computational physics.

Comments:<br>39 pages, 5 figures. Accepted at the ICML 2026 AI for Science Workshop (this https URL). Includes the pipeline-generated companion physics manuscript as an appendix. Data and scaffolding archive: this https URL

Subjects:

Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Computational Physics (physics.comp-ph)

Cite as:<br>arXiv:2607.02329 [cs.AI]

(or<br>arXiv:2607.02329v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.02329

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Haonan Huang [view email]<br>[v1]<br>Thu, 2 Jul 2026 15:35:41 UTC (1,076 KB)

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