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版本更新 · v2arXiv:2610.16733

TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving

A. Ramesh Chandra, Aaghaz Mahajan, Ainesh Sanyal, Mianqi Wang, Shubhadeep Chakraborty, Sudip Chakraborty, Shailesh Chandrasekharan, Rama Govindarajan 等 35 位作者

原文摘要

We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved by a world expert in about a day or less but would take considerably longer for experts in adjacent specialties. We test the ability of off-the-shelf AI agents to tackle these problems autonomously, without human hints. When given access to tools available in a realistic setting, including the ability to search the literature and write scripts for numerical and symbolic work, the best-performing agent, GPT-6-Astra harnessed in Codex, obtains the final answers with a near-perfect accuracy of 98% at an average cost under USD 3 per problem. On average, agents show only small changes in accuracy when denied web search and access to background research papers, suggesting significant memorization of the literature. These results suggest that state-of-the-art agents can accurately perform concrete calculations required across theoretical physics research. We comment on the broader implications of these findings and discuss possible explanations for the current lack of agent-led breakthroughs in theoretical physics despite their outstanding problem-solving abilities.

hep-th · cond-mat.stat-mech · cond-mat.str-el · cs.AI · gr-qc

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24074

QRecoupling.jl：经典与量子重耦合系数的数值与精确计算

QRecoupling.jl: numerical and exact computation of classical and quantum recoupling coefficients

Seth K. Asante

AI 速读 基于摘要

介绍 Julia 包 QRecoupling，用于数值、精确和符号地计算 Clebsch-Gordan 系数、3j/6j 符号及用户自定义阶乘乘积求和。

cs.MS · gr-qc · math-ph · physics.comp-ph

arXiv ↗PDF ↗
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The Julia package QRecoupling evaluates classical and quantum recoupling coefficients, such as Clebsch-Gordan coefficients and $3j$ and $6j$ symbols, and user-defined sums of factorial products, with numerical, exact and symbolic targets. One compact factorial rule covers classical and deformed evaluations, while its cyclotomic exponents reveal common factors and determine which terms contribute at unitary levels. Adaptive arithmetic resolves strongly cancelling sums, while exact tests in quantum integers distinguish algebraic zeros from small nonzero values without a computer-algebra system. At spin cutoff 500, classical $3j$ and $6j$ symbols are evaluated about 7 and 15 times faster than with WIGXJPF. For the tested quantum deformations, relative errors remain below $4\times10^{-14}$ through spin cutoff 400. Recurrences compute fusion matrices with 401 channels about 100 times faster than loops over their entries. The resulting coefficients support recoupling identities, changes of coupling basis and tensor-network calculations.

新论文 · v1arXiv:2611.05790

Unsupervised Neural Operators for Rotating Relativistic Stars: High-Fidelity Equation of State Inference and Pulsar Spin Evolution at Scale

Anarya Ray

原文摘要

Observed populations of rotating neutron stars~(NSs) with millisecond-order periods are powerful probes of fundamental physics. Mass-radius measurements inform poorly constrained properties of degenerate nuclear matter, whereas high-fidelity population synthesis connects unknowns in NS formation and binary evolution to large radio-timing surveys. However, repeated structure calculations are prohibitive due to the computational cost of solving the highly coupled partial differential equations of axisymmetric general relativistic hydrostatic equilibrium that govern rotating NS interiors. Non-rotating calculations or approximate rotational corrections can bias equation of state inference and limit the fidelity of population synthesis in tracing the evolutionary history of progenitors and propagating equation of state uncertainties. In this \textit{Letter}, we develop physics-informed neural operators that can solve the axisymmetric problem with high accuracy and negligible computational cost. \textit{Our solvers are trained in a fully unsupervised setting, with the training objective informed solely by the partial differential equations, and achieve a $10^3\times$ speed-up with negligible accuracy loss}. These solvers enable scalable EoS inference that self-consistently accounts for rotation and repeated stellar-structure calculations during binary evolution, making spin predictions that account for rotational structure and EoS uncertainties computationally accessible to population synthesis.

astro-ph.HE · cs.AI · cs.LG · gr-qc

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03596

Spacetime Emergence in the Memory of a Learning Agent

Philip A. LeMaitre, T. Rick Perche

原文摘要

We show that the causal structure of spacetime can emerge within the memory of a learning agent performing a quantum information task. We train a model-based reinforcement-learning agent to maximize the quantum mutual information between localized quantum probes interacting with a quantum field in Minkowski spacetime, without directly providing it with information regarding the spacetime metric or causal structure. In learning to accomplish this task, the reinforcement-learning agent constructs an internal predictive model whose planner develops the characteristic null structure of spacetime, allowing us to reconstruct the causal structure of the Minkowski metric with high accuracy. Within this task, spacetime geometry is obtained from an agent's successful predictions and actions, providing a first step toward showing that spacetime concepts emerge from operational quantum experience.

quant-ph · cs.AI · cs.LG · gr-qc

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02084

Black Hole Love Numbers with Physics-Informed Neural Networks

Maria J. Rodriguez, Luis Fernando Temoche, Pavlos Protopapas

原文摘要

We develop and validate a physics-informed neural network (PINN) framework for computing the tidal Love numbers of black holes directly from their perturbation equations. We consider static perturbations of the Kerr black hole, whose exact hypergeometric solutions provide a controlled benchmark for both the numerical solver and the extraction of the physical response. The framework combines exact enforcement of the horizon boundary conditions with an $H^2$-Sobolev loss that incorporates derivatives of the differential-equation residual. The Love number is then obtained by extracting the asymptotic coefficients of the learned solution, without using the analytic connection formula. Across the tested scalar, vector, and tensor perturbations, the method recovers the finite dissipative response with relative errors below $10^{-5}$. Controlled comparisons show that the higher-derivative loss terms improve solution accuracy by orders of magnitude, while independent tests of the extraction procedure distinguish solver errors from errors in recovering the response coefficients. The conservative response, which vanishes analytically, is constrained by the numerical error floor. These results demonstrate that PINNs can accurately compute black-hole tidal response and establish a validated framework for future extensions to frequency-dependent and non-separable perturbation problems.

gr-qc · cs.LG · hep-th · math.NA

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arXiv ↗PDF ↗