Papers

Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors, particularly superconducting devices whose sparse fixed coupling makes routing a central compilation cost. Routes that appear efficient by standard overhead metrics such as SWAP count, routed two-qubit count, and depth can still lose fidelity when they pass through poorly calibrated couplers. We study a calibration-aware graph reinforcement-learning router that uses same-day calibration data from superconducting IBM Heron r2 processors to choose hardware-edge SWAPs. We train the policy with proximal policy optimization and evaluate it with exact simulated fidelity across nine Munich Quantum Toolkit (MQT) Bench circuits and three calibration snapshots. Across these evaluations, pooled mean exact fidelity is 0.727, compared with 0.440 for SWAP-based bidirectional heuristic search (SABRE)-best20 and 0.481 for target-aware SABRE. We observe that fidelity gains come with higher routed two-qubit counts and are concentrated in 5 qubit and 8 qubit circuit families; under the fixed tree action graph, all 10 qubit families favor SABRE-best20. Overall, our results show that calibration-aware learned routing can improve fidelity beyond gate-count-driven compilation, by roughly 0.25 to 0.29 in absolute mean fidelity over the SABRE-family baselines.

The Jaynes-Cummings (JC) Hamiltonian is a paradigmatic model of light-matter interaction and, more generally, qubit-boson interactions, widely used across atomic, optical, and superconducting qubit platforms. In the multi-qubit setting, where n qubits are identically coupled to a single boson mode, this interaction is known as the Tavis-Cummings (TC) Hamiltonian. The structure of the TC model is usually understood in terms of two standard symmetries: permutation invariance of the qubits and a U(1) symmetry associated with conservation of the total excitation number. Here we identify an additional, independent "accidental" symmetry of the TC Hamiltonian and construct the corresponding conserved observable. We show that, for n>2 qubits, this symmetry imposes strong constraints on the realizable unitary transformations. These constraints persist in the presence of the global $J_z$ Hamiltonian, but are removed by adding $J_z^2$, even though $J_z^2$ preserves both permutation invariance and the U(1) symmetry. Finally, we explain the origin of this previously unnoticed symmetry using Schwinger's boson representation of angular momentum. These restrictions have important implications for controllability of the TC system and for its applications to quantum computing, which are investigated further in a companion paper.

Machine-learning interatomic potentials (MLIPs) have become powerful tools for material simulations. Many MLIPs are trained based on density functional theory (DFT) datasets generated with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. Using a PBE-based MLIP for HfO2, we identify a previously unreported low-energy I41/amd structure, which is predicted to be more stable than the well-known ground-state structure, the monoclinic P21/c structure. Since experiments show clearly that HfO2 takes the P21/c structure as the ground state, this is obviously a wrong prediction. Unfortunately, the same prediction is also made by widely used PBE-based foundation models such as NequIP-OAM-L and MatterSim-v1-5M. Comparisons among different DFT functionals show that this error originates from the PBE functional, which overstabilizes low-density structures containing sixfold Hf-O octahedral units, such as the I41/amd and Pbcn phases. The error also affects the calculated energy landscapes and barrier heights along ferroelectric HfO2 polarization switching paths when there are large lattice relaxations. Fortunately, the error can be largely suppressed by other functionals such as PBEsol and local density approximation. Our study serves as a warning about the impact of errors in exchange-correlation functional approximations on the reliability of MLIP simulations of crystal structures and phase transitions.

Short-term load forecasting is essential for reliable energy management, but practical deployment on edge devices requires models that remain accurate under limited memory, finite measurement budgets, and hardware noise. This work proposes a hardware-efficient Quantum Reservoir Computing (QRC) framework for energy load forecasting, where a fixed quantum reservoir transforms temporal input windows into high-dimensional features and only a classical Elastic Net readout is trained. To reduce deployment cost, the trained readout is compressed using post-training fixed-point quantization at bit widths from 8 to 2 bits. The framework is evaluated on the Tetouan and Spain energy load datasets under exact statevector simulation, 512-shot finite sampling, and realistic hardware-noise models from IBM FakeTorino and IBM FakeMarrakesh. Results show that 6-bit readout precision preserves full-precision forecasting performance while reducing readout memory by 81.2%. Below this point, degradation becomes dataset dependent, with Tetouan showing stronger sensitivity and Spain degrading more gradually. Hardware-noise validation further shows that the trained readout transfers to noisy reservoir states without retraining. These findings support quantized QRC as a resource-aware forecasting approach for near-term quantum time-series applications.

In this paper, we introduce and prove a set of rank recurrence relations for $q$-Whittaker and Macdonald operators. We also derive an explicit expression for the $k$-th power of the $q$-Whittaker operators in terms of the $q$-deformed binomial probability distribution, and we express the rank recurrence relations for the Macdonald operators in terms of the Cauchy determinant.

Proofreading is generally thought to improve accuracy at the expense of speed. We show that this trade-off can be reversed in stochastic processes with long-lived stalled states. Using a non-Markovian renewal framework, we derive exact expressions for the error rate and completion time under proofreading for arbitrary stall-time distributions. Our analysis reveals that fluctuations in stall durations, rather than their mean alone, determine whether proofreading can simultaneously increase speed and accuracy. In the limit of strong stalling, this regime emerges when the coefficient of variation of the stall time exceeds a threshold set by the intrinsic error rate. These results provide a general criterion for proofreading in systems ranging from self-assembly and polymer replication to immune recognition and other nonequilibrium information-processing systems.

A central challenge in nonlinear spectroscopy is the order-selective readout of weak higher-order responses that spectrally overlap with dominant lower-order signals. This bottleneck is particularly severe in two-dimensional (2D) spectroscopy, where extending conventional phase-cycling schemes to higher orders rapidly increases measurement and analysis complexity. Here we introduce a computation-assisted strategy that combines rotating-frame acquisition with a frame-shift tracking algorithm to separate signals by their frame-dependent spectral shifts. In a rubidium vapor experiment, we use this approach to isolate a 7th-order nonlinear contribution from coexisting 3rd-order components, enabling direct access to higher-order quantum-coherence dynamics without sacrificing operation at comparatively high pulse intensities. The method is broadly compatible with multidimensional spectroscopy platforms and provides a practical route to probing many-body and collective ultrafast dynamics beyond third order.

Strong lensing of massive black hole binaries can produce multiple gravitational-wave images with different magnifications and arrival times. LISA signals remain in band for months to years, allowing multiple lensed images to overlap during the inspiral stage and generate beat patterns. A singular isothermal sphere lens model is adopted to describe the lensing configuration, and two-image beat waveforms are constructed from massive black hole binary signals. To isolate the beat pattern itself, waveform mismatch is evaluated only during the overlapping inspiral stage before the coalescence of the first image, excluding contributions from the delayed merger peak of the second image. Using the HS-nod-SN (B+20) strong-lensing population, the occurrence rate of identifiable beat events is estimated, and Bayesian parameter estimation is performed with a beat template. Beat patterns are most readily identified when the lensing time delay is short and the delayed image has a relatively large magnification. Among 196 detectable two-image lensed events, 92 satisfy the temporal-overlap condition and 14 satisfy the beat-identification criterion, corresponding to an identifiable beat fraction of about 7%. Posterior inference shows that the beat template can recover the lensing time delay and magnification parameters for a representative beat event. These results indicate that lensed beat patterns constitute a distinguishable subset of strongly lensed LISA events and provide a unique observational signature of strong lensing in the LISA band.

We study the capability of future space-based gravitational-wave (GW) detectors to constrain supermassive black hole (SMBH) properties through observations of inspiraling stellar-mass binary black holes (BBHs) orbiting them. Focusing on stable hierarchical triple systems, we model the BBH motion in Kerr spacetime and compute the modulated GW signals using the post Newtonian waveform combined with moving-source transformation. Based on the LISA configuration and second-generation time delay interferometry technology, we estimate parameter uncertainties with the Fisher information matrix. Our results show that the outer semimajor axis has the strongest influence on parameter precision, while the SMBH spin and eccentricity mainly affect their own uncertainties. For high-SNR signals, the SMBH mass and orbital parameters can be measured with relative uncertainties on the order of $10^{-5}$, while the spin magnitude and its orientation can be constrained to within a few percentages. Applying the method to an M87*-like system, GW observations provide more precise measurements of the SMBH mass and spin compared with current electromagnetic observations, highlighting the potential of space-based GW astronomy to probe SMBH properties with high accuracy.

The high computational cost of periodic coupled-cluster theory has limited the density of Brillouin zone sampling, yielding finite-size errors that need to be removed by extrapolation. Here we report the development and application of a distributed-memory software implementation of periodic coupled-cluster theory with single and double excitations (CCSD) that runs efficiently on up to 12 nodes with 96 cores each. This new implementation allows ground-state and excited-state calculations in which the Brillouin zone is sampled with up to $6^3=216$ $k$-points, allowing us to reliably extrapolate to the thermodynamic limit. For eight simple semiconductors and insulators, we report the cohesive energy and band gap, which are converged to 0.1 eV, providing definitive benchmark numbers for the CCSD level of theory. Compared to experimental values, average errors for the cohesive energy are 0.1-0.2 eV (typically an underestimate), and average errors for the band gap are about 0.4 eV (typically an overestimate).

Quantum networks encounter unavoidable channel noises and erasure errors, presenting a huge obstacle in designing protocols that attain both high reliability and efficiency. Typically, quantum networks fall into two categories: those utilize quantum entanglements for quantum teleportation, and those directly transfer the actual quantum messages. In this paper, we present SurfNet, a quantum network that inherits the main advantages from both categories. It employs surface codes as logical qubits for encoding messages, and utilizes two parallel communication channels to fault-tolerantly transfer each surface code in a modular manner. Our approach of using surface codes can timely correct both operational and photon loss errors within the network, and the integration of the two channels within the network can greatly improve network throughput. For the implementation of SurfNet, we propose a novel network architecture, designed to better integrate surface codes into quantum networks. We also propose a novel error correction decoder, designed to fully utilize the modular characteristic of surface codes within our network. Simulation results demonstrate that SurfNet with its decoder significantly enhances the communication fidelity within quantum networks.

Accurate surface thermochemistry requires balanced error cancellation between extended slabs and molecular reference states. This balance can fail whenever the electronic-structure error is not transferable across the chemically distinct species entering a thermodynamic cycle. Here we examine this problem in single-determinant fixed-node diffusion Monte Carlo (SD-FNDMC) for oxygen-reduction intermediates on Pt(111) and selected CO-reduction intermediates on Cu(111). Gas-phase thermochemistry is used to diagnose the reference-state imbalance, and a hybrid cycle is introduced to separate slab–adsorbate binding from molecular formation. The hybrid cycle keeps the surface binding term at the SD-FNDMC level, where cancellation is expected to be most favorable, and replaces the molecular formation contribution with a benchmark coupled cluster reference. For Pt(111), the resulting correction is small for O and OH but larger for OOH, while the geometry-matched refinement gives only a secondary correction. Applying the same cycle to $^\ast$CHO and $^\ast$COH adsorption on Cu(111) gives corrections of opposite sign, showing that the bias is controlled primarily by the electronic structure of the corresponding HCO and COH molecular references rather than by adsorbate geometry alone. This decomposition identifies molecular reference imbalance as a separable source of error in SD-FNDMC surface thermochemistry and reduces the corresponding bias without modifying the SD-FNDMC slab-binding contribution.

Thermal conductivity in a conductor changes by the application of an external magnetic field, which functions as a magneto-thermal switch. For superconductors, a large magneto-thermal switching can occur through a superconducting-to-normal conducting phase transition due to the change in the electron contribution in thermal conductivity. Arima et al. recently reported a nonvolatile nature of the magneto-thermal switching for superconducting solders, which consist of phase-separated Sn and Pb domains. Although they clarified that magnetic flux trapping is required to induce the nonvolatile magneto-thermal switching, a rule for such material design is still unclear. Here, we investigate the microstructure dependence of magneto-thermal switching in superconducting Sn/Pb multilayered composites, which are created by an accumulative roll bonding method. The thickness of each layer, that is the scale of microstructure, can be systematically controlled by the repetition number of roll bonding while the whole sample size and average composition are unchanged. We find that, as the formation of micro-scaled Sn domains proceeds by increasing the repetition number, a nonvolatility in the magneto-thermal conductivity gradually appears in correlation with the remanent magnetization. This study directly confirms that the inclusions with a size comparable to or less than the magnetic vortex in superconducting matrix is essential for magnetic flux trapping, enabling the nonvolatile magneto-thermal switching in superconducting composites.

Primordial black holes (PBHs) in the asteroid-mass window ($10^{17}$-$10^{22}$ g) can account for all of the dark matter without violating any observational constraint, yet are routinely dismissed as fine-tuned. I put that dismissal to the test by applying three complementary fine-tuning measures uniformly across a broad landscape: three non-inflationary PBH production mechanisms, six classes of inflationary PBH models, and seven particle dark matter benchmarks, all evaluated against the same observable target. Three distinct naturalness universality classes emerge, determined entirely by the analytic structure of the abundance map rather than by the nature of the dark matter candidate. Biased-domain-wall PBHs are as natural as off-resonance weakly interacting massive particles and freeze-in particles; early-matter-domination and first-order phase transition PBH mechanisms occupy an intermediate tier alongside coannihilating WIMPs, unified by a structural identity in which the fine-tuning measure equals the logarithm of the ratio of the formation scale to the matter-radiation equality scale; and single-field ultra-slow-roll inflationary collapse is severely tuned for a distinct reason: a double exponential in which the power spectrum amplitude is itself exponentially sensitive to the inflaton potential coefficients, on top of the exponential collapse sensitivity of the abundance map. My main conclusion is that the claim that PBH dark matter is generically fine-tuned conflates the worst case with a landscape spanning every naturalness tier. The three-measure protocol also resolves a tension in the recent literature: the Barbieri-Giudice and Iovino-Riotto fine-tuning measures answer complementary questions and are reconciled within the two-layer decomposition developed here.

Quantum computation of fluid dynamics has attracted growing attention as a key application of fault-tolerant quantum computers anticipated in the coming decade, with lattice Boltzmann methods emerging as a particularly promising approach. Explicit and efficient elementary-gate-level circuit simulations, however, have so far been demonstrated only in the linear case. Here we include the leading nonlinearity through second-order Carleman linearization of the one-dimensional Boltzmann equation, and demonstrate, via explicit quantum-circuit simulation, the preparation of the final-time state using a Taylor-expansion-based ODE solver based on the quantum singular value transformation. With this construction, we analyze the gate and qubit complexities, which scale logarithmically with the grid size, the nonlinearity captured by the higher-order Carleman linearization, and the practical utility of higher-order expansions in the Taylor ODE solver. The construction provides a concrete baseline for computational cost reduction and further developments such as extensions to higher dimensions, complex geometries, and the extraction of physical quantities, towards industrially useful quantum CFD.

We introduce a general phase-space representation that includes global quantum symmetries in the basis expansion. This method, called matrix phase-space, projects the basis onto a reduced Hilbert space, which can greatly reduce sampling errors of many-body quantum simulations and unifies several previous phase-space methods. The purpose of this paper is to provide detailed proofs of basic theorems and operator identities. We also treat several different types of symmetries. To illustrate the benefits of matrix phase-space methods, we give a detailed derivation of a recent application to the topical problem of verifying the outputs of Gaussian boson sampling (GBS) quantum computers with photon number resolving detectors. This has exponential complexity, and using parity symmetry reduces sampling errors by very large factors relative to earlier methods.

We investigate the harvesting of quantum steering and its directional asymmetry between two Unruh-DeWitt detectors in a Lorentz-violating BTZ black hole spacetime. Since the detectors are located at different radial positions outside the black hole, they experience inequivalent local environments induced by gravitational redshift, causing Alice to undergo stronger effective thermal noise than Bob. Remarkably, we uncover a counterintuitive phenomenon in which the detector subjected to a higher effective temperature exhibits stronger steerability than the other one, revealing a nontrivial inversion of thermal intuition in curved spacetime. Furthermore, quantum steering survives only within a finite window of detector energy gaps and reaches its maximum within an optimal regime. We find that Lorentz violation suppresses steering most strongly near this optimal energy gap, indicating an enhanced sensitivity of maximal correlation extraction to symmetry breaking effects. Our results demonstrate that Lorentz violation acts as a geometric constraint on the quantum information capacity of spacetime, simultaneously restricting both the strength and the directionality of quantum correlations.

Accurately representing convection and precipitation remains a persistent challenge for Numerical Weather Prediction (NWP) models due to biases in convective initiation, storm organization, and rainfall distribution, particularly in subtropical/tropical environments. This study evaluated how microphysics parameterizations influence convective organization and precipitation using hindcasts with the Model for Prediction Across Scales - Atmosphere (MPAS-A) on a variable-resolution mesh down to 1-km resolution. Two operational microphysics schemes, National Severe Storm Labs (NSSL) microphysics and Thompson-Eidhammer Microphysics Parameterization for Operations (TEMPO), were examined across three subtropical/tropical regions during boreal summer under strongly- and weakly-forced regimes. Both schemes captured the general timing and placement of convection, but differed in storm structure and rainfall distribution. TEMPO produced more numerous, weaker convective cores with earlier, more widespread precipitation and cooler surface conditions, while NSSL favored fewer, stronger cores and updrafts with more cloud water, ice, and graupel hydrometeors, though less snow, and more spatially concentrated, intense rainfall. Despite these structural differences, both schemes diverged more from observations than from each other, producing scattered convective cells with minimal mesoscale organization and insufficient stratiform precipitation. The simulations also exhibited regime-dependent errors, with rainfall under- (over)-represented in strongly- (weakly)-forced regimes and forecast skill notably lower in the latter. Improving representation of localized precipitation processes remains essential for capturing convection across a wider range of scales and regimes. Future work should target microphysics evaluation across regimes and regions, with process-level improvements reducing convective biases.

We study operator growth in quantized non-KAM systems using out-of-time-ordered correlators (OTOCs), focusing on the kicked harmonic oscillator as a representative example. Since the classical harmonic oscillator is degenerate, the dynamics fall outside the usual Kolmogorov-Arnold-Moser (KAM) framework, and resonances play a central role in shaping the phase space. We examine the system near resonances, where the ratio between the oscillator and driving frequencies takes integer values. Even though the classical Lyapunov exponent remains small at these points, and hence no conventional chaos, the phase space still undergoes strong structural changes. The OTOCs are particularly sensitive to these resonances, with a quadratic-in-time growth at resonance compared to linear growth away from it. Within a perturbative treatment, we derive closed-form expressions for the OTOCs and uncover a number-theoretic structure emerging in the behavior of OTOCs, governed by the Euler totient function of the frequency ratio. Overall, the results we present in this short note imply that resonant structures can play an important role in controlling information spreading.

An implication of Faraday's Law is thst the rate of change of the poloidal flux relative to the toroidal flux associated with a magnetic surface is given by the loop voltage. The emphasis in the tokamak literature on the poloidal almost to the exclusion of the toroidal magnetic flux has led to fundamental mistakes in understanding magnetic field evolution, especially as it affects plasma disuptivity. Designs of tokamak power plants include a divertor that is defined by a separatrix at the plasma edge. The use of the toroidal flux clarifies the descriptions of such plasmas. This paper explores the effects of a separatrix on plasma parameters that are used for assessing disruptivity: the edge safety factor and the internal inductance. The exploration of these effects includes an analytic model, which illustrates why the use of the poloidal instead of the toroidal flux introduces a significant definitional uncertainty in what is meant by the edge safety factor and the internal inductance.

Forecasting pandemic spread across heterogeneous regions demands models that are both accurate and interpretable for public health action. This paper proposes a hybrid forecasting framework that integrates an SEIR epidemiological model enhanced with a Temporal Fusion Transformer (TFT) to produce interpretable multi-horizon infection forecasts. Using region-wise COVID-19 data from the Our World in Data corpus, we present a unified pipeline that integrates epidemiological indicators (susceptible, exposed, infectious, recovered), intervention signals (masking, vaccination, policies), mobility covariates, and demographic context. The SEIR component estimates latent disease dynamics and injects mechanistic priors, while the TFT learns non-linear temporal dependencies and cross-feature interactions from historical observations and known future inputs. To ensure transparency, SHAP (SHapley Additive exPlanations) is applied at both global and local levels, quantifying feature contributions and revealing region-specific drivers of surges and slowdowns. Compared with a calibrated standalone SEIR baseline, the hybrid SEIR-TFT approach provides more stable and accurate forecasts across epidemic phases, particularly under shifting intervention regimes. The framework is further operationalized through structured prediction and explanation outputs for interactive dashboards, enabling time-series, policy-impact, and geospatial analysis. By unifying mechanistic modeling, deep temporal learning, and explainable AI, this study offers a practical decision-support system for policy-sensitive pandemic forecasting.

Commercial off-the-shelf~(COTS) Li ion cells offer an attractive and rechargeable power source for spacecraft applications. However, since these cells are typically designed for charging above 0\,\textdegree C, challenging sub-zero temperatures could inflict irreversible degradation and safety hazards during in-flight operation. To address these challenges, this work explores strategies affecting their low-temperature performance. Specifically, we investigate the Li plating behavior of the COTS~LG~Chem~18650~HG2 cell during galvanostatic cycling at $-20\,$\textdegree C and its dependence on prior high-temperature preconditioning protocols, involving either two-week galvanostatic cycling or storage at 30\,\textdegree C, as well as a reference case without preconditioning, i.e., direct cycling at $-20\,$\textdegree C. Experimental cycling tests reveal distinct differences in Li metal deposition between cells subjected to these conditions. To identify the origin of these discrepancies, we combine computed tomography of the electrode microstructure with three-dimensional, spatially resolved cell modeling and analyze the simulation results.

We study constitutive conditions of hyperelastic potentials for incompressible material behavior in three dimensions. By means of a counterexample, we show that polyconvexity does not imply true-stress-true-strain monotonicity. Thus, polyconvexity alone is not strong enough to guarantee a physically reasonable response for idealized elasticity.

Binary collision approximation (BCA) codes are potentially powerful tools to simulate ion irradiation ejecta properties, such as the composition and the angular and energy distributions of the sputter yield. However, recent advances in the sputtering of minerals have highlighted the low predictive fidelity of BCA codes such as SDTrimSP when compared to experimental measurements. We demonstrate how a sputtering model that underestimates the forward sputtering on a flat surface at large ion incidence angles from surface normal will lead to an erroneous result for rough and porous surfaces, where most ejected particles are directed along the surface normal. We demonstrate how this is the case for an existing model, which reliably predicts sputtering mass yields from a flat enstatite surface but fails to accurately reproduce the angular distribution of sputtered particles. We then compare this to a BCA model incorporating higher surface-binding energies$-$based on a molecular dynamics description of plagioclase$-$which underestimates mass yields but significantly reduces back-sputtering and better reproduces laboratory sputter angle distributions measured at large ion incidence angles. We conclude that the BCA model cannot simultaneously reproduce both the sputter yield and the sputter angle distribution arising from He irradiation of mineral targets, either due to the inherent geometric simplicity of the BCA or because the model neglects yield-enhancing processes such as molecule and cluster sputtering. This demonstrates a structural limitation of current BCA-based models when realistic surface morphologies are considered, rather than a problem that can be resolved by parameter tuning alone.

Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling. Building on iBonD, the largest experimental pKa database established, we and other researchers have developed several methods including machine-learning-based empirical prediction and high-accuracy energy calculations. Despite this foundation, the rapid augmentation of high-quality pKa data remains fundamentally constrained. As part of this work, we performed large-scale regression-based pKa prediction on unlabeled molecular datasets using a collection of extensively optimized machine-learning models. The results indicate that, since the feature distributions of unlabeled molecular datasets, the pKa data distribution approximates normality, with extreme scarcity of tail-region samples. Although such augmentation is highly valuable for improving overall data availability and predictive modeling, it remains insufficient for efficiently discovering molecules with broad-spectrum pKa properties. To address this, we explore the targeted generation of molecules with sparse pKa properties from the vast chemical space. Given that traditional continuous latent space VAE-RNN methods for molecular generation suffer from insufficient stability and fail to demonstrate clear advantages in complementing sparse data, we design and implement a quantum-assisted sparse-pKa molecular generation. Feasibility is validated on a simulated quantum annealer, and superior extreme-value sampling is further achieved on physical coherent Ising machines (CIMs). (to be continued)

The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties. As the community transitions toward CMIP7, efficiently extracting and operationalizing this unstructured knowledge alongside live data analysis represents a critical bottleneck. Here we present CMIP-Forge, a hybrid retrieval-augmented generation (RAG) and autonomous analysis system that bridges the gap between scientific literature and Earth System Grid Federation (ESGF) data archives. The system pairs a curated corpus of 6,581 CMIP6-related open-access publications (101,828 indexed chunks) with an agentic pipeline in which a tool-augmented worker plans and executes Python workflows over live climate data, while a panel of independent reviewer models audits its methodology end to end. CMIP-Forge introduces a multi-layered Defense-in-Depth architecture that enforces physical and methodological invariants through executable mechanisms: Abstract Syntax Tree (AST) static analysis, audited scientific primitives, and an autonomous adversarial peer-review protocol. We demonstrate the system's capabilities through end-to-end autonomous research pipelines spanning atmospheric teleconnections, ocean dynamics, regional extremes, and global warming projections. An agentic analysis system grounded in peer-reviewed literature, constrained by automated code guardrails, and audited by an independent adversarial review loop can complete complex climate-research workflows autonomously. The same experiments expose concrete failure modes of the review loop (sycophantic regression, REVISE verdicts that are never resolved, and the submission of stub code for review), each diagnosable from the immutable telemetry and provenance record released with the article.

We investigate the effective four-dimensional cosmology induced by unimodular gravity in $D=4+d$ dimensions, where the internal extra-dimensional volume is encoded in a scalar degree of freedom. After dimensional reduction, we show that the resulting FLRW equations admit a natural autonomous formulation whose phase-space structure differs qualitatively from that of general relativity. In the vacuum sector, the reduced system exhibits a continuous family of finite equilibrium points, $\lambda=dH$, together with well-defined asymptotic Poincaré directions. In the matter sector, we focus on the five-dimensional case $d=1$ and use the reduced Bianchi relation as the consistency condition that links the ordinary matter component to the internal-volume degree of freedom. The system is then closed by adopting the minimal higher-dimensional conservation prescription, according to which matter is diluted by both the external volume and the internal-volume modulus. This leads to a reduced matter–geometry dynamics with isolated critical points and a globally organized compactified flow. Numerical examples illustrate how the internal-volume degree of freedom affects the background evolution and the global phase-space structure. The comparison with $\Lambda$CDM is used only as a benchmark, while a full observational analysis and more general matter–geometry exchange prescriptions are left for future work.

Metastable reaction rates are commonly inferred from transition-state fluxes, mean first-passage times, or fitted kinetic models. We show that they are directly encoded in the plateau gap of an occupation-time Poisson corrector. For a centered basin-occupation observable, the Poisson corrector develops metastable plateaus in the reactant and product basins, and their separation determines the forward and backward transition rates. This construction requires only the generator, stationary measure, and metastable partition, and therefore does not rely on a predefined transition-state surface. In overdamped and underdamped double-well dynamics, the plateau-gap rate recovers the Kramers, Grote-Hynes, and Pollak-Grabert-Hänggi hierarchy. The same corrector-martingale decomposition yields a reactive-noise density, revealing where stochastic forcing contributes to transitions in configuration or phase space. Thus, reaction rates and their fluctuation sources emerge from a single corrector field.

Imploding solutions to the radially symmetric, isentropic, compressible Euler equations have been well-studied, inspired by the work of Guderley. However, these smooth imploding solutions are shown to be numerically unstable and difficult to compute in practice. On the other hand, the imploding solution of Kidder has a closed form solution and is numerically computable. But, it is unbounded in the far field. We consider Kidder's formulation in one dimension in which the unbounded far field condition is replaced with a constant density cutoff of the initial data. Strikingly, a non-centered rarefaction emerges from the cutoff and suppresses the implosion. We present an exact analytic solution to the problem with the cutoff and support our theoretical predictions with numerical simulations.

Quantum clocks can acquire relativistic phases from motional or gravitational proper-time differences, but reduced clock dephasing alone does not certify nonclassical proper-time histories. We formulate this distinction as a channel-certification problem. First, we show that any two-level single-time dephasing signal, including one generated by an effective quantum proper-time label, admits a classical random proper-time representation. We then define the convex set of classical mixtures of experimentally specified proper-time histories and prove a Choi-rank separation criterion for conditioned coherent history recombination. A two-branch Ramsey protocol gives explicit bright- and dark-port population witnesses outside this classical set. The certification is operational and relative to the specified history set: it rules out classical mixtures of the same implemented proper-time histories, not arbitrary classical protocols with different histories or controls.