Computational Physics
BattMo – Battery Modelling Toolbox
Xavier Raynaud, Halvor Møll Nilsen, August Johansson et al. (+6 more)
This paper presents the Battery Modelling Toolbox (BattMo), a flexible finite volume continuum modelling framework in MATLAB\textsuperscript{\textregistered} (\citeproc{ref-MATLAB}{The MathWorks Inc., 2025}) for simulating the performance of electro-chemical
cells. BattMo can quickly setup and solve models for a variety of battery chemistries, even considering 3D designs such as cylindrical and prismatic
cells. The simulation input parameters, including the material parameters and
geometric descriptions, are specified through JSON schemas. In this respect, we follow the guidelines of the Battery Interface Ontology (BattINFO) to support semantic interoperability in accordance with the FAIR principles (\citeproc{ref-fair}{Wilkinson et al., 2016}). The Doyle-Fuller-Newman (DFN) (\citeproc{ref-Doyle1993ModelingCell}{Doyle et al., 1993}) approach is used as a base model. We include fully coupled thermal simulations. It is possible to include degradation mechanisms such as SEI layer growth, and the use of composite material, such as a mixture of Silicon and graphite. The models are setup in a hierarchical way, for clarity and modularity. Each model corresponds to a computational graph, which introduces a set of variables (the nodes) and functional relationship (the edges). This design enables the flexibility for changing and designing new models. The solver in BattMo uses automatic differentiation and support adjoint computation. We can therefore compute the derivative of objective functions with respect to all parameters efficiently. Gradient-based optimization routines can be used to calibrate parameters from experimental data.
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All authors Xavier Raynaud, Halvor Møll Nilsen, August Johansson, Eibar Flores, Lorena Hendrix, Francesca Watson, Sridevi Krishnamurthi, Olav Møyner, Simon Clark
Biological Physics
Subcellular Metabolic Tracking Using Fluorescent Nanodiamonds Relaxometry
Jia Su, Linyu Zeng, Pengyu Chen et al. (+2 more)
Fluorescent nanodiamonds (FNDs) relaxometry holds promising future for advancement of high spatiotemporal resolution metabolic imaging technology. In this study, we demonstrate a simultaneous integration of spatial position tracking with FND relaxometry to characterize the temporal dynamics of metabolic processes, thereby enhancing the capability to monitor cellular activities over time. To enable targeted metabolic probing in living
cells, FNDs were surface-functionalized to achieve specific localization within key organelles, including the
nucleus and mitochondria. This strategy not only facilitates subcellular-level metabolic monitoring but also allows for direct comparison between intra- and extranuclear microenvironments within the same living
cell, offering substantial potential for elucidating the spatial and functional heterogeneity of cellular metabolism.
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All authors Jia Su, Linyu Zeng, Pengyu Chen, Zenghao Kong, Fazhan Shi
General Physics
Matter-free gravitational collapse and the equivalence principle
Juri Dimaschko
The dynamics of a degenerate spherically symmetric wormhole in a
vacuum is considered. An extension of the equivalence principle to matter free objects that are the source of a gravitational field is proposed. Using the Klinkhamer metric as an example, it is shown that a degenerate wormhole is precisely such an object. Application of the extended equivalence principle reduces the radial dynamics of the Klinkhamer wormhole to the dynamics of the radial fall of a test particle in a Schwarzschild gravitational field. It is proven that any bound state of the traversable Klinkhamer wormhole eventually collapses into a nontraversable Einstein-Rosen wormhole. An estimate is presented showing that the traversable Klinkhamer wormhole, although nonstationary, is a longlived state.
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Physics and Society
Language, participation and inclusivity in the urban planning process in Mzuzu City
Francis Engwayo A Mgawadere and Mtafu Manda
Participation in urban planning is championed for entrenching democracy and development. Malawi passed the Local Government Act (1998) and Decentralization Policy (1998) to facilitate community participation in decision-making processes. Several studies have been conducted on decentralization and local governance on community participation. Little
attention has been paid to examining the impact of the
language used in planning processes on democracy and inclusivity envisaged in the law and policy. Using communicative action theory, the study examined challenges posed by
language used in planning processes on inclusivity in the approval processes of urban plans. Data were collected through semi-structured interviews, focus group discussions, observations and document review and analyzed using thematic and discourse analysis. The findings show that while there is high participation at community planning levels, because planners communicate using local
languages, participation is compromised in the service committees at city level where final planning decisions are made due to
language barrier. Specifically, lack of sincerity, truthfulness, comprehensibility and therefore legitimacy are apparent. Planners are reluctant to simplify written
language and translate planning jargon into local
languages for councillors to understand. The study concludes that community participation in the urban planning process in Mzuzu fails to entrench democracy due to lack of inclusiveness owing to the
language barrier at city level where final planning decisions are made. The study proposes a framework for inclusive participation in urban planning including the motivation, conditions for effective participation and outcomes of participation.
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Physics and Society
Topological Dirac cluster synchronization on directed hypergraphs
Yupeng Guo, Ahmed A. A. Zaid, Xueming Liu et al. (+1 more)
Topological higher-order synchronization reveals collective phenomena demonstrating how
topology shapes dynamics, yet providing a general theoretical framework for designing and controlling dynamical states on higher-order networks remains a challenge of the field. Here we propose a dynamical systems framework for topological cluster synchronization on directed hypergraphs that can be used to design synchronization patterns on nodes and hyperedges of directed hypergraphs. By encoding oriented hyperedges into the hypergraph topological Dirac
Hamiltonian, we obtain a spectrum whose isolated eigenstates correspond to distinct topological synchronization cluster states defined jointly on nodes and hyperedges. By selecting any isolated eigenstate, the system can be driven toward the associated dynamical state reflecting a specific partition of the hypergraph without modifying the underlying hypergraph structure. We numerically demonstrate the ability to design different topological cluster synchronization states on directed-hypergraph block models and empirical systems-including higher-order contact networks and the ABIDE functional
brain network. Our results establish a general and interpretable route for controlling collective dynamics in directed higher-order systems.
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All authors Yupeng Guo, Ahmed A. A. Zaid, Xueming Liu, Ginestra Bianconi
Fluid Dynamics
Reduced-Order Hydrodynamic Modelling of a Sphere Near a Wall Using Sparse Regression and Neural Networks
Zev Hoffman, Sara Vahaji, Arpan Das et al. (+4 more)
This work presents an interpretable parametric surrogate model motivated by the need to identify a hydrodynamic model for resolving the trajectory of an object in real-time. The surrogate is formulated as a reduced-order model for a canonical configuration in which a one-degree-of-freedom heaving sphere operates near a vertical wall. High-fidelity CFD simulations are used to generate a parametric
dataset of heave-decay responses over varying wall distances (WD) and drop heights (DH). Sparse Identification of Nonlinear Dynamics (SINDy) is then applied to each CFD trajectory to identify a low-order nonlinear ordinary differential equation (ODE) with
polynomial terms representing effective hydrostatic restoring and radiation damping, and the harmonic terms representing the wave-induced excitation forces. The SINDy identified coefficients are then used as a prior constraint in a neural operator network (ONet) that learns a smooth mapping from wall distance and drop height to the ODE coefficients, yielding a surrogate capable of predicting dynamics at arbitrary points in the input space without rerunning expensive CFD calculations. The resulting surrogate reproduces CFD heave-decay responses with near-optimal accuracy given the limiting assumptions while being capable of running in real time. The approach provides a practical pathway toward real-time, physics-informed surrogate modelling for launch-and-recovery operations.
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All authors Zev Hoffman, Sara Vahaji, Arpan Das, Micheal Candon, Daniel Sgarioto, Jayarathne Nirman, Pier Marzocca
Optics
Solid-state Laser Cooling
Yang Ding, Shenghao Zhang, Alexander R. Albrecht et al. (+8 more)
Since the first proof-of-concept demonstrations of photoluminescence-based optical refrigeration, solid-state
laser cooling has developed into a credible competitor to conventional cryogenic technologies. Solid-state
laser cooling continues to advance as new materials push cooling limits. These developments have created a need to consolidate progress made to date as well as standardize critical experimental considerations needed for reliable and verifiable cooling measurements. This primer therefore outlines essential concepts and requirements, which underpin solid-state
laser cooling. The primer summarizes key milestones achieved with cooling-grade, rare-earth-doped glasses and
crystals as well as with
semiconductors. It additionally highlights emerging applications of solid-state optical refrigeration. To strengthen the consistency and reproducibility of cooling results going forward, two reporting checklists are introduced. They cover materials, cooling metrics, and thermometry. This primer is intended to serve as both a tutorial and a practical reference for incoming and existing researchers involved in solid-state
laser-cooling.
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All authors Yang Ding, Shenghao Zhang, Alexander R. Albrecht, Zhaojie Feng, Lars Forberger, Hiroki Tanaka, Markus P. Hehlen, Galina Nemova, Peter J. Pauzauskie, Denis V. Seletskiy, Masaru Kuno
Optics
Momentum-space non-Hermitian skin effect in an exciton-polariton system
Yow-Ming Hu, Mateusz Król, Daria A. Smirnova et al. (+10 more)
Localization of a macroscopic number of eigenstates on a real-space boundary, known as the non-Hermitian skin effect, is one of the striking topological features emerging from non-Hermiticity. Realizing this effect typically requires periodic (
lattice) systems with asymmetry of intersite coupling, which is not readily available in many physical platforms. Instead, it is meticulously engineered, e.g., in photonics, which results in complex structures requiring precise fabrication steps. Here, we propose a simpler mechanism: introducing an asymmetric, purely imaginary potential in a topologically trivial system induces momentum-space localization akin to the skin effect. We experimentally demonstrate this localization using
exciton polaritons, hybrid light-matter quasi-particles in a simple engineered `round box' trap, pumped by a
laser pump offset from the trap center. The effect disappears if the pump is concentric with the trap. The localization persists and becomes stronger at higher densities of polaritons, when a non-
equilibrium Bose-Einstein condensate is formed and the system becomes nonlinear. Our approach offers a new route to realizing skin effects in continuous, non-periodic systems and exploring the interplay of non-Hermiticity,
topology, and nonlinearity in macroscopic
quantum states.
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All authors Yow-Ming Hu, Mateusz Król, Daria A. Smirnova, Lev A. Smirnov, Bianca Rae Fabricante, Karol Winkler, Martin Kamp, Christian Schneider, Sven Höfling, Timothy C. H. Liew, Andrew G. Truscott, Elena A. Ostrovskaya, Eliezer Estrecho
Optics · Cross-listed from cond-mat.mes-hall
Harnessing Vacuum Fluctuations to Shape Electronic and Photonic Behavior
Qing-Dong Jiang
Vacuum quantum fluctuations are an inescapable and fundamental feature of modern physics. By integrating cavity-enhanced or surface-modified
vacuum quantum fluctuations with low-dimensional materials, a new paradigm-vacuumronics-emerges, enabling unprecedented control over both material properties and photonic responses at the micro- and nanoscale. This synergy opens novel pathways for engineering
quantum light-matter interactions, advancing applications in
quantum photonics, nanoscale optoelectronics, and
quantum material design.
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Instrumentation and Detectors
{\mu}RWELL-PICOSEC: Precision Timing with Resistive Micro-Well Detector
Kondo Gnanvo
The PICOSEC detector concept uses a micro-pattern gaseous detector (MPGD) amplification structure combined with a Cerenkov radiator coated with a semi-transparent photocathode to provide below tens of picosecond-level precision timing capabilities with minimum ionizing particles. PICOSEC has triggered interest in the development of time-of-flight detectors for particle identification and timing detectors for track reconstruction in the high rate environment of future
nuclear and high energy physics experiments. The PICOSEC Micromegas (or PICOSEC-MM) detector, developed by the CERN-based PICOSEC collaboration, use the Micromegas structure for gaseous amplification and achieve below 20 ps timing resolution. A new type of PICOSEC detector, the {\mu}RWELL-PICOSEC based on {\mu}RWELL amplification structure, is being investigated at Thomas Jefferson National Accelerator Facility (Jefferson Lab) alongside PICOSEC-MM R&D efforts in Europe. Preliminary results from the two 2024 beam test campaigns at CERN demonstrate a timing performance better than 24 ps is achievable with a single-channel {\mu}RWELL-PICOSEC prototype. A vigorous R&D effort is ongoing to improve the timing performance, robustness and operational stability of {\mu}RWELL-PICOSEC detectors. Development of a large size {\mu}RWELL-PICOSEC is also under consideration for applications in large scale experiments.
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Computational Physics
Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder
Pietro Chiarantoni, Oscar Serra, Mohammad Erfan Mowlaei et al. (+5 more)
We present a variational autoencoder framework for learning and generating configurations of structured
polymer globules from distance
matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as the training set for our deep learning model. By combining convolution and
attention layers, the model encodes the structural patterns of distance
matrices into an organized and roto-translationally invariant latent space of lower dimensionality. The generative capability of the variational autoencoder, coupled with a post-processing pipeline based on multidimensional scaling and short molecular dynamics, enables the recovery of physically meaningful configurations. The reconstructed and generated samples reproduce key observables, including energy, size, and
entanglement, despite minor discrepancies in the raw decoder output.
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All authors Pietro Chiarantoni, Oscar Serra, Mohammad Erfan Mowlaei, Venkata Surya Kumar Choutipalli, Mark DelloStritto, Xinghua Shi, Micheal L. Klein, Vincenzo Carnevale
Computational Physics
A Model-Guided Neural Network Method for the Inverse Scattering Problem
Olivia Tsang, Owen Melia, Vasileios Charisopoulos et al. (+3 more)
Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in capturing prior knowledge of imaging targets relative to classical optimization-based approaches; however, they perform poorly in regimes where the scattering behavior is highly nonlinear. A key limitation is that ML methods struggle to incorporate the physics governing the scattering process, which are typically inferred implicitly from the training data or loosely enforced via architectural design. In this paper, we present a method that endows a machine learning framework with explicit knowledge of problem physics, in the form of a differentiable solver representing the forward model. The proposed method progressively refines reconstructions of the scattering potential using measurements at increasing wave frequencies, following a classical strategy to stabilize recovery. Empirically, we find that our method provides high-quality reconstructions at a fraction of the computational or sampling costs of competing approaches.
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All authors Olivia Tsang, Owen Melia, Vasileios Charisopoulos, Jeremy Hoskins, Yuehaw Khoo, Rebecca Willett
Instrumentation and Detectors
A Computational Procedure for Assessing I$_c$($\varepsilon$) in Nb$_3$Sn/Bi-2212 Hybrid Magnets
A. D'Agliano, A. V. Zlobin, I. Novitski et al. (+5 more)
The critical current of
superconductors is commonly measured by testing unloaded wires under an external
magnetic field. While stressed by intense Lorentz forces, the existing HTS/LTS
superconductors are prone to a reduction in critical current before reaching their structural mechanical limit. In this work, the
magnetic and mechanical analysis of the FNAL 4-layer Bi-2212/Nb$_3$Sn hybrid dipole magnet is reported, aimed at predicting the critical current degradation for both the
superconductors during powering at 16 T. All the Rutherford cables in the coils of the hybrid magnet were modeled at the strand level in Ansys APDL with the heterogeneous cable model. Utilizing this detailed geometry, it was possible to evaluate the effects of strain on the critical current degradation for both the Nb$_3$Sn and Bi-2212
superconductors under the intense Lorentz forces. The analysis presented in this paper integrates strain-dependent critical current laws, with parameters derived from experimental data, to simulate the hybrid magnet's performance for all possible current-powering configurations. The proposed methodology enables a detailed assessment of conductor integrity and I$_C$($\varepsilon$) reduction in existing hybrid magnet designs, providing a versatile and rigorous framework for optimizing future high-field hybrid magnets.
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All authors A. D'Agliano, A. V. Zlobin, I. Novitski, G. Vallone, P. Ferracin, E. Barzi, S. Donati, V. Giusti
Biological Physics
Unifying Theories in High-Dimensional Biology: Approaches, Challenges and Opportunities
Marianne Bauer, Akshit Goyal, Sidhartha Goyal et al. (+19 more)
Across biological subdisciplines, the last decade has seen an explosion of high-dimensional
datasets, including
datasets for
cells, species, immune systems,
neurons and behaviour. At the ICTS workshop 'Unifying Theories in High-Dimensional Biophysics' we discussed whether this high dimensionality poses a challenge or opportunity for describing, understanding and predicting biological systems theoretically. We discussed methods, models and frameworks that can help with addressing empirical observations based on these high-dimensional
datasets. We summarize the challenges and opportunities that emerged in discussions according to individual participants below.
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All authors Marianne Bauer, Akshit Goyal, Sidhartha Goyal, Gautam Reddy, Shaon Chakrabarti, Michael M Desai, William Gilpin, Jacopo Grilli, Kabir Husain, Sanjay Jain, Mohit Kumar Jolly, Kyogo Kawaguchi, Aneta Koseska, Milo Lin, Leelavati Narlikar, Simone Pigolotti, Archishman Raju, Krishna Shrinivas, Rahul Siddharthan, Greg J Stephens, Andreas Tiffeau-Mayer, Suriyanarayanan Vaikuntanathan
Applied Physics
Investigation of Real-Space Transfer Noise in InP Quantum Wells
Jiayin Zhang, Anthony J. Ardizzi, Kieran A. Cleary et al. (+1 more)
Indium phosphide (InP) high electron-mobility transistors (HEMTs) are widely used in many fields such as
quantum computing because of their unparalleled microwave noise performance. Achieving improved noise performance requires a physical understanding of the noise mechanisms. Here, we experimentally test a theoretical proposal for drain (output) noise as originating in part from real-space transfer (RST) by characterizing the microwave noise temperature of transfer-length method structures with the same channel composition but two different barrier compositions. This choice was made to alter the confining potential of electrons in the channel, thereby affecting the RST mechanism, while avoiding changes to the channel transport properties. We observe trends of noise temperature with physical temperature and source-drain voltage which are compatible with the predictions of RST noise theory. This finding supports the hypothesis that RST contributes to drain noise in HEMTs.
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All authors Jiayin Zhang, Anthony J. Ardizzi, Kieran A. Cleary, Austin J. Minnich
Instrumentation and Detectors · Cross-listed from hep-ex
Enabling searches for long-lived particles at a future 10 TeV Muon Collider
Mira Littmann, Mark Larson, Kate Wandrisco et al. (+5 more)
Muon Colliders offer fantastic opportunities to explore new phenomena at the energy frontier. However, beam-induced-backgrounds from
muon decays pose significant challenges for detector design, readout, and reconstruction. Previous detector studies have employed stringent hit-timing requirements to reduce occupancy to manageable levels with negligible efficiency loss for
prompt Standard Model particles. In the spirit of maximizing discovery potential, we investigate the capability of detecting meta-stable charged long-lived particles at a 10 TeV
Muon Collider. As a
benchmark, we consider a Gauge Mediated Supersymmetry Breaking (GMSB) model in which the stau is long-lived and can be identified as a high momentum, slowly moving track. We find that nominal hit-timing selections are too restrictive, and investigate the impact of looser requirements. We demonstrate that it is possible to recover sensitivity to particles with masses close to $\sqrt{s}/2$ by expanding time acceptance, and provide recommendations to further improve tracker design and track reconstruction.
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All authors Mira Littmann, Mark Larson, Kate Wandrisco, Benjamin Rosser, Tate Flicker, Kane Huang, Leo Rozanov, Karri Folan Di Petrillo
Optics
Generation of Perfectly Achromatic Optical Vortices Using a Compensated Tandem Twisted Nematic Cell
Dmytro O. Plutenko and Mikhail V. Vasnetsov
The generation of white optical
vortices is currently constrained by intrinsic trade-offs between spectral bandwidth, conversion efficiency, and temporal pulse integrity in conventional diffractive and
geometric-phase approaches. In this work, we theoretically investigate a compensated tandem crossed twisted nematic (TN)
liquid crystal architecture that overcomes these fundamental limitations. By developing a rigorous Jones
matrix model and defining specific figures of merit for chromatic fidelity, we analyze the impact of manufacturing imperfections and non-adiabatic waveguiding (deviations from the Mauguin regime) on the device performance. We propose and evaluate three distinct compensation strategies, ranging from optimized passive designs for specific manufacturing tolerances to a robust active compensation scheme utilizing a tunable retarder. Our analysis demonstrates that the active approach effectively nullifies parasitic amplitude modulation, enabling the generation of perfectly achromatic
vortices with high phase purity across an arbitrary bandwidth. This establishes the compensated tandem TN
cell as a superior and versatile platform for high-fidelity white-light singular optics.
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Computational Physics · Cross-listed from math.NA
Efficient Boys function evaluation using minimax approximation
Rasmus Vikhamar-Sandberg and Michal Repisky
We present an
algorithm for efficient evaluation of Boys functions $F_0,\dots,F_{k_\mathrm{max}}$ tailored to modern computing architectures, in particular graphical processing units (GPUs), where maximum throughput is high and data movement is costly. The method combines rational minimax approximations with upward and downward recurrence relations. The non-negative real axis is partitioned into three regions, $[0,\infty\rangle = A\cup B\cup C$, where regions $A$ and $B$ are treated using rational minimax approximations and region $C$ by an asymptotic approximation. This formulation avoids lookup tables and irregular
memory access, making it well suited hardware with high maximum throughput and low latency. The rational minimax coefficients are generated using the rational Remez
algorithm. For a target maximum absolute error of $\varepsilon_\mathrm{tol} = 5\cdot10^{-14}$, the corresponding approximation regions and coefficients for Boys functions $F_0,\dots,F_{32}$ are provided in the appendix.
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Data Analysis and Statistics · Cross-listed from stat.ME
A Primer on Bayesian Parameter Estimation and Model Selection for Battery Simulators
Yannick Kuhn, Masaki Adachi, Micha Philipp et al. (+2 more)
Physics-based battery modelling has emerged to accelerate battery materials discovery and performance assessment. Its success, however, is still hindered by difficulties in aligning models to experimental data.
Bayesian approaches are a valuable tool to overcome these challenges, since they enable prior assumptions and observations to be combined in a principled manner that improves numerical conditioning. Here we introduce two new
algorithms to the battery community, SOBER and BASQ, that greatly speed up
Bayesian inference for parameterisation and model comparison. We showcase how
Bayesian model selection allows us to tackle data observability, model identifiability, and data-informed model development together. We propose this approach for the search for battery models of novel materials.
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All authors Yannick Kuhn, Masaki Adachi, Micha Philipp, David A. Howey, Birger Horstmann
Data Analysis and Statistics · Cross-listed from cs.LG
Detailed balance in large language model-driven agents
Zhuo-Yang Song, Qing-Hong Cao, Ming-xing Luo et al. (+1 more)
Large
language model (LLM)-driven
agents are emerging as a powerful new paradigm for solving complex problems. Despite the empirical success of these practices, a theoretical framework to understand and unify their macroscopic dynamics remains lacking. This paper proposes a method based on the least action principle to estimate the underlying generative directionality of LLMs embedded within
agents. By experimentally measuring the transition probabilities between LLM-generated states, we report evidence consistent with a tendency toward detailed balance in LLM-generated transitions, indicating that LLM generation may not be achieved by generally learning rule sets and strategies, but rather by implicitly learning a class of underlying potential functions that may transcend different LLM architectures and
prompt templates. To our knowledge, this provides an effective macroscopic description of LLM generative dynamics that does not depend on specific model details. This work is an attempt to establish a macroscopic dynamics theory of complex AI systems, aiming to elevate the study of AI
agents from a collection of engineering practices to a science built on effective measurements that are predictable and quantifiable.
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All authors Zhuo-Yang Song, Qing-Hong Cao, Ming-xing Luo, Hua Xing Zhu
Applied Physics · Cross-listed from quant-ph
Broadband Spatio-Spectral Mode Conversion via Four-Wave Mixing
Helaman Flores, Mahmoud Jalali Mehrabad, Siavash Mirzaei-Ghormish et al. (+2 more)
We introduce a framework for scalable and broadband free-space phase-matched four-wave mixing in ring resonators. This method for four-wave mixing reduces the complexity of coupling an emitter to a
quantum network by combining the spatial and spectral interfaces between them into one nonlinear optical process. The device is compliant with current heterogeneous integration capabilities and has a bandwidth of 165 nm for efficient spatio-spectral conversion. We outline a fabrication-ready diamond-on-
insulator pathway towards modular unit
cells that natively bridge visible color centers to the infrared spectrum for scalable
quantum networks. We also present and analyze an end-to-end framework for considering single-
photon coupling efficiency from a color center to a
quantum network. This framework represents a step forwards in analyzing and reducing system-scale losses in a
spin-
photon interface.
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All authors Helaman Flores, Mahmoud Jalali Mehrabad, Siavash Mirzaei-Ghormish, Ryan M. Camacho, Dirk Englund
Instrumentation and Detectors
Identifying Neutron Sources using Recoil and Time-of-Flight Spectroscopy
David Breitenmoser, Ricardo Lopez, Shaun D. Clarke et al. (+1 more)
Neutron-source identification is central to
nuclear physics and its applications, from planetary science to
nuclear security, yet direct source discrimination from measured
neutron spectra remains fundamentally elusive. Here, we introduce a
Bayesian protocol that directly infers source ensembles from measured
neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to
recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance ($>\!\!4\sigma$) at event counts as low as $\sim\!\!10^{3}$. These results demonstrate that
neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.
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All authors David Breitenmoser, Ricardo Lopez, Shaun D. Clarke, Sara A. Pozzi