Solar and Stellar Astrophysics
A Search For Stellar-mass Black Holes Via Astrometric Microlensing II: 2012-2015 Keck Candidates
Mace J. Huston, Jessica R. Lu, Casey Y. Lam et al. (+55 more)
The Milky Way is expected to host $\sim$10$^8$
stellar-mass black holes with an uncertain binary fraction. The only proven method to detect isolated
stellar mass black holes is gravitational microlensing. Here we report the results of a microlensing search for black holes with photometry and astrometry. By combining 10 years of seeing-limited photometry from OGLE and MOA with diffraction-limited photometry and astrometry from adaptive optics imagers at the W.~M.~Keck Observatory, we constrain lens masses for OGLE-2012-BLG-0169, OGLE-2014-BLG-0613/MOA-2015-BLG-041, OGLE-2015-BLG-0029/MOA-2015-BLG-170, and OGLE-2015-BLG-0211. Of the four long-duration microlensing events monitored, we ruled out black hole lenses in 3 events, which likely have
stellar or white
dwarf lenses. OGLE-2015-BLG-0211 remains a black hole candidate with a poorly constrained lens mass with a 1$\sigma$ upper mass limit of 3.2$M_\odot$ and a 3$\sigma$ upper mass limit of 21.6$M_\odot$. This event suffered from poor observing conditions and significant astrometric reference frame uncertainties, but its analysis may benefit from additional astrometric data in the upcoming
Gaia Data Release 4. Of the six long-timescale ($t_E>100$ days) microlensing events from this work and previous studies, one black hole has been confirmed with a second not ruled out. We briefly examine
Galactic model simulations and find that our result agrees with current expectations. Ultimately, we need a larger sample of isolated black holes to constrain their formation processes. This will be possible in the coming years with Rubin and Roman, as well as improved astrometry from JWST and large, ground-based
telescopes equipped with adaptive optics.
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All authors Mace J. Huston, Jessica R. Lu, Casey Y. Lam, J. Nijaid Arredondo, Natasha S. Abrams, Shep Brooke, Sage H. Remulla, Eran Ofek, Michael S. Medford, Fatima Abdurrahman, Shrihan Agarwal, Edward Broadberry, Matthew Freeman, Matthew W. Hosek Jr., Siyao Jia, Sean K. Terry, The OGLE Collaboration: Andrzej Udalski, Przemek Mroz, Radoslaw Poleski, Jan Skowron, Michal K. Szymanski, Igor Soszynski, Pawel Pietrukowicz, Szymon Kozlowski, Krzysztof Ulaczyk, Krzysztof A. Rybicki, Patryk Iwanek, Marcin Wrona, Mariusz Gromadzki, Mateusz J. Mroz, Michal Malkowski, The MOA Collaboration: Fumio Abe, David P. Bennett, Aparna Bhattacharya, Ian A. Bond, Kotaro Daimon, Ryusei Hamada, Yuki Hirao, Shuma Makida, Stela Ishitani Silva, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Ryo Ogawa, Ryunosuke Oishi, Hideaki Ose, Greg Olmschenk, Clement Ranc, Nicholas J. Rattenbury, Yuki K. Satoh, Takahiro Sumi, Daisuke Suzuki, Takuto Tamaoki, Chihiro Ueda, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
Statistics Applications
Multivariate Continuous-Time Autoregressive Moving Average Processes for Astronomical Multiband Time Series
Izak Schmidlkofer, Zhirui Hu, Lishan Shi et al. (+2 more)
Large-scale astronomical surveys provide unprecedented volumes of multivariate time-series observations obtained through multiple optical filters. We develop a structured multivariate continuous-time autoregressive moving average (MCARMA) framework for multi-band time series with irregular sampling, heteroscedastic measurement errors, and partially observed bands. The framework allows band-specific
stochastic dynamics while modeling cross-band dependence through correlated Brownian driving processes, with state-space and spectral representations enabling likelihood-based inference and interpretation of the fitted
stochastic dynamics. We develop a two-stage estimation procedure in which a numerically stabilized preliminary fit initializes subsequent maximum likelihood estimation. Simulations show that higher-order
stochastic structure can be recovered when its characteristic features are adequately resolved, but can become weakly identifiable because of limited temporal resolution or near pole–zero cancellation. Joint multivariate estimation improves parameter recovery in 23 of 27 settings and spectral recovery in 26 of 27 settings relative to separate single-band fits. Three Sloan Digital Sky Survey Stripe 82
quasars, respectively favoring MCARMA(1,0), MCARMA(2,0), and MCARMA(2,1), illustrate how joint multiband modeling uses cross-band dependence to inform marginal dynamics and can yield different model-order and spectral inference. The methodology is implemented in the Python package mcarma.
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All authors Izak Schmidlkofer, Zhirui Hu, Lishan Shi, Weixiang Yu, Hyungsuk Tak
Strongly Correlated Electrons
Intrinsic anomalous Hall effect in the surface conduction regime of non-magnetic narrow-gap insulator FeSi
Yong-Cheng Pan, Hwai-Chi Lin, Li-Han Wang et al. (+6 more)
The realization of an intrinsic anomalous
Hall effect (AHE) in the absence of bulk magnetism is a rare phenomenon, requiring a spontaneous breaking of the time-reversal
symmetry within the boundary states without a thermodynamic phase transition. Here, we report a robust, zero-field-hysteretic intrinsic AHE confined to the low-temperature surface-conduction regime of the prototypical narrow-gap
insulator FeSi. By systematically investigating single
crystals grown via Czochralski and chemical vapor transport methods with controlled thicknesses and surface preparations, we demonstrate that low-temperature electrical conductivity correlates with surface roughness and anti-correlates with sample thickness, confirming the emergence of surface conducting states (CSS) below $T_{\rm CSS} \simeq 55-75\text{ K}$. Meanwhile, a robust, field-hysteretic and temperature-independent anomalous
Hall conductivity is found below $T_{\rm AHE}\simeq45-70\text{ K}$, implying the presence of
magnetic order in the surface conduction regime. Bulk
magnetic susceptibility and specific-heat measurements reveal no thermodynamic phase transitions, verifying that the sample interior remains a non-
magnetic insulator. Analyses of conductivity scaling and effective three-dimensional conductivity reveal the intrinsic nature of this surface-driven AHE, evidencing the presence of an emergent
magnetic order within the metallic surface of a non-
magnetic bulk
insulator. These findings establish FeSi as a compelling platform to investigate the interplay between strong electron correlations and anomalous
Hall transport.
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All authors Yong-Cheng Pan, Hwai-Chi Lin, Li-Han Wang, Chia-Nung Kuo, Han-Shiuan Lin, Che-Ning Yeh, Chin-Shan Lue, Anne de Visser, Yu-Te Hsu
Combinatorics
Ehrhart polynomials of cyclic polytopes as averages of zonotope Ehrhart polynomials
Masato Konoike
We prove an averaging formula for the Ehrhart
polynomial of a cyclic polytope whose vertices are given by integer parameters on the moment curve. More precisely, its Ehrhart
polynomial is the average of the Ehrhart
polynomials of an explicitly constructed family of
lattice zonotopes. Since
lattice zonotopes are magic positive, this formula implies magic positivity for these cyclic polytopes. Consequently, their $h^\ast$-
polynomials are real-rooted, and their $h^\ast$-vectors are log-concave and unimodal.
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Computation and Language
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
Shuang Sun, Guoxin Chen, Fanzhe Meng et al. (+6 more)
Recent advances in large
language models (LLMs) have enabled
agents to tackle long-horizon tasks across diverse environments. To further improve
agent performance, existing
language world models typically predict environment observations, yet reconstructing high-
entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile,
agents suffer from task-state contamination, where unsupported assumptions and outdated plans persist in history and distort subsequent decisions. We propose the
Agent-Editing World Model (AEWM), which models how
reasoning and actions shape future task progress rather than simulating tool responses. AEWM combines Action Judge to distinguish Critical, Exploratory, and Noisy decisions with State Revision to edit noisy
reasoning–action continuations from the same observed history. EditAct integrates these capabilities with real execution, directly changing the state underlying subsequent decisions rather than merely providing critiques. We train AEWM across Search, Terminal, and Software Engineering through mid-training and supervised fine-tuning. AEWM achieves 70.5% macro-F1 on our Action Judge
benchmark, exceeding the strongest frontier baseline by 10.6 points. Across six
benchmarks and three
agent backbones, EditAct improves average scores by 3.2–6.7 points over the strongest baseline. Furthermore, rejection sampling fine-tuning on verified EditAct trajectories, termed AEWM-RFT, improves over Self-RFT by 2.2–2.6 points across three domains without online AEWM guidance.
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All authors Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
High Energy Physics (Phenomenology)
Inelastic dark matter mediated by natural dark photon
Jie Tang and Pei-Hong Gu
At loop level, a dark $U(1)_X^{}$ gauge field for dark
photon can naturally acquire a tiny kinetic mixing with the standard model $U(1)_Y^{}$ gauge field for hypercharge after a $Z_2^{}$ discrete
symmetry is spontaneously broken for generating a small mass difference between two vector-like
fermions carrying the same $U(1)_Y^{}$ charge and the opposite $U(1)_X^{}$ charges of equal magnitude. To avoid an unacceptable relic density with electric charge, these vector-like
fermions can decay into the standard model charged
leptons with a TeV-scale complex dark scalar. The real and imaginary parts of this dark scalar can elegantly obtain a mass split below the MeV scale after a dark
Higgs scalar develops its
vacuum expectation value for generating a dark
photon mass of the GeV order. Therefore, the dark
photon can mediate an exothermic inelastic scattering of dark matter off
nucleus to naturally explain the recently reported event in the LUX-ZEPLIN experiment and substantially relax the stringent constraints from the IceCube
neutrino searches.
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Computer Vision
Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model
Xiwen Chen, Rigaudiere Z. Li, Zhiruo Zhou et al. (+2 more)
Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for teleport-like motion. We trace the failure to the training signal, not the representation: anchor-sparse, latent-only supervision never says where along the horizon change belongs. Decode-augmented rollout training (DART) repairs this while keeping the representation frozen, retraining only the flow with decode-path supervision. DART outperforms its latent only parent on the full protocol, restores the temporal structure of motion, and re-couples predicted motion to the scene; at larger scale it further improves prediction quality, closing nearly half the remaining gap to an oracle-informed interpolation reference. Finally, we report an unexpected finding about evaluation: pixel error alone rewards frozen predictions.
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All authors Xiwen Chen, Rigaudiere Z. Li, Zhiruo Zhou, Xiaojun Zhu, Houde Liu
Systems and Control
Two-impulse Rendezvous Planning about Thrusting Spacecraft on SE2(3)
Micah Condie, Abigaile Woodbury and James Goppert
The classical Hill–Clohessy–Wiltshire equations assume an unforced Keplerian reference trajectory, an assumption that is violated by missions requiring continuous thrust. We formulate spacecraft relative motion on the $\mathrm{SE}_2(3)$ Lie group, which encodes position, velocity, and attitude in a single
geometric state, linearize the
gravity and body-frame control mismatches between the two vehicles, and derive analytic bounds on every neglected term. Under circular coasting Keplerian assumptions the framework recovers the Hill–Clohessy–Wiltshire equations exactly. For thrusting references the state transition
matrix acquires thrust-induced attitude–translation coupling blocks that, when the two vehicles share body-frame inputs, are exact in log coordinates and independent of the attitude error, and a mismatch between the vehicles' inputs re-centers the linearization at their mean. A two-impulse rendezvous planner follows directly. A scalar coupling ratio, computable before planning, separates two regimes. Where it exceeds unity, the planner reduces the terminal error of coupling-blind planners by more than an order of magnitude, and somewhat above unity it is also more accurate than a Cartesian planner of the same computational structure; well below unity, simpler planners are adequate or better. Simulations confirm the recovery to machine precision and validate every bound along the transfer.
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Numerical Analysis
A second-order structure- and positivity-preserving convex limiting method for the Vlasov equations
Katharina Kormann, Murtazo Nazarov and Junjie Wen
In this paper, we introduce a novel second-order, positivity-preserving finite element method for the Vlasov equations using a convex limiting
algorithm. The method employs strong-stability-preserving (SSP) Runge-Kutta time integration and a tensor-product construction of the phase-space mesh for efficient high-dimensional implementations. The convex limiting
algorithm combines the robust first-order positivity-preserving graph viscosity approach with high-order residual-based viscosity stabilization to obtain a high-order positivity-preserving scheme. Both novel first-order and high-order methods applicable to high-dimensional problems such as the Vlasov system are presented. In addition, we propose a divergence-cleaning technique for Maxwell's equations to ensure that the divergence constraints of the electromagnetic fields are satisfied. Numerical experiments are provided to demonstrate the accuracy and robustness of the proposed methods.
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Differential Geometry
The J-equation at the birational minimal slope
Junbang Liu
We prove the existence, uniqueness, and partial regularity outside a proper analytic subset for the Kähler current solving the $J$-equation at the birational minimal slope. This confirms Datar–Mete–Song's
conjecture 1.5 in \cite{DMS26}. We introduce an analytic threshold, and prove its equivalence to the birational threshold introduced by Datar–Mete–Song. One of the key tools is the approximation of subsolution by Bergman's kernels, which is motivated by the work of Demailly on the approximation of plurisubharmonic functions with analytic singularities. As an application of the Bergman kernel approximation, we combine the results of Fang–Ma \cite{FM26} to give an analytic characterization of the $J$-null locus of a semistable pair $(\alpha,\beta)$. This removes the technical assumptions in \cite{L26b}.
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Differential Geometry
Existence of disks with prescribed mean curvature and contact angle in arbitrary codimension
Rui Gao and Miaomiao Zhu
Under suitable relative homotopy and boundary admissibility assumptions, we establish a min-max theory in arbitrary codimension that produces nonconstant branched immersed disks or spheres in closed Riemannian
manifolds, with controlled Morse index, where the prescribed mean
curvature type tensor and the nonorthogonal, nonconstant contact angle condition of disks along a supporting submanifold are determined by the same differential 2-form. In Euclidean space, under suitable topological and convex barrier assumptions, we obtain such disks with free boundary on a closed supporting hypersurface and a Morse index bound depending only on the ambient dimension.
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Machine Learning
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Mohamed Malek Abid and P. Michael Furlong
Vector Symbolic
Algebras project data structures into a hyperdimensional vector space through the application of their vector
algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data.
Embedding unstructured data remains an open question. We present Clifford-VAE, a variational autoencoder that learns to project data onto a Clifford
torus in arbitrary dimensions. Experiments using the MNIST, FashionMNIST, and CIFAR-10
datasets demonstrate that Clifford-VAE produces representations that are competitive with those produced by Gaussian and Hyperspherical VAEs for semi-supervised classification tasks while outperforming Gaussian and Hyperspherical counterparts in the VSA
benchmark tests of self-binding and unbinding, role-filler recovery, and bundle capacity. Clifford-VAE provides a principled technique for grounding perceptual data into a symbolic
reasoning framework, providing a new approach to a long-standing problem in the VSA literature.
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Nuclear Theory
Strange Quark Stars and Spontaneous Scalarization
Mina Mehraeen Nazdik and Zeinab Rezaei
Strange
quark matter, as one of the most extreme dense states in astrophysics, can be explored through relativistic compact objects. Hypothetical relativistic
stars composed of dense strange
quark matter are characterized by the equation of state of that matter. The spontaneous scalarization of strange
quark stars is influenced by the equation of state of strange
quark matter. Here, we investigate how the equation of state of strange
quark matter influences the spontaneous scalarization of these
stars. Considering Normal, color-
flavor-locked (CFL), and CFLm
quark matter in the bag models and applying the scalar-tensor
gravity, we calculate the structure of scalarized strange
quark stars. We investigate how the effective bag constant, perturbative QCD parameter, CFL pairing gap, and strange
quark mass control the mass-radius relation, the central scalar field, the scalarization thresholds, and the scalar charge. Our results demonstrate that strange
quark stars in scalar-tensor
gravity are viable, testable objects, and they establish a complete framework for constraining both the gravitational theory and the properties of cold
quark matter with current and future multimessenger observations.
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Statistics Applications
Statistical Methods for Estimating Probability of Detection in Structural Health Monitoring
Qizheng Xia, William Q. Meeker, Dennis Roach et al. (+2 more)
There is much interest in the potential to use structural health monitoring (SHM) technology to augment traditional nondestructive inspection (NDI) methods to improve safety, increase asset availability, and reduce maintenance and inspection costs. SHM has the potential to be used in many applications, including critical components in aircraft and pipelines. Probability of detection (POD) plays a critical role in aircraft structural integrity programs, leading to increased interest in developing methods to assess POD in SHM applications. In contrast to traditional NDI laboratory experiments involving specimens with cracks, SHM sensors are fixed, and SHM data are acquired over time as cracks grow or otherwise evolve. Thus, traditional statistical methods for assessing POD must be replaced or extended to properly handle repeated-measures data. The purpose of this paper is to review the basic statistical concepts of POD and show how these concepts can be extended or adapted for SHM-POD applications. The paper presents statistical methods for modifying and extending existing POD methods, including a simple size-of-damage-at-detection (SoDaD) method and a random-parameter (RP) method for repeated-measures data. The methods are compared using three case studies involving Piezoelectric Transducer (PZT), Carbon Nanotube (CNT), and Comparative
Vacuum Monitoring (CVM) sensor systems. Results show that the SoDaD method provides a simple approach for POD estimation with limited data, while the RP method offers enhanced modeling fidelity by utilizing repeated measurements. These methods are applicable when a scalar damage index or similar response is used to make a detection decision.
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All authors Qizheng Xia, William Q. Meeker, Dennis Roach, Seth S. Kessler, Qing Li
Astrophysics of Galaxies
Interstellar extinction of classical Cepheids from neighboring stars I. Methodology and application to 16 Galactic Cepheids
Wilma Kiviaho, Pierre Kervella, Mika Juvela et al. (+21 more)
Galactic Cepheid variable
stars are fundamental calibrators of the
cosmic distance scale through their Period-Luminosity (PL) relation. The high-precision parallaxes expected from
Gaia Data Release 4 (DR4) will enable a calibration of the Milky Way PL relation with unprecedented precision. This will place stringent requirements on interstellar extinction corrections, which remain a dominant source of uncertainty. We strive to derive highly accurate extinction values for
Galactic Cepheids. We present an innovative methodology relying on the analysis of neighboring field
stars and apply it to a pilot sample of 16 Cepheids. We identify physically near field
stars using
Gaia DR3 astrometry. For these
stars, we determine atmospheric parameters from medium- to high-resolution VLT/FLAMES spectroscopy. Fixing these parameters, we derive individual extinctions by fitting spectral energy distributions constructed from
Gaia XP spectra and infrared photometry. We then reconstruct the three-dimensional distribution of extinction and interpolate extinction to the Cepheid position. We report extinction Av, total-to-selective extinction ratio Rv, and color excess E(B-V) for all 16 Cepheids. Our method achieves a mean precision of approximately 5%, improving upon typical literature values, with no significant systematic offset detected. We demonstrate that using field
stars bypasses uncertainties associated with Cepheid variability and can provide robust extinction estimates for
Galactic Cepheids.
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All authors Wilma Kiviaho, Pierre Kervella, Mika Juvela, Alexandre Gallenne, Nicolas Nardetto, Carine Babusiaux, Rosine Lallement, Vincent Hocdé, Garance Bras, Nancy Remage Evans, Antoine Mérand, Grzegorz Pietrzyński, Gergely Hajdu, Henryka Netzel, Ricardo Salinas, Bogumił Pilecki, Cezary Gałan, Frédéric Arenou, Manon Bailleul, Katia Sivkova, Daniela González, Louise Breuval, Rajeev Singh Rathour, Brankica Apostolova
Machine Learning
Learning Collective Dynamics with Differentiable Gaussian Representations
Jianxiang Ma, Mingfu Zhang, Xiaocui Yang et al. (+3 more)
Collective responses depend on individual differences, contact opportunities, and accumulated experience. Learning their dynamics from aggregate counts requires connecting a population's response distribution to both current observations and future behavior. We introduce Differentiable Gaussian Dynamics (DGD), which learns this connection through three components: a Gaussian mixture representing heterogeneous response propensities, differentiable aggregation of contact intensity and behavioral probabilities, and feedback recurrence that updates subsequent responses. Reparameterized integration and temporal recurrence let aggregate prediction errors jointly train the distribution, observation functions, and feedback parameters. On four windows from KuaiRand-Pure and Online Retail II, DGD achieves lower joint behavioral negative log-likelihood than a DeepAR adaptation with a joint-behavior head. In Retail 2010, its one-day behavioral-count MAE is 4.71 versus 6.88 for this adaptation. Learning the distribution reduces behavioral negative log-likelihood by 10.82% relative to a fixed Gaussian in KuaiRand's standard-recommendation window; removing feedback dynamics raises joint KL from 0.0340 to 0.2577 in a controlled experiment. These results establish the value of learning population representations and their feedback process from aggregate observations. Code is available at https://github.com/OranAi-Ltd/oransim.
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All authors Jianxiang Ma, Mingfu Zhang, Xiaocui Yang, Yichen Gao, Junzhao Huang, Yuesong Hou
Combinatorics
Chvátal's conjecture: a proof from The Book
David Ellis, Yuval Filmus and Ehud Friedgut
Chvátal conjectured that every downset has a maximum-size intersecting family which is a
star, that is, consists of all members of the downset containing a fixed element. Recently, Chang, Liu and Liu gave a proof of this
conjecture, which follows as a corollary of more general results such as Kleitman's
conjecture and a version of Kahn's
conjecture (which they also prove). We give a short, direct (spectral) proof of Chvátal's
conjecture, and a proof of Kleitman's
conjecture along the same lines. We also prove a strengthening of Chvátal's
conjecture concerning the projection packing number. Finally, we propose two spectral Chvátal
conjectures which are motivated by extensive numerical experiments.
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Group Theory
Sylow synchronization in finite groups: the good case
Hong Yi Huang, Francesca Lisi, Aluna Rizzoli et al. (+1 more)
Let $G$ be a finite solvable group such that, for any
prime $p$ and any quotient $Q$ of $G$, there are two Sylow $p$-subgroups of $Q$ intersecting in $O_p(Q)$. Then, for every family $(P_i)_{i=1}^n$ of Sylow subgroups of $G$ for distinct
primes $p_1,\ldots,p_n$, there exists $x \in G$ such that $P_i \cap P_i^x = O_{p_i}(G)$ for all $i$. This covers groups of odd order, partially settling a
conjecture of the second and fourth authors and unifying old results of Bialostocki and Mann on the intersection of nilpotent subgroups. We also prove a result for all symmetric and alternating groups, completing the proof of the
conjecture for simple groups as initiated by Burness and the first author.
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All authors Hong Yi Huang, Francesca Lisi, Aluna Rizzoli, Luca Sabatini
Signal Processing
Robustness Guarantees for Optimal RIS Placement in Self-Localization Under Placement Errors
Mahdi Koloushani, Omid Rezaei, Seyed Mohammad Karbasi et al. (+2 more)
This paper studies the effect of Reconfigurable Intelligent Surface (RIS) placement errors on optimal RIS deployment for Time-of-Flight (ToF)-based self-localization. We consider a setup in which a source transmits a signal, receives the RIS-reflected echo, and estimates its own position from the corresponding ToF measurements. Under the conditions of moderate, i.i.d. Gaussian RIS position errors and i.i.d. Gaussian ToF noise, we demonstrate that the placement uncertainty manifests as a geometry-independent scaling factor in the Cramér-Rao Bound (CRB). Consequently, we prove that the optimal RIS placement remains unchanged by the presence of RIS position errors. This result is established for A-, D-, and E-optimality criteria. Simulation results verify the analysis and show that optimized RIS deployments retain their advantage even in the presence of placement uncertainty.
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All authors Mahdi Koloushani, Omid Rezaei, Seyed Mohammad Karbasi, Mohammad Mahdi Naghsh, Mohammad Mahdi Nayebi
Optics
Label-Efficient Generative Inverse Design and Design-Rule Discovery in Freeform Topological Photonics
Yuhan Zhang, Xin Xie, Bowen Song et al. (+3 more)
Topological photonic
crystals support robust, disorder-resilient light transport protected by their band-structure
topology, yet their design remains confined to a small set of
symmetry-defined templates. The surrounding freeform design space, where optimal performance and new functionality may reside, is difficult to access because of its high dimensionality and costly full-wave simulation needed for evaluating each candidate. % Here we report generative inverse design of freeform valley photonic
crystals. A label-free
diffusion prior learns the distribution of three-fold rotationally symmetric geometries from 7,689 unlabeled designs that cost 0.15~s each to produce, while a convolutional surrogate is trained on 1,666 full-wave band-gap simulations costing 4.28~min each, exploiting a 1,700-fold cost asymmetry. Across nine target band gaps up to $225~$meV, we generate and verify by full-wave simulation 270 valley photonic
crystal designs, reaching a mean absolute error of $1.4$–$6.7$~meV within the labeled range and retaining below $5.1\%$ fractional error at targets $25\%$ beyond its upper bound, while a label-conditioned
diffusion model trained on the same label data performs substantially worse. Beyond inverse design, the trained model functions as an instrument for discovering structure–property relations. At a target band gap where the labeled set contains only five designs, it generates hundreds, resolving
geometric trends that are statistically inaccessible in the training data and yielding interpretable rules for large-gap valley photonic
crystals. Our results establish surrogate-guided
diffusion as a label-efficient route to freeform topological-photonic design, and as a means of extracting design principles where physical intuition and simulation labels are both scarce.
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All authors Yuhan Zhang, Xin Xie, Bowen Song, Lixuan Chen, Liyue Shen, Hui Deng
Signal Processing
Empirical Analysis of Near-Field Beam Shaping for Blockage Events
Jy-Chin Liao, Volodymyr Vizitiv and Edward W. Knightly
Millimeter-wave (mmWave) to sub-terahertz (sub-THz) links can realize high data rates, yet are highly vulnerable to dynamic blockages due to high directivity, limited multipath, and dependence on line-of-sight (LoS) propagation. While structured and self-healing beams are promising solutions, it remains unclear how beam types perform under dynamic blockage. In this paper, we present an empirical analysis of structured near-field beams using two transmission policies. At one extreme, we study beam resilience without obstacle adaptation. At the other extreme, we empirically optimize beam parameters to study the limits of multiple beam types under idealized adaptation. We employ numerical analysis based on the angular spectrum method (ASM) and experimental validation using a sub-THz time-domain spectroscopy (TDS) platform to characterize beam performance over complete blockage events. For the scenarios considered, despite lacking self-healing, focused beams provide the strongest resilience when beam parameters are not adapted during a blockage event (i.e., when all beams are optimized only a priori). In contrast, when optimally adapted to obstacle position, curved beams provide the best performance. Lastly, although Bessel beams benefit from self-healing, this property alone can be insufficient to outperform optimized curved and focused beams. These findings provide guidance for blockage-aware beam selection and adaptation protocols.
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Machine Learning
Jiale Kang
Language models typically construct
attention values from contextual hidden states, even when some of their content may be reusable across contexts. We investigate whether
token-indexed
memory can replace the dedicated value projection when complemented by contextual information. We propose
Memory Attention (MA), which forms values by combining layer-specific
token memory with contextual keys. The
memory supplies
token-specific representations, while the keys preserve context dependence. At inference, normalization can be folded into the
memory tables, reducing value construction to lookup and addition.
Token-indexed
retrieval also enables CPU offloading with prefetching, reducing GPU parameter storage. Under matched training
token budgets and with additional
memory parameters, experiments across
attention configurations show improved
language modeling and average downstream performance.
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Plasma Physics
Retrospective on the Design and Implementation of the Milestone-Based Fusion Development Program of the U.S. Department of Energy
Scott C. Hsu, Samuel E. Wurzel and Narayan S. Subramanian
This paper provides a detailed account of the origins, objectives, design, and implementation of the Milestone-Based Fusion Development Program, a public-private-partnership (PPP) program launched by the U.S. Department of Energy's Office of Science in September 2022. The purpose of this program is multi-faceted, with policy, scientific and technological (S&T), and commercialization objectives. Tactically, the program supports privately funded fusion companies in closing S&T gaps toward delivering preliminary engineering designs of their demonstration fusion plants (aka "fusion pilot plants"). The program is a key element of a shift in U.S. fusion strategy, initiated in 2022, to accelerate fusion commercialization by leveraging PPPs. The design of the Milestone Program was underpinned by its authorizing legislation and the U.S. National Academies report Bringing Fusion to the U.S. Grid (2021), and drew inspiration from other PPP programs discussed in this paper. Finally, the paper provides the authors' perspectives and reflections on the strengths/weaknesses of the program as implemented, implementation barriers, and lessons learned, with the intent to inform future PPP programs in fusion and other energy/technology disciplines.
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Data Structures and Algorithms
Transposition achieves OPT$+O(1)$ in polynomial time for IID list update
Clayton Mizgerd
In the classical list update problem, a set of items must be stored in a list-type structure, where accessing the $i$-th element costs $i$. Items will be queried in an IID manner according to some probability distribution $p$ on the items. We want to minimize the expected cost of each query. The optimal order is to place the items in decreasing order of probability $p_1 \geq p_2 \geq \cdots$ with expected cost $\mathsf{OPT} = \sum_j j p_j$, but the probability vector $p$ is generally unknown. Thus we use a self-organizing list following the transposition rule: an item is transposed 1 position forward whenever it is queried. Coester (2026) proved that, at stationarity measure for the transposition rule, the expected cost of a query is at most $\mathsf{OPT} + 1$. However, this Markov chain may have arbitrarily slow mixing time. We prove that, for arbitrary $p$ and arbitrary initial orderings $\sigma$, after polynomially many queries in the number of items, the expected cost of a query is at most $\mathsf{OPT} + O(1)$.
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Robotics
Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward
Vaishnavi Jagabathula, P Sangeerth and Pushpak Jagtap
This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and strict actuator limits to satisfy complex high-level specifications. We denote these high-level specifications using Signal Temporal Logic (STL) and propose a novel time-aware Reinforcement Learning (RL) framework that leverages the
geometric properties of Spatiotemporal Tubes (STTs). While traditional analytical STT controllers often struggle to enforce input constraints, and existing RL approaches rely on
memory-intensive state history, our method natively overcomes both limitations. By mapping the logical and temporal complexities of the full class of STL into time-varying
geometric boundaries, we directly constrain the multidimensional system state without relying on scalar robustness metrics. Augmenting the state space with time, we train a time-aware Soft Actor-Critic (SAC)
agent using a continuous, geometry-aware reward function that eliminates the need to explicitly evaluate complex logical semantics during execution. The proposed framework offers a history-free, computationally efficient approach to learn continuous control policies that ensure robust satisfaction of specifications while strictly adhering to system input constraints.
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Computation and Language
Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following
Niklas Scholz, David Thulke, Abdallah Nasir et al. (+3 more)
Fine-tuning large
language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general
benchmarks. We ask whether these findings transfer to machine translation (MT) fine-tuning and to MT-specific instruction following (MT-IF): instructions that modify a translation, such as formality, grammatical gender, and length control. We compare methods anchored to auxiliary data, to model outputs, and to the base model parameters, first in a screening study with Llama 3.2 1B Instruct, then on Llama 3.1 8B Instruct fine-tuned on bidirectional Arabic-English or Spanish-English data. Elastic Weight Consolidation preserves general capabilities best in both stages; on the 8B Spanish model the average score on general
benchmarks drops 1.7 points versus 11.0 for standard fine-tuning, yet its scores for formality and grammatical gender control remain close to standard fine-tuning. Only data mixing with control-task examples preserves these controls, but its gains do not transfer to unseen
prompts for the same task.
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All authors Niklas Scholz, David Thulke, Abdallah Nasir, Will Allred, Evgeny Matusov, Hermann Ney
High Energy Physics (Phenomenology)
Simulating the emission source function in very peripheral relativistic heavy-ion collisions
Angel Reina Ramírez, Volodymyr Magas and Juan M. Torres-Rincon
We present a microscopic femtoscopic investigation of the space-time emission source in low-multiplicity (80–90% centrality) $\text{Pb}-\text{Pb}$ collisions at $\sqrt{s_{NN}} = 5.02~\text{TeV}$, using a hybrid transport framework coupling SMASH initial conditions, 3+1D viscous hydrodynamics (vHLLE), and a SMASH hadronic cascade afterburner. Access to the full space-time
evolution of the produced particles allows for the direct reconstruction of emission source functions in both the Longitudinally Comoving System and the Pair Rest Frame. We study identical pairs of
mesons ($\pi\pi$, $KK$) and
baryons ($pp$), as well as cross-species combinations, while explicitly isolating the contributions from
primordial particles, hadronic rescattering, and long-lived resonance decays. Our analysis shows that simple Gaussian fits are inadequate for precision source analyses, whereas a combined Gaussian plus exponential parametrization better captures the compact core and the extended tail. Finally, the extracted Gaussian core radii, $R_{\text{core}}$, for our very peripheral collisions demonstrate an approximate $m_T$-scaling behavior rather similar to the one observed in pp reactions.
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Robotics
PointCast: One World Model for Rigid, Articulated, and Deformable Object Manipulation
Hantao Ye, Ross Worobel, Zhuoli Xie et al. (+4 more)
World models are useful for robotic manipulation because
robots can predict how actions change the states of objects before executing them. We present PointCast, a point-set world model that spans rigid, articulated, and deformable object manipulation. Its state is a set of 3D points on the object and the end-effector, mesh-free and
topology-agnostic. Each point keeps its identity and is supervised on its own trajectory, which teaches the model where every point goes rather than only the shape the points form. Its backbone is a
diffusion transformer that denoises a short window of future point positions, conditioned on the points' recent history and the commanded end-effector motion. The backbone's
attention alternates between local and global, and cross-
attention to the end-effector carries the coupling. This one architecture at 19.8M parameters and one training recipe cover four regimes, rigid objects, cloth, rope, and multi-joint cabinets, with a separate checkpoint trained for each. Trained on randomized simulation and scored against four baselines on the same metric, it is best on three of four regimes and second on rigid. Trained on a real-world
robot teleoperation
dataset, it has the lowest mean error in four of its six categories, is second in the other two, and improves on the
dataset's own model in all six; zero-shot, its simulation checkpoints are best on two of four captures. Frozen inside sampling-based model-predictive control at one network evaluation per window, it plans four simulated tasks over 64 episodes, competitive with or outperforming every baseline on each. Project website at https://pointcast-wm.github.io.
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All authors Hantao Ye, Ross Worobel, Zhuoli Xie, Mingen Li, Houjian Yu, Youngjin Hong, Changhyun Choi
Theoretical Economics
Cursed Rationalizability
Shani Cohen and Shengwu Li
Cursed players understand the distribution of opponents' actions and the distribution of the payoff-relevant state, conditional on their own information, but respond as if the two were independent. Cursed
equilibrium additionally requires statistically correct beliefs about the distribution of play. We propose a solution concept that separates the bias from
equilibrium: cursed rationalizability iteratively deletes every action that is not a cursed response to a
conjecture about opponents' surviving play. The cursed-rationalizable set is the interim correlated rationalizable set of a virtual game, in which payoffs are averaged over the state. Under strategic complements, cursed rationalizability yields the same bounds on behavior as cursed
equilibrium. A cautious refinement, the analog of admissibility, delivers sharp predictions in the laboratory games that documented the winner's curse.
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Quantum Physics
Quantum score matching with applications to learning thermal states
Yulong Dong and Jiaqi Leng
Score matching has driven major advances in classical generative learning by enabling models to learn from data without evaluating intractable normalization constants, or partition functions. Yet, extending this principle to
quantum learning requires rethinking its foundations, as
quantum states are described by noncommuting density operators rather than scalar probabilities. The noncommutativity creates fundamental challenges not only in defining
quantum scores, but also in developing a training framework with efficient
circuit implementations and rigorous theoretical guarantees. In this work, we bridge this gap by establishing a general
quantum score-matching framework with end-to-end theoretical guarantees. Applied to Gibbs-state learning, our approach avoids additional thermal-state preparation and achieves information-theoretically optimal sample complexity in the high-temperature regime for
Hamiltonians with bounded locality and interaction degree. This positions score matching as a new route to state-of-the-art performance in learning
quantum Gibbs states. Beyond these theoretical results, numerical simulations show that our method remains effective even when gradients are estimated inaccurately under limited measurement budgets. Experiments on IBM
quantum hardware further demonstrate that
quantum score matching is NISQ-friendly: without any error mitigation or correction, it reduces the relative
Hamiltonian-parameter error from 64% to approximately 10%. Together, these results extend score matching into an experimentally realizable paradigm for
quantum-state learning.
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