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All of arXiv

Friday 18 September

Applied Physics

Boundary-condition identification from a scale-invariant frequency ratio in shear-deformable and nonlocal beams and plates

Ak{\i}n Oktav

The ratio of a structure's first two natural frequencies depends on its boundary conditions but not on its size, material density, or absolute stiffness, which multiply every frequency equally and cancel in the quotient. We use this to identify the boundary conditions of a small-scale beam or plate from two ratio measurements, without knowing its exact dimensions or material. The paper first establishes how the ratio $r=\omega_2/\omega_1$, and the fractional shift $\Delta r$ it undergoes when a boundary condition changes, respond to the two effects that matter at small scale: transverse shear deformation, through Timoshenko beam and Mindlin plate theory, and small-scale elasticity, through Eringen's nonlocal Timoshenko model. Using a locking-free Rayleigh-Ritz beam solver and a finite-element Mindlin plate solver, each checked against benchmarks, we find an asymmetry. The absolute ratio moves appreciably with thickness, by 12 to 14 per cent over the shear-deformable range, and with orthotropy, by 6 to 9 per cent, but only weakly with nonlocality; the shift $\Delta r$, by contrast, varies by less than 0.2 per cent over the full nonlocal range and by a few per cent under thickness change. Because $\Delta r$ is largely independent of these details, a measured shift identifies which boundary-condition transition a deliberate change of support has produced, with a resolution we quantify and verify against molecular-dynamics data. Two spring continua then extend the method to estimating support compliance, with the non-uniqueness of the tip-support case stated explicitly. The method needs no value of the small-scale parameter or the material constants.
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Computation and Language

Do Student LLMs Inherit OOD Robustness? Invariance-Weighted Distillation for Reliable Knowledge Transfer

Dileesha Kannangara and Sanghamitra Dutta

Knowledge distillation (KD) aims to compress high-performance teacher LLMs into lightweight students. However, distilled students often exhibit substantial performance degradation in out-of-distribution (OOD) settings, a critical gap that remains underexplored. We identify two compounding mechanisms causing OOD performance degradation: (1) data spuriousness: students can learn spurious correlations in the distillation dataset over genuine causal relationships; and (2) teacher capability: standard KD treats all samples uniformly, ignoring whether the teacher is guided by causal features or misled by spurious shortcuts on a given sample. To address these challenges, we propose Invariance-Weighted Distillation (IWD), a theoretically grounded framework that dynamically reweights training samples using an estimate of the teacher's causal reliance derived from prediction invariance across multiple synthetic environments. IWD perturbs spurious cues while preserving core semantics, assigning higher distillation weights to samples whose teacher predictions remain invariant, indicating greater reliance on causal rather than spurious features. We theoretically show that IWD reduces the student's Spurious-to-Causal (S2C) gradient ratio compared to standard uniformly weighted KD, driving the student toward more invariant representations. Experiments on four NLP benchmarks (MNLI, SQuAD-v2, CoNLL-2003 NER, and SST-2) across two model families (DeBERTa-v3 and Qwen-2.5) demonstrate that IWD consistently outperforms strong KD baselines on OOD evaluations while maintaining competitive in-distribution (ID) performance. Specifically, IWD achieves the highest accuracy in 15 out of 16 OOD benchmarks and improves average OOD performance over standard KD by 4.34 percentage points on NLI and 14.94 percentage points on QA.
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Differential Geometry

Chain-level decoupling of the physical heterotic $G_2$ deformation complex at the standard embedding

Bram Brongers

The physical deformation complex of heterotic $G_2$ compactifications couples instanton deformations to geometric fields and their first-order $\alpha'$ corrections. We give an explicit chain-level decoupling of its first-order coefficient complex at the minimal standard embedding, where the gauge bundle is the tangent bundle, the gauge connection is the Levi–Civita connection, and the background $G_2$ structure is torsion-free. The first Atiyah coupling is null-homotopic through a skew covariant derivative extending the induced connection variation. A second homotopy, given by the covariant codifferential of the skew part of an endomorphism-valued cochain, removes the coupling from the bundle sector to the geometric sector. The residual geometric curvature term cancels against the contribution of the first transformation. These identities hold on arbitrary cochains in every canonical degree and yield mutually inverse differential-operator chain maps. Consequently, the cohomology decomposes into instanton and geometric coefficient cohomologies in all degrees. The argument requires only $\mathrm{Hol}(g)\subseteq G_2$. To our knowledge, no previous explicit all-degree splitting removes the internal gauge and geometric couplings of this physically reduced standard-embedding coefficient complex.
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Quantum Physics

Generating Dicke State Graphs

Rebekah Herrman

Graph theory is a powerful tool in quantum computing, with applications ranging from quantum circuit synthesis and optimization to entanglement mapping. Recent work has shown how one can use edge-colored graphs to model photonic experiments that generate GHZ and W states. However, the latter work also proved that verifying that a graph models a Dicke state experiment is coNP-complete. In this work, we provide families of graphs that generate $|D_{k}^a\rangle \otimes |0\rangle^{\otimes b }$, where $b = |a-2k|$ is the number of spectator modes. The graph setup consists of a doubled complete subgraph on $a$ vertices and a collection of auxiliary vertices. We prove that every coincidence carries exactly $k$ excitations, every weight-$k$ computational basis state on bitstrings of length $a$ is realized, and each of those bitstrings is realized exactly $(n/2)!$ times, where $n = a+b$. Since verifying the Dicke FORALL condition is coNP-complete in general, constructing explicit families that provably generate Dicke states is of interest.
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Statistical Methodology

Simulation-based Criticism of Complex Biological Models with Neural Evidence Estimation

Prayag Chatha, Fan Bu and Jon Zelner

In statistical modeling, the likelihood is the basis for both inference and criticism, which constitute the two stages of data analysis. However, many realistic scientific models lack computationally tractable likelihoods. Increasingly, these are implemented as computer simulations. New deep learning-based methods for simulation-based inference (SBI) have demonstrated the ability to efficiently and accurately calibrate complex simulation models. However, in this setting, the standard tools for comparing models and assessing their potential limitations are often unavailable. We propose a new method for simulation-based model criticism, Neural Evidence Estimation. NEE trains a normalizing flow on simulated data to learn an estimate of the marginal likelihood (a.k.a. evidence). These estimates can be used to diagnose model misspecification and perform model comparison without evaluating (or approximating) a model's likelihood. Compared to related techniques designed for either of these tasks, NEE is quick, robust, and modular. We demonstrate our methodology through applications to infectious disease modeling. Here, model criticism is difficult yet imperative: infectious disease modelers often face a choice among several plausible models featuring intractable likelihoods. We first validate NEE's ability to discern incorrect transmission models through simulation experiments. We then perform data analysis of a historic multi-wave influenza epidemic, where we use SBI and NEE to rigorously compare several competing mechanistic theories of reinfection. Overall, NEE provides a unifying framework for simulation-based criticism, advances efficient multi-model data analysis, and helps improve the reliability of SBI.
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Computer Vision

AdaMerge: Tuning-Free Patch Compression for Multi-Vector Visual Document Retrieval

Jianxin You and Kun Ni

Multi-vector visual document retrieval (VDR) models such as ColPali and ColNomic achieve strong accuracy by representing each document with hundreds to thousands of patch-level embeddings, at substantial storage and latency cost. Existing compression methods either prune unimportant patches or merge similar ones into clusters; the recent state-of-the-art merging method Prune-then-Merge (PtM) consistently outperforms pruning-only baselines at high compression, but requires a per-dataset cluster budget m to be tuned by grid search. We observe that the merge-cosine sequence produced by hierarchical clustering exhibits a sharp cliff separating mergeable redundancy from salient signal, and that the location of this cliff is concentrated in a narrow band across more than 11,000 documents from 14 datasets. This suggests the merge boundary can be detected per document rather than tuned per dataset. Building on this observation, we propose AdaMerge, a plug-and-play compression method that (i) detects each document's own cliff via gap analysis on the merge-cosine trajectory, and (ii) builds attention-weighted cluster centroids to preserve salient signal. On the long-document benchmark ViDoRe-V2 (4 datasets, two backbones), AdaMerge significantly outperforms tuned PtM across the operating range (p < 10^-4); on the short-document benchmark ViDoRe-V1 (10 datasets, two backbones), where all merging methods are already near-lossless, AdaMerge matches tuned PtM without any per-dataset tuning. AdaMerge adds only about 10 ms per document and exposes a single global hyperparameter shared across all datasets and backbones.
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Probability

Uniform in time weak convergence for a Fleming-Viot particle system with hard killing

Pierre Cardaliaguet, Marco Cirant, Joe Jackson et al. (+1 more)

This paper is concerned with the Fleming-Viot particle system introduced by Burdzy, Hołyst and March (2000). In this model, $N$ Brownian particles evolve independently in a bounded domain $D$ until one of the particles reaches the boundary. Then, the particle which has hit the boundary instantaneously jumps to the location of one of the other particles, chosen uniformly at random. Burdzy, Hołyst and March showed that when $N$ tends to infinity, the empirical measure of the system converges to a solution to the heat equation on $D$ with Dirichlet boundary conditions, renormalized to have total mass $1$. Our main result is a sharp, uniform-in-time, quantitative version of this result. We employ the method of weak propagation of chaos, which necessitates a careful study of the (backward) Kolmogorov equations associated to the $N$-particle systems, and the infinite-dimensional transport equation whose characteristics are given by renormalized solutions of the Dirichlet heat equation. The proofs are entirely analytical, and most of the technical effort is devoted to building barrier functions which are used to control the singular behavior of the system when most of the particles approach the boundary.
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All authors Pierre Cardaliaguet, Marco Cirant, Joe Jackson, Panagiotis E. Souganidis

Number Theory

Almost Trivial Units in Group Rings

Efrat Shani and Anupam Srivastav

We find all ring of integers, R, and finite groups G, such that RG has almost trivial units. Moreover, for such a group ring we determine the Swan subgroup of its locally free classgroup

High Energy Astrophysical Phenomena

The chirping of Lense-Thirring precession in tidal disruption event accretion flows

Andrew Mummery and Eliot Quataert

Tidal disruption event X-ray light curves do not appear to show clear signs of periodic modulation. This is naively somewhat surprising since the stars that are disrupted originate at large scales in the galaxy where they cannot know about the orientation of the black hole spin axis. This should lead to the formation of a misaligned disk that precesses due to Lense-Thirring torques, modulating X-ray emission from the inner disk regions. We argue that in fact the properties which are required for solid-body precession, namely a thick $(H/R\sim {\mathcal O}(1))$ disk, naturally lead to rapid precession period change, with a per-period increase of $\Delta T_{\rm prec}/T_{\rm prec} \sim {\mathcal O}(1-10)$, as the disk spreads to larger radii to conserve angular momentum. In other words the precession of thick TDE disks is aggressively chirped, washing out any possibility of observing multiple cycles other than for fine tuned regions of parameter space. The global disk alignment timescale is equally strongly chirped by the exact same mechanism, meaning that TDE disks will not in general align with the black hole spin axis during a super-Eddington phase, and should generically show global quasi-steady warp profiles at the beginning of any thin disk phase. These results have important implications for interpreting timing features associated with TDE disks, including models for quasi-periodic X-ray eruption timing phenomenology, and the observational properties of TDE disks in the initial transition to the thin disk phase.
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Materials Science

Compressive Splitting in Brittle Solids: The Inverse of Wrinkling in Sheets

Maryam Khodadad, Francois Barthelat, John D. Clayton et al. (+2 more)

Axial splitting is the dominant failure mode of brittle solids under compression, yet its mechanical origin remains unclear. We show that clamped loading platens suppress lateral Poisson expansion, generating boundary-induced tensile stresses at the specimen interior – the compressive analog of wrinkling in stretched sheets. This mechanism provides a predictive strength law, relating axial splitting to tensile strength, geometry, and confinement pressure. This is validated against diverse materials ranging from rocks to ceramics, establishing axial splitting as a geometry-controlled elastic process rather than a stochastic flaw problem.
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All authors Maryam Khodadad, Francois Barthelat, John D. Clayton, George Gazonas, Kaushik Dayal

Algebraic Geometry

Maximal singular curves over finite fields from elliptic and hyperelliptic curves

Cesar Hilario, Rodrigo Salomão and Renato Vidal Martins

We construct explicit families of maximal singular curves over finite fields. A maximal singular curve is a curve that attains the Aubry-Perret bound, which is the natural extension of the Hasse-Weil-Serre bound on the maximum number of rational points on a smooth curve over a finite field. Starting from a smooth elliptic or hyperelliptic curve $\tilde{C}$ over $\mathbb{F}_{q}$ ($q$ odd), we generate singular curves $C$ with non-split singularities or cusps. In our approach we use linear projections of geometric embeddings, and we apply Stöhr's embedding of hyperelliptic Gorenstein curves and the Rosa-Stöhr's theory of trigonal Gorenstein curves. Some of our models attain the Aubry-Perret bound, whenever their normalization is maximal, and the construction is applied to the Tafazolian and Tafazolian-Torres smooth maximal curves to produce the desired family. Finally, we determine the gonality over $\mathbb{F}_q$ of most of the curves we construct.
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Instrumentation and Methods for Astrophysics

Initial modeling and testbed feasibility studies of WaveDriver: a laser guide star AO system for HWO

Dominic F. Sanchez, Benjamin L. Gerard, Peter Waswa et al. (+16 more)

The Habitable Worlds Observatory (HWO) will require $>100\times$ greater wavefront stability than achieved by JWST, pushing conventional telescope architectures to picometer-level performance limits. WaveDriver is a mission concept that relaxes these constraints by employing an external laser guide star (LGS) spacecraft flying in formation with HWO to enable wavefront sensing and control. Early results from the High Contrast Testbed demonstrate feasibility of required stability using coronagraphs, multiple deformable mirrors, and a suite of wavefront sensors. We report mission design analyses, initial testbed stability metrics, compare sensor performance, and present an optical model that quantifies LGS brightness, conjugation errors, and field errors arising from finite beacon separation.
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All authors Dominic F. Sanchez, Benjamin L. Gerard, Peter Waswa, Alex Geringer-Smith, Aditya Sengupta, Alexx Perloff, Michael Messerly, William Moore, Matthew Cook, Paul Pax, Cesar Laguna, Mathew DeMartino, Kevin Bundy, Rebecca Jensen-Clem, Aaron Lemmer, S. Mark Ammons, Megan Eckart, Lisa Poyneer, Eric Strang

High Energy Physics (Experiment)

First measurement of the forward rapidity dependence of $W$ boson transverse helicity fractions

LHCb collaboration: R. Aaij, M. Abdelfatah, A.S.W. Abdelmotteleb et al. (+1187 more)

The transverse helicity fractions of $W$ bosons are measured as a function of the $W$ boson rapidity, $y_{W}$, in the range $0 \leq y_{W} \leq 5$ using $W\to\mu\nu_{\mu}$ decays in $pp$ collisions at $\sqrt{s}$ = 13 TeV recorded by the LHCb experiment and corresponding to an integrated luminosity of $5.1$ fb$^{-1}$. The fractions are extracted from a template fit to the muon transverse momentum and pseudorapidity. The results show a strong rapidity dependence and agree with next-to-leading-order Standard Model predictions, providing the first determination of the transverse helicity fractions of $W$ bosons in the forward region.
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All authors LHCb collaboration: R. Aaij, M. Abdelfatah, A.S.W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A.A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C.A. Aidala, S. Akar, K. Akiba, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, A.A. Alves Jr, S. Amato, J.L. Amey, Y. Amhis, Z. Amos, L. An, L. Anderlini, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J.A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J.J. Back, M. Bai, Z.B. Bai, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, I.R. Barbosa, W. Barker, R.J. Barlow, M. Barnyakov, S. Baron, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P.B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I.B. Bediaga, N.A. Behling, S. Belin, A. Bellavista, I. Belyaev, G. Bencivenni, E. Ben-Haim, J.L.M. Berkey, R. Bernet, A. Bertolin, L. Bertsch, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M.S. Bieker, N.V. Biesuz, A. Biolchini, M. Birch, F.C.R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J.E. Blank, S. Blusk, J.A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, C. Bolognani, R.B. Bonacci, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J.T. Borsuk, E. Bottalico, S.A. Bouchiba, M. Bovill, T.J.V. Bowcock, A. Boyer, C. Bozzi, J.D. Brandenburg, A. Brea Rodriguez, N. Breer, C. Breitfeld, J. Brodzicka, J. Brown, E. Buchanan, M. Burgos Marcos, C. Burr, E. Butera, C. Buti, J.S. Butter, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, Y. Cai, A. Caillet, R. Calabrese, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J.I. Cambon Bouzas, P. Campana, A. Campomagnani, A.C. Campos, A.F. Campoverde Quezada, Y. Cao, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A.J. Chadwick, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, S. Chernyshenko, X. Chiotopoulos, G. Chizhik, V. Chobanova, A. Christakakis, M. Chrzaszcz, Y. Chu, V. Chulikov, P. Ciambrone, X. Cid Vidal, P. Cifra, P.E.L. Clarke, M. Clemencic, H.V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, A. Codovini, C.S. Codovini, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, A. Corallo, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, G.C. Costantino, C. Cotirlan, J. Cottee Meldrum, B. Couturier, D.C. Craik, N. Crepet, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C.L. Da Silva, X. Dai, J. Dalseno, C. D'Ambrosio, G. Darze, A. Davidson, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, F. De Gregorio, E. De Lucia, J.M. De Miranda, L. De Paula, A. De Robertis, E. De Santis, M. De Serio, P. De Simone, F. De Vellis, J.A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Demaire-Lepape, J. Deng, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, S. Ding, Y. Ding, L. Dittmann, J.F. Diverchy, A.D. Docheva, A. Doheny, C. Dong, F. Dordei, J. Dorta Moreno, A.C. dos Reis, J. Dos Santos Oliveira, A.D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M.M. Duras, J.M. Durham, K. Duwe, A. Dziurda, S. Easo, E. Eckstein, U. Egede, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, T. Evans, F. Fabiano, S. Faghih, L.N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, C. Feng, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, B. Fernandez Rodino, J. Fernandez-John, A.D. Fernez, F. Ferrari, F. Ferreira Rodrigues, R.A. Fini, R. Fiorenza, M. Fiorini, M. Firlej, D.S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, M. Fontes Vaz, L.A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D.A. Friday, J. Fu, Y. Fu, Q. Führing, T. Fulghesu, M. Fulghieri, G. Galati, M.D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T.Q. Gao, Y. Gao, Y. Gao, Y. Gao, L.M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, J. George, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F.I. Giasemis, V. Gibson, H.K. Giemza, A.L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M.A. Giza, F.C. Glaser, V.V. Gligorov, A. Glioti, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, A. Golutvin, S. Gomez Fernandez, A.G. Gomez Mongui, W. Gomulka, F. Goncalves Abrantes, I. Gonçales Vaz, M. Goncerz, G. Gong, S. Gong, J.A. Gooding, C. Gotti, E. Govorkova, J.P. Grabowski, L.A. Granado Cardoso, R. Grande Quartieri, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A.T. Grecu, N.A. Grieser, L. Grillo, C. Gu, M. Guarise, L. Guerry, M. Guittiere, A.-K. Guseinov, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P.M. Hamilton, Q. Han, S. Han, X. Han, S. Hansmann-Menzemer, N. Harnew, T.J. Harris, L. Hartman, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R.D.L. Henderson, A.M. Hennequin, K. Hennessy, A. Henrot, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D.C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R.J. Hunter, D. Hutchcroft, M. Idzik, P. Ilten, A. Iohner, S. Jacevicius, H. Jage, S.J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y.J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C.R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, A. Kauniskangas, J.W. Kautz, M.K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, S. Kholodenko, V. Kholoimov, G. Khreich, F. Kiraz, T. Kirn, V.S. Kirsebom, N. Kleijne, A. Kleimenova, D. Klekots, K. Klimaszewski, M.R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, O. Kravcov, M. Kreps, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, A. Kupsc, A. Kurzina, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, G. Lanfranchi, C. Langenbruch, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, M. Lehuraux, C. Lemettais, E. Lemos Cid, O. Leroy, T. Lesiak, E.D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, L. Li, P. Li, P.-R. Li, Q. Li, T. Li, T. Li, W. Li, Y. Li, Y. Li, Y. Li, Z. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, J. Lin, S. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F.L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F.C.L. Lopes, J.H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, M. Lupberger, E. Luppi, K. Lynch, J. Lyu, S. Lyu, X.-R. Lyu, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L.M. Mackey, L.R. Madhan Mohan, M.J. Madurai, D. Magdalinski, J.J. Malczewski, S. Malde, L. Malentacca, G. Manca, C. Mancuso, R. Manera Escalero, A. Mangalasseri, F.M. Manganella, R. Mangrulkar, D. Manuzzi, S. Mao, D. Marangotto, J.F. Marchand, R. Marchevski, U. Marconi, L. Mareso, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A.M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, C. Martinez, A. Martinez Armas, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, C. Matteuzzi, K.R. Mattioli, L. Matzner, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N.T. McHugh, A. McNab, R. McNulty, B. Meadows, S.E.R. Medaer, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F.M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, S. Mico, M. Mikhasenko, D.A. Milanes, A. Minotti, E. Minucci, B. Mitreska, D.S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R.D. Moise, E.F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M.J. Morello, M.P. Morgenthaler, A. Moro, J. Moron, W. Morren, A.B. Morris, A.G. Morris, R. Mountain, Z. Mu, N. Muangkod, E. Muhammad, F. Muheim, M. Mulder, K. Müller, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, G. Napoletano, I. Nasteva, M. Needham, N. Neri, S. Neubert, N. Neufeld, J. Nicolini, D. Nicotra, E.M. Niel, L. Nisi, Q. Niu, B.K. Njoki, P. Nogarolli, P. Nogga, J. Nombela Royo, C. Normand, A. Novo Cal, J. Novoa Fernandez, G. Nowak, H.N. Nur, A. Oblakowska-Mucha, T. Oeser, O. Okhrimenko, R. Oldeman, N. Oldman, F. Oliva, E. Olivart Pino, M. Olocco, R.H. O'Neil, J.S. Ordonez Soto, D. Osthues, J.M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K.O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, L. Palombini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, L. Paolucci, A. Papanestis, M. Pappagallo, L.L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C.J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, H. Pereira Da Costa, M. Pereira Martinez, C. Perez, A. Perez Casas, P. Perret, A. Perrevoort, A. Perro, M.J. Peters, A. Petkovic, K. Petridis, A. Petrolini, S. Pezzulo, J.P. Pfaller, H. Pham, L. Pica, E. Picatoste Olloqui, M. Piccini, L. Piccolo, F. Piernas Diaz, B. Pietrzyk, R.N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V.M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J.R. Pybus, Q. Qian, W. Qian, N. Qin, R. Quagliani, R.I. Rabadan Trejo, B. Rachwal, R. Racz, J.H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, G. Ramsey, M.S. Rangel, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P.K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J.A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogovskiy, D.L. Rolf, P. Roloff, A. Romano, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, W. Rose, M. Rotondo, M.S. Rudolph, G. Ruggiero, M. Ruiz Diaz, J. Ruiz Vidal, J. Ruz Armendariz, J.J. Saavedra-Arias, J.J. Saborido Silva, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, H. Schumacher, M.H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, B. Sevilla Sanjuan, Y. Shang, D.M. Shangase, R.S. Sharma, L. Shchutska, T. Shears, S. Shelton, J. Shen, Z. Shen, S. Sheng, B. Shi, J. Shi, Q. Shi, W.S. Shi, E. Shmanin, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M.W. Slater, E. Smith, M. Smith, M. Smith, L. Soares Lavra, M.D. Sokoloff, F.J.P. Soler, A. Solomin, K. Solovieva, N.S. Sommerfeld, R. Song, Y. Song, Y. Song, Y.S. Song, F.L. Souza De Almeida, G. Souza De Castro, B. Souza De Paula, K.M. Sowa, E. Spadaro Norella, E. Spedicato, J.G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, F. Suljik, J. Sun, L. Sun, M. Sun, D. Sundfeld, P. Svihra, V. Svintozelskyi, J. Swallow, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y.T. Tang, M.D. Tat, J.A. Teijeiro Jimenez, F. Terzuoli, F. Teubert, E. Thomas, D.J.D. Thompson, A.R. Thomson-Strong, R. Thornton, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T.T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Torlai, L. Toscano, D.Y. Tou, G. Tuci, N. Tuning, L.H. Uecker, A. Ukleja, A. Upadhyay, B. Urbach, A. Usachov, U. Uwer, V. Vagnoni, A. Vaitkevicius, A. Valassi, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C.B. Van Hulse, R. Van Laak, M. van Veghel, P. Varrella, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J.J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F.C. Volle, D. vom Bruch, K. Vos, C. Vrahas, J. Wagner, J. Walsh, N. Walter, E.J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, C. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N.W. Wang, X. Wang, X. Wang, X. Wang, X.W. Wang, Y. Wang, Y. Wang, Y. Wang, Y.H. Wang, Z. Wang, Z. Wang, J.A. Ward, M. Waterlaat, N.K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B.D.C. Westhenry, A.S. White, C. White, M. Whitehead, E. Whiter, A.R. Wiederhold, D. Wiedner, M.A. Wiegertjes, C. Wild, G. Wilkinson, M.K. Wilkinson, M. Williams, M.J. Williams, M.R.J. Williams, R. Williams, S. Williams, Z. Williams, F.F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S.A. Wotton, H. Wu, J. Wu, T. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T.X. Xing, A. Xu, L. Xu, M. Xu, R. Xu, Z. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, Z. Yang, H. Yeung, H. Yin, X. Yin, C.Y. Yu, J. Yu, K. Yu, X. Yuan, Y Yuan, S. Zalambani, J.A. Zamora Saa, F. Zangari, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, S.H Zeng, C. Zhang, C. Zhang, D. Zhang, J. Zhang, L. Zhang, Q.Z. Zhang, R. Zhang, S. Zhang, S.L. Zhang, Y. Zhang, Z. Zhang, J. Zhao, M. Zhao, Y. Zhao, A. Zhelezov, S.Z. Zheng, X.Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, V. Zhovkovska, L.Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, T. Zies, D. Zuliani, X. Zuo

Machine Learning

The Ups and Downs of Backprop Weights

Giuseppe Chindemi and Benjamin F. Grewe

Backpropagation (BP) has driven the remarkable success of modern deep learning by enabling large hierarchical networks to learn complex functions end-to-end. Yet it does not by itself determine how parameters should be organized so that functional components can be reused and adapted selectively. For example, object recognition and motion prediction may depend on overlapping parameter sets, making them difficult to isolate or modify independently. We call this condition weight entanglement. Modern architectures dynamically select which parts of a network process each sample: nonlinearities gate units, attention selects interactions, and Mixture-of-Experts architectures route inputs to modules. Yet such selection does not ensure that the same functional component remains linked to an identifiable parameter set across samples. We propose weight operators: parameterized modules that implement reusable functional components and can be composed at inference to form the function required by each sample. Learning proceeds in two stages: the model first infers the required operator composition, then updates only the selected operators' parameter sets. Vector Networks (VNs) provide one implementation. They couple operator selection to local error-driven updates within each layer and show that learned operators can be reused in combinations absent from training while updates remain restricted to the selected parameter sets. This provides a basis for testing functional parameter identifiability: whether an operator remains linked to the same functional component during learning. We argue that functional parameter identifiability may provide an organizing principle for models that systematically reuse and recombine learned functions while adapting only the components that need to change.
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Computation and Language

Correct Diagnosis, Better Feedback: A Symbolic-Verifier for Faithful LLM Tutoring Feedback in Logic Proofs

Tahreem Yasir, Arnav Mody, Xioayi Tian et al. (+1 more)

Effective LLM tutoring depends on correctly identifying the specific error in a student's reasoning before generating feedback. We study this problem in propositional-logic proof tutoring, where student actions can be checked against formal inference rules. We introduce a verifier-grounded architecture that separates diagnosis from language generation. Using 600 balanced student actions, we compare a zero-shot LLM detector, a fine-tuned detector, and a symbolic verifier. Each diagnosis is processed by shared rationale and feedback agents, isolating the effect of the initial diagnosis. The zero-shot detector achieves a macro-F1 of 0.191; fine-tuning raises this to 0.709 but retains systematic errors between structurally related classes. Rationales generally preserve the diagnosis supplied to them, showing that an incorrect diagnosis can be faithfully propagated through the pipeline. Feedback can likewise remain faithful to its rationale, non-revealing, and pedagogically appropriate while addressing the wrong error. Verifier-grounded feedback achieves the highest diagnostic correctness, and expert ratings largely uneven with the automatic feedback evaluations. These findings show that apparent feedback quality can conceal upstream diagnostic errors and that faithfulness must be evaluated separately from correctness. Our code is publicly available
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All authors Tahreem Yasir, Arnav Mody, Xioayi Tian, Tiffany Barnes

Plasma Physics

Proton Heating Rates in the Inner Heliosphere: Helios Observations

Alaa Fayad, Anna Tenerani and Chadi Salem

Solar wind protons exhibit a temperature anisotropy that deviates from the double-adiabatic prediction, suggesting an additional energy source. To investigate solar wind heating mechanisms, we analyze Helios 1 and 2 data and characterize the radial evolution of the adiabatic invariants and anisotropic heating rates of fast wind protons, considering both the bulk population and its core and beam components separately. Unlike previous studies, we account for proton heat fluxes and deviations from the Parker spiral caused by large-amplitude fluctuations, which causes significant differences in the parallel heating rates compared to earlier results. Although earlier work has questioned the reliability of power laws to estimate heating rates and proposed as a more robust approach the investigation of the radial evolution of adiabatic invariants, we show that the two approaches yield consistent results when applied to the same dataset. Our analysis shows that all proton populations considered require net perpendicular heating, consistent with earlier studies. In contrast, the parallel energy evolution differs between populations. Core protons require parallel heating, whereas beam protons undergo parallel cooling although the total proton population undergoes a net parallel cooling. This behavior is consistent with the combined action of turbulence and core-beam kinetic instabilities, which together can drive preferential perpendicular heating while simultaneously produce parallel cooling in the fast solar wind. Core proton parallel heating could instead be a signature of Alfvén wave decay.
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Category Theory

The category of nominal sets is locally monoidal closed

Fahimeh Bayeh, Peng Fu and Peter Selinger

The category Nom of nominal sets was proposed by Pitts and Gabbay as a setting for the semantics of abstract syntax with variable bindings. It is well-known that Nom is a topos, also known as the Schanuel topos, and in particular it follows that Nom is locally closed, i.e., every slice category Nom/X is cartesian-closed. In this paper, we show that Nom has a much stronger property: every monoidal (closed) structure on Nom induces a monoidal (closed) structure on all slice categories Nom/X. One monoidal closed structure of particular interest on Nom is the separated product A * B, whose right adjoint A -* B is called the separated function space. In particular, it follows that Nom is locally separatedly closed.
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Rings and Algebras

On the positive semidefinteness of a class of Hermitian Cauchy-like matrices

Augusto Ferrante

We establish the positive semidefiniteness of a class of Hermitian Cauchy-like matrices associated with real Hurwitz polynomials having distinct zeros. Writing the zeros as $-\lambda_1,\ldots,-\lambda_n$, the matrix entries are defined through ratios of elementary symmetric polynomials in the variables $\lambda_i$. The result extends an earlier positivity theorem obtained under the assumption that all zeros are real. We first show that the coefficients appearing in the denominators are nonzero, so that the matrices are well defined. We then construct an explicit congruence between each matrix and the solution of a Lyapunov equation whose state matrix is in companion form. Positive semidefiniteness follows from a recent result on such equations with entrywise nonnegative right-hand-side data. This approach establishes the extension to complex zeros through the connection between structured matrices and Lyapunov equations.
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Computers and Society

AI-written admissions essays are widespread but penalized

Calvin Isley, Johann D. Gaebler and Sharad Goel

AI is rapidly transforming higher education, including the application process, yet relatively little is known about its use and consequences. To help close this gap, we analyze nearly 7{,}500 applications submitted between 2020 and 2025 to a large public policy master's program in the United States. We find that in the 2025 admissions cycle, the majority of applicants submitted at least one essay that was primarily AI-generated—despite an explicit prohibition against using AI. Leveraging the abrupt introduction of ChatGPT in November 2022, we find that the availability of AI assistants improved the writing quality of submitted essays. These improvements, however, came with an apparent AI penalty: Applicants submitting AI-written essays were admitted less often than comparable non-users. To help explain this penalty, we conduct an experiment with admissions officers, finding that they can often recognize AI writing and rate essays they believe to be AI-generated lower than essays they believe to be human generated. These findings indicate that AI is changing both how applicants write and how that writing is evaluated, raising questions about whether admissions practices and policies designed for a pre-AI era remain appropriate.
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Data Structures and Algorithms

The Nelson-Nguyen Conjecture via Mean-to-Moments Concentration

Tung Mai and Anup Rao

An oblivious subspace embedding (OSE) is a distribution over matrices that approximately preserves the squared Euclidean norm of every vector in any fixed low-dimensional subspace. We prove the Nelson-Nguyen conjecture: for every $0 < \delta < 1$, there exists a distribution that gives an OSE with embedding dimension $O((d + \log(1/\delta))/\varepsilon^2)$ and column sparsity $s = O(\log(d/\delta)/\varepsilon)$, with failure probability at most $\delta$. We first bound the mean spectral error using a trace-moment argument and then upgrade this bound to the desired high-probability guarantee using concentration and resampling. ChatGPT-5.6-Pro was used in proving and writing the results of this manuscript.
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Machine Learning (Statistics)

When Pass@$k$ Crossovers Are Real - and Why: Paired Inference and the Markov Kernel of RLVR

Chen Yang

Reinforcement learning with verifiable rewards (RLVR) typically improves pass@1 but can fall behind its base model at large sampling budgets $k$, commonly interpreted as sharpening existing solutions. Because adjacent pass@$k$ estimates reuse generations, pointwise error bars cannot establish a crossover. We formulate paired inference on the RL-minus-base difference curve: a prompt-level multiplier bootstrap yields a one-sided dominance test and simultaneous confidence bands requiring both an early gain and a later loss. Among nine public RLVR checkpoints at 3k-16k token limits, only one narrowly meets this criterion before multiplicity adjustment; asymmetric truncation can create, mask, or reverse crossovers. On 1,060 deduplicated competition problems at 32k tokens, two 1.5B models exhibit certified crossovers around $k\approx10$-$60$, while two 7B models show no supported loss. A paired variance decomposition shows why adding prompts improves power more than deeper sampling. Prompts with identical base success probability diverge after RLVR, with dispersion replicating across independent generation halves and violating Lipschitz slope bounds for reasonable deterministic maps. Modeling RLVR as a Markov kernel, Jensen's inequality shows that conditional dispersion depresses pass@$k$ for $k\ge2$. Once dispersion is modeled, the conditional-mean log-odds slope is below one in every pair: RLVR improves hard prompts on average, while late-$k$ loss comes from a 2%-3% minority of base-solved prompts collapsing onto a repeated modal wrong answer and resisting prefix rescue up to 4,096 tokens. In controlled runs from one base model on 3,000 new problems, all four GRPO arms fall behind near $k\approx200$ and remain behind through $k=4096$, while SFT stays significantly ahead throughout.
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Systems and Control

Learning Control Policies from Heterogeneous Multi-Horizon Time Series in Battery Energy Management Systems

Sheng Yin, Vivek Teja Tanjavooru, Holger Hesse et al. (+1 more)

This paper introduces Representation-to-Decision (R2D), an end-to-end imitation learning framework that maps heterogeneous multi-horizon time-series inputs directly to battery control decisions through modular Temporal Feature Extractors (TFEs) and a shared latent representation, without an explicit load and PV forecasting step. While accurate forecasting improves prediction quality, optimal control performance remains unguaranteed in prediction-then-optimization pipelines; standard Reinforcement Learning (RL) lacks the long-horizon temporal awareness due to short-window observations, even when forecast signals are available as additional inputs. R2D offers a different perspective: rather than forecasting first and deciding second, it learns to decide directly from raw temporal inputs, with control optimality anchored by an aging-aware Mixed-Integer Linear Programming (MILP) expert through Behavior Cloning (BC). Benchmarked against six controllers on a high-fidelity electro-thermal battery simulation across five industrial sites, R2D achieves 62–77% of the global clairvoyant optimum, outperforms the tested Model Predictive Control (MPC) and RL benchmarks, and yields battery degradation nearly identical to its MILP teacher across all five sites. Comprehensive ablation studies over temporal backbone, model size, expert formulation, aging-cost weighting, and horizon configuration confirm that an LSTM encoder with a 15-minute single-step control horizon provides the most robust and deployment-ready configuration, and cross-site and single-factor out-of-distribution tests show that generalization to unseen profiles is profile-dependent, with policies trained on high-activity sites transferring most reliably.
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All authors Sheng Yin, Vivek Teja Tanjavooru, Holger Hesse, Christoph Goebel

Statistical Methodology

Adaptive online kernel changepoint detection

Qianqian Jiang and Dean Bodenham

We propose an adaptive online kernel-based changepoint detection method for streaming data that is capable of detecting a broad range of changes in the underlying data distribution. The method maintains a recursively-weighted reproducing kernel Hilbert space representation of observations and adaptively updates the forgetting factor through a gradient-based procedure driven by a maximum mean discrepancy-type statistic between the current observation and the weighted empirical distribution of the past. This self-tuning mechanism allows the detector to adapt its effective memory and responsiveness to changes in the underlying process. Simulation results demonstrate that the proposed method achieves strong detection performance across a wide range of distributional changes. Further, our proposed approach maintains constant computational and storage cost through recursive updates, and is very computationally efficient in comparison to competing methods. Experiments on both simulated data and benchmark real-world datasets show improved performance over several other leading kernel-based methods.
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Number Theory

Bad Reduction of Genus Two Curves with complex multiplication

Jan Hendrik Bruinier, Tonghai Yang and Peng Yu

We study genus two curves whose Jacobians have complex multiplication by a biquadratic CM field. In this setting the Jacobian decomposes as a product of two elliptic curves with complex multiplication by orders in the same imaginary quadratic field. We derive upper bounds for the primes of stable bad reduction of such curves in terms of the discriminants of the CM orders. To do so, we give an explicit description of principally polarized abelian surfaces with CM by a biquadratic field via small CM cycles on an orthogonal Shimura variety of signature (3,2). We use it to prove an arithmetic intersection formula between such CM cycles with Humbert surfaces in terms of coefficients of incoherent Eisenstein series and representation numbers of ternary positive definite quadratic forms. Finally, employing the arithmetic properties of higher Green functions, we show that primes of stable bad reduction always exist.
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Astrophysics of Galaxies

The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters

Caleb Ogle, Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer et al. (+5 more)

Accurately inferring dark matter halo properties like the total halo mass (M_{halo}) and gas fractions (f_{gas}) remains particularly challenging at group scales, where low member counts, shallow potential wells, and AGN feedback-driven baryon expulsion introduce significant observational scatter. As large-scale surveys begin to provide unprecedented multi-wavelength data, there is a pressing need for methods that can jointly leverage diverse observables to overcome these uncertainties. We introduce the Deep Learning Halo Definer (DLHD), a multimodal deep learning framework that simultaneously processes galaxy catalogues and X-ray imaging through the combination of a Deep Sets and a Convolutional Neural Network (CNN) to improve M_{halo} and f_{gas} estimation for galaxy groups and clusters. Using mock datasets derived from the IllustrisTNG300 hydrodynamic simulation, we demonstrate that the DLHD outperforms each of its component networks individually, achieving RMSE improvements in M_{halo} of 1.9x over Deep Sets and 1.3x over the CNN alone. For the gas fraction enclosed within R_{200c}, DLHD reduces RMSE by 2.0x relative to Deep Sets and 1.1x relative to the CNN alone, with consistent improvements across other apertures. These results highlight a novel ability to leverage multi-band information inaccessible to single-modality methods, positioning DLHD as a promising tool for next-generation survey analyses.
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All authors Caleb Ogle, Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer, Naomi Gluck, Matthew Ho, Daisuke Nagai, Joseph N. Burchett, Mohammadreza Ayromlou

Systems and Control

Communication Delay Robust Control of BESS for AI Training Load Smoothing

Xue Lyu, Wei Du, Sheik Mohammad Mohiuddin et al. (+1 more)

AI training loads can exhibit rapid power fluctuations because their power demand differs significantly between computational and communication phases, creating challenging ramp rates at the data center point of common coupling (PCC). Integrating battery energy storage systems (BESS) in data centers is a promising mitigation option. This paper proposes a hybrid BESS control strategy that combines droop-based grid-forming (GFM) control with instantaneous load current-based compensation to suppress high frequency load fluctuations. The GFM control loop regulates the long-term power exchange of the BESS, while the load-following control provides fast compensation for short-term AI workload fluctuations. Communication delay between the load current measurements and the BESS controller is explicitly modeled, and its impact on smoothing performance is analyzed. To mitigate delay-induced degradation, a predictor-based compensation method is incorporated into the BESS control structure. High-fidelity electromagnetic transient simulations are conducted to validate the proposed approach. Results demonstrate effective smoothing of AI training load fluctuations across different grid strength conditions and under time varying communication delays.
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All authors Xue Lyu, Wei Du, Sheik Mohammad Mohiuddin, Brett A. Ross

Statistical Mechanics

Intermittency in Wind-Driven Fires

Laurent Hébert-Dufresne, Aanjaneya Kumar and S. Redner

We construct a wind-driven forest-fire model in one dimension in which a fire can jump gaps between trees to ignite disjoint downwind forests. The size of a gap that a fire can jump depends on the fire intensity, which increases as the fire propagates through trees and diminishes as the fire jumps gaps. Trees grow on empty sites at rate $r$ and lightning strikes each site with rate $f$. When $f\ll r/L$, where $L$ is the system length, lightning is sufficiently rare that quasi-deterministic dynamics arises where all trees are consumed when a lightning-induced fire occurs. For $f\gg L^{-\mu}$ with $\mu\approx 0.8$, lightning is sufficiently frequent that a steady state is reached, but with unexpected behaviors for the forest- and gap-size distributions. Intermittency arises in between these regimes, with coexisting temporal domains of deterministic and chaotic dynamics.
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Optics

Ultrafast light-sheet optical tweezers for in situ parallelized biomechanical characterization of cells and soft tissues

Krishangi Krishna, Gannon Lemaster, Jieyi Xu et al. (+8 more)

Quantitative characterization of the mechanical properties of cells and tissues is essential for understanding disease progression and tissue regeneration. Optical tweezers (OT) enable the direct application of biologically relevant forces; however, OT has been limited to single axial indentations of cells, thereby restricting throughput. Furthermore, the use of quadrant photodiodes is insufficient for assessing the large displacements required for biomechanical characterization of tissues. We present light-sheet optical tweezers as a force transducer (LOFT), an approach that improves the throughput by at least 3x through simultaneous multiparticle trapping and parallelized characterization under sub-nN forces. LOFT is achieved by uniquely integrating light-sheet illumination for extended trapping, femtosecond-pulsed lasers to augment the optical gradient force, and videography-based particle tracking for observation of force transduction. The platform is validated through single-cell indentation experiments. We then apply LOFT to myocardial tissue, revealing significant biomechanical differences between healthy and infarcted regions; the interpretation of which is further supported by quantitative multiphoton imaging using the same optical source and platform. This work represents the first demonstration of OT for the mechanical testing of intact soft tissues, and establishes LOFT as a versatile, multifunctional platform for high-throughput, minimally invasive, mechanical characterization of complex biological systems in situ.
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All authors Krishangi Krishna, Gannon Lemaster, Jieyi Xu, Zhaowei Jiang, Carlonia G. Casas, Zahra Ahmed, Stephanie Roser, Kareen L. K. Coulombe, Anita Shukla, Vikas Srivastava, Kimani C. Toussaint Jr

Cosmology and Nongalactic Astrophysics

Axio-Dilaton Dark Energy: A Dynamical Systems and Bayesian Inference Analysis

Mario Ramos-Hamud, Gabriela García-Arroyo, Fernando Quevedo et al. (+1 more)

We initiate a systematic cosmological investigation of an axio-dilaton system governed by a curved field-space metric as generically motivated by supergravity and string compactifications. The scalar sector consists of a light scalar dilaton field evolving along an asymptotically exponential potential and a pseudoscalar field spanning an essentially flat direction, kinetically coupled through an exponential field-space metric. Deviations from the asymptotic regime are incorporated by introducing a generalised Albrecht-Skordis potential containing a polynomial factor. Utilising a dynamical systems approach alongside numerical evolution and Bayesian inference, we constrain the model using Type Ia Supernovae (SNe Ia), Baryon Acoustic Oscillations (BAO) and Planck 2018 distance-prior data. We identify a parameter degeneracy indicating that steeper potentials demand stronger field-space kinetic couplings to sustain late-time cosmic acceleration. Finally, a joint likelihood analysis reveals that while the model successfully accounts for dark energy dynamics, it is only slightly statistically favoured over $\Lambda$CDM by current observations.
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All authors Mario Ramos-Hamud, Gabriela García-Arroyo, Fernando Quevedo, J. Alberto Vázquez

Robotics

FRAMES: Failure Recovery And Monitoring of Embodied Skills for Humanoid Loco-Manipulation

Ajay Vikram Periasami, Xinyuan Luo, Haoyu Li et al. (+1 more)

Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does not guarantee successful physical execution. This gap is especially important in humanoid loco-manipulation, where errors during approach, grasping, transport, or placement can invalidate the remainder of a long-horizon plan. We present FRAMES, a failure-aware supervisory framework for the Unitree G1 humanoid that operates above the CEER whole-body controller. A Planner Agent selects subtasks through parameterized mid-level skills, while a vision-language-model-based Monitor Agent evaluates each skill using temporal multi-view observations and structured robot and contact evidence. Detected failures stop the active skill and provide grounded feedback to a Recovery Agent. The framework further includes a Memory Module for reusing prior skill experience, and geometric grounding via depth and segmentation. We independently evaluate the monitoring module of the framework in MuJoCo using 100 trials comprising 50 failed and 50 successful executions across five tasks. The monitor detects 48 of 50 failures, correctly accepts 46 of 50 successful executions, and achieves 94.0% overall accuracy. These results provide initial evidence for the monitoring component, while end-to-end evaluation of the complete recovery loop remains ongoing.
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All authors Ajay Vikram Periasami, Xinyuan Luo, Haoyu Li, Xianyi Cheng