Showing 1–50 of 911,242 results for author: J.

VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences

Authors: Elaine Lau, Thanuka Udumulla, Lee Izhaki-Tavor, Francisco Guzmán, Nicholas Magazine, Jonas Mueller

Abstract: In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ...) to inform research decisions. We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the types of artifacts examined throughout experimental workflows in the biotech ind… ▽ More In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ...) to inform research decisions. We introduce VIALS, a visual question-answering benchmark with 161 such interpretation tasks, spanning the types of artifacts examined throughout experimental workflows in the biotech industry (rather than polished figures from publications and textbooks). While frontier vision-language models can now fluently describe natural images, we find that they are unable to accurately interpret these scientific images, reflecting limitations in domain knowledge and domain-specific visual reasoning capabilities. In contrast, scientists with relevant domain expertise find these visual interpretation tasks straightforward. AI that cannot similarly interpret such images will have limited utility in professional life sciences workflows, where such artifacts are central to how scientists reason, communicate, and make decisions. △ Less

Submitted 21 August, 2026; originally announced August 2026.

AI with Authority, from Application to Silicon

Authors: Jason Hickey

Abstract: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI inverts this relationship: at AI speed, machine verification is not only economical but essential to productivity --- it is the incorruptible referee that lets one person safely direct autonomous machine work at scale. In five weeks, one researcher on c… ▽ More For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI inverts this relationship: at AI speed, machine verification is not only economical but essential to productivity --- it is the incorruptible referee that lets one person safely direct autonomous machine work at scale. In five weeks, one researcher on consumer AI subscriptions directed a small fleet of AI agents from application code, through a verified compiler and executive, to a RISC-V processor taped out on a community silicon shuttle; no proof passed through human review, and no RTL was written by a human. The working discipline --- the Salt method --- rests on a proof kernel no hallucinated proof can pass: mathematical claims travel between agents as kernel-checked artifacts, and human attention is reserved for statements, designs, and rulings. Verification is stated link by link, from the Lean 4 kernel to SAT-checked equivalence at the silicon boundary. We publish the complete accounting: theorem provenance, a pre-registered token meter, floor-bounded human time, and an error ledger whose catch numbering runs to #256 --- a monotone counter over the mathematics campaign's append-only flags ledger, maintained 2026-07-07 to 2026-07-20 (one number, #79, was never assigned; later catches are recorded un-numbered) --- against zero incorrect proofs reaching the record. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 17 pages, 6 figures

Magic Velocity Selection in Atom Interferometry

Authors: Yuno Iwasaki, Jack Roth, Madeline Bernstein, Andrew Christensen, Holger Mueller

Abstract: Velocity-selective Raman transitions are widely used in atom interferometers to prepare atomic ensembles with narrowly defined momentum distributions. However, differential light shifts between atomic energy levels generate velocity distributions that are correlated with the Raman beam intensity, and therefore with the position of the atoms within the laser beam. We show that these spatially inhom… ▽ More Velocity-selective Raman transitions are widely used in atom interferometers to prepare atomic ensembles with narrowly defined momentum distributions. However, differential light shifts between atomic energy levels generate velocity distributions that are correlated with the Raman beam intensity, and therefore with the position of the atoms within the laser beam. We show that these spatially inhomogeneous velocity distributions interact with detuning-dependent systematic effects in a Bragg diffraction-based simultaneous conjugate Ramsey-Bordé interferometer. These interactions can induce systematic phase shifts of order 10 milliradians in the interferometer phase. We further identify a "magic" detuning for velocity selection and show that operating at this detuning suppresses the systematic phase shift. Magic velocity selection eliminates systematic errors arising from correlations between atom velocity and position, facilitating high-resolution atom interferometry experiments targeting sub-part-per-billion accuracy. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 10 pages, 12 figures

Truthful Calibration Measures for Sequential Prediction

Authors: Anagha Gokul, Jason Hartline, Lunjia Hu, Jonathan Ullman, Yifan Wu

Abstract: Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated reports. Haghtalab et al. (2024) proposed an approximately truthful calibration measure for online prediction, leaving open whether exact truthfulness is compatible with completeness and soundness. We resolve this ques… ▽ More Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated reports. Haghtalab et al. (2024) proposed an approximately truthful calibration measure for online prediction, leaving open whether exact truthfulness is compatible with completeness and soundness. We resolve this question negatively for sequential binary prediction: exact truthfulness is incompatible with completeness and soundness, even for independent outcomes. We then show that this impossibility is specific to exact truthfulness. We give two general reductions from a base calibration measure, producing additively and multiplicatively approximately truthful calibration measures, respectively. Applying the multiplicative reduction, for every $0 < \varepsilon < 1$ we construct a sound and complete calibration measure that is $(1+\exp(-T^{(1-\varepsilon)/2}/2))$-multiplicatively truthful. This improves the approximate-truthfulness guarantee of Haghtalab et al. (2024). △ Less

Submitted 21 August, 2026; originally announced August 2026.

Exact Minimum $d$-Degree Thresholds for Hypergraph Perfect Matchings

Authors: Jie Han, Hongliang Lu, Bin Wang, Feihong Yuan

Abstract: For fixed integers $k\ge3$ and $1\le d\le k-1$ and sufficiently large $n\in k\mathbb N$, we establish the sharp minimum $d$-degree thresholds that forces perfect matching in every $n$-vertex $k$-uniform hypergraphs. This was conjectued by Treglown and Zhao, and the $d=1$ case was conjectued by Kühn, Osthus and Treglown. For fixed integers $k\ge3$ and $1\le d\le k-1$ and sufficiently large $n\in k\mathbb N$, we establish the sharp minimum $d$-degree thresholds that forces perfect matching in every $n$-vertex $k$-uniform hypergraphs. This was conjectued by Treglown and Zhao, and the $d=1$ case was conjectued by Kühn, Osthus and Treglown. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 11 pages. Comments welcome

Bottomonium transport in the sQGP at RHIC and the LHC

Authors: Biaogang Wu, Jacob Boyd, Ralf Rapp

Abstract: Bottomonium transport is studied in heavy-ion collisions at RHIC and the LHC by implementing a kinetic rate equation into (3+1)D viscous hydrodynamic simulations of an expanding quark-gluon plasma (QGP). The two main transport parameters are the inelastic reaction rates and equilibrium limits for each individual bottomonium state, $Y$. The former are taken from the thermodynamic $T$-matrix formali… ▽ More Bottomonium transport is studied in heavy-ion collisions at RHIC and the LHC by implementing a kinetic rate equation into (3+1)D viscous hydrodynamic simulations of an expanding quark-gluon plasma (QGP). The two main transport parameters are the inelastic reaction rates and equilibrium limits for each individual bottomonium state, $Y$. The former are taken from the thermodynamic $T$-matrix formalism with recent constraints from lattice-QCD and including interference effects and in-medium binding energies, resulting in large rates characteristic of a strongly coupled QGP. The equilibrium limits are evaluated from pertinent in-medium bottomonium and bottom-quark masses. The calculation of observables includes a total of nine $Y$ states (up to 3$S$ and 2$P$) with a feed-down matrix estimated from vacuum branching fractions. At the LHC, the large reaction rates rapidly suppress the initial population of excited states, rendering regeneration their main source even in rather peripheral collisions, while for the more strongly bound ground state $Υ(1S)$, a significant primordial component survives in central collisions. On the other hand, at RHIC energies, regeneration is overall a smaller effect. Together with effects from nuclear absorption, this offers an explanation for the experimental observation that $Υ(1S)$ production at RHIC and the LHC is of comparable magnitude despite the significantly higher temperatures reached in the QGP at the LHC. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Efficient Event Generation for High-Multiplicity LHC Processes: An End-to-End GPU Workflow with Normalizing Flows

Authors: Enrico Bothmann, Joshua Isaacson, Claudius Krause, Carla J. López-Zurita, Maximilian Spannring, Daohan Wang

Abstract: Producing very large unweighted event samples for high-multiplicity processes is limited by expensive matrix-element evaluations and low unweighting efficiencies. We present the first end-to-end GPU-resident event-generation workflow that integrates normalizing-flow proposals with the parton-level event generator Pepper. Helicity-conditioned coupling flows are trained using online updates suppleme… ▽ More Producing very large unweighted event samples for high-multiplicity processes is limited by expensive matrix-element evaluations and low unweighting efficiencies. We present the first end-to-end GPU-resident event-generation workflow that integrates normalizing-flow proposals with the parton-level event generator Pepper. Helicity-conditioned coupling flows are trained using online updates supplemented by sample replay and deployed across all subprocesses of complete proton--proton collision processes with many final-state jets. In this workflow, a Python-based control layer and Pepper exchange flow-generated phase-space points and the corresponding target-density evaluations directly in device memory. The control layer performs flow sampling, proposal-density evaluation, and unweighting, while Pepper evaluates the matrix elements, PDFs, and phase-space factors defining the target density and writes the accepted events in standard formats. We compare subprocess-specific flows, with one flow per partonic subprocess, to grouped conditional flows that share parameters among subprocesses with related parton content. The workflow is benchmarked for $pp \to e^+e^- + 4j$, $pp \to e^+e^- + 5j$, $pp \to t \bar t + 4j$, $pp \to 4j$, and $pp \to 5j$ production. On four H100 GPUs, we generate $10^9$ unweighted events for each benchmark process. Including the cost of flow training, the workflow achieves end-to-end speedups of up to two orders of magnitude over standalone Pepper event generation and turns a multi-week task into a sub-day computation. It thereby makes billion-event production more practical and offers a pathway to alleviating the Monte Carlo statistics bottleneck in high-multiplicity collider physics. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 43 pages, 18 figures, 16 tables

Report number: MBI-ML-26-04, MCNET-26-23, MSUHEP-26-012

Maximal right ideals of the Banach algebra of bounded operators on a Banach space

Authors: Tomasz Kania, Niels Jakob Laustsen

Abstract: We study finitely generated maximal right ideals of the Banach algebra $\mathcal{B}(E)$ of bounded operators on a complex Banach space $E$. Every maximal right ideal is either fixed by a non-zero functional or contains the ideal of finite-rank operators; when $E$ is infinite-dimensional, each non-fixed maximal right ideal in fact contains the ideal of inessential operators. Using the elementary… ▽ More We study finitely generated maximal right ideals of the Banach algebra $\mathcal{B}(E)$ of bounded operators on a complex Banach space $E$. Every maximal right ideal is either fixed by a non-zero functional or contains the ideal of finite-rank operators; when $E$ is infinite-dimensional, each non-fixed maximal right ideal in fact contains the ideal of inessential operators. Using the elementary representation of finitely generated right ideals as lifting ideals $\operatorname{Lift}(T)=\{TU:U\in\mathcal{B}(E,E^n)\}$, where $T\in\mathcal{B}(E^n,E)$ for some $n\in\mathbb{N}$, we identify the exact operator-theoretic obstruction. The ideal $\operatorname{Lift}(T)$ contains the finite-rank operators precisely when $T$ is surjective, and it equals $\mathcal{B}(E)$ precisely when $T$ is right invertible. If $T$ is surjective but not right invertible, then $\operatorname{Lift}(T)$ is maximal exactly when the row operator $[T\ S]$ is right invertible for every $S\in\mathcal{B}(E)\setminus\operatorname{Lift}(T)$. We apply this framework, together with duality, pullback, lattice-theoretic and cardinality arguments, to obtain maximal right ideals which are not finitely generated for large classes of Banach spaces. These include the following infinite-dimensional spaces: reflexive spaces, separable spaces with an unconditional Schauder decomposition into a countably infinite sequence of non-zero subspaces, spaces containing a complemented copy of $\ell_1$, KB-spaces, Lebesgue spaces $L_p(μ)$ for $1\leqslant p<\infty$, full Orlicz spaces with order-continuous norm, and scalar-plus-compact spaces. We obtain the stronger conclusion that every finitely generated maximal right ideal is fixed for Hilbert spaces, $\ell_1(Γ)$-spaces, reflexive spaces with the bounded approximation property, and the mixed spaces $\ell_1(Γ)\oplus H$ with $H$ a separable Hilbert space. △ Less

Submitted 21 August, 2026; originally announced August 2026.

MSC Class: 47L10; 46H10 (Primary); 47L20; 46B42 (Secondary)

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

Authors: Daniel Abadjiev, Eliza Howard, Tsz Ngong You, Ryan Michaud, Benjamin Ryan Roberts, Benjamin Rosser, Karri Folan Di Petrillo, Anthony Badea, Doug Berry, Arghya Ranjan Das, Jennet Dickinson, Giuseppe Di Guglielmo, Harshul Gupta, Farah Fahim, Abhijith Gandrakota, Lindsey Gray, James Hirschauer, David Jiang, Shiqi Kuang, Ron Lipton, Mira Littmann, Miaoyuan Liu, Nicholas Manganelli, Petar Maksimovic, Corrinne Mills , et al. (14 additional authors not shown)

Abstract: A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning fo… ▽ More A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Time-Aware Tranformer-Based Prediction Model for AECOPD

Authors: Weihao Qu, Ling Zheng, Dongyang Wang, Jiacun Wang, Haowen Pan

Abstract: The rapid symptom change of Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) makes it critical to have time-sensitive prediction models. However, most current machine learning models studying AECOPD use clinical and laboratory data, which will inevitably cause latency. To ensure timely detection of AECOPD and minimize latency, this paper focuses on home monitoring scenarios whe… ▽ More The rapid symptom change of Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) makes it critical to have time-sensitive prediction models. However, most current machine learning models studying AECOPD use clinical and laboratory data, which will inevitably cause latency. To ensure timely detection of AECOPD and minimize latency, this paper focuses on home monitoring scenarios where only respiratory data from daily-use ventilators is available. We introduce a Time-Aware transformer-based AECOPD prediction model, which generates meaningful patient representations using the Time-Aware transformer to capture the symptoms and their temporal progression in ventilator data. Our experimental results demonstrate that our Time-Aware transformer-based approach outperforms traditional methods in multiple classification tasks, highlighting its potential to enhance AECOPD prediction accuracy. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 5 pages, 1 figure, 1 table. Published in MEDINFO 2025

Journal ref: Stud Health Technol Inform. 329:1089-1093 (2025)

Diversity of Galaxy Centers from Small-Scale Isocurvature

Authors: Jessica N. Lopez-Sanchez, Wen Yin

Abstract: The observed diversity of galaxy centers motivates the possibility that the local dark matter composition may vary among galaxies. In conventional multicomponent dark matter scenarios, however, large stochastic variations in the relative abundances are not generically expected, with the cold component typically remaining dominant. We perform $N$-body simulations with a subdominant ultralight dark… ▽ More The observed diversity of galaxy centers motivates the possibility that the local dark matter composition may vary among galaxies. In conventional multicomponent dark matter scenarios, however, large stochastic variations in the relative abundances are not generically expected, with the cold component typically remaining dominant. We perform $N$-body simulations with a subdominant ultralight dark matter component carrying significant large-amplitude small-scale isocurvature perturbations. We find that although nonlinear evolution largely homogenizes its fraction on galactic scales,ultralight dark matter can still be enhanced and even dominate the centers of small halos because large initial ultralight dark matter fluctuations seed early potential wells that later form halo centers. The ultralight-dominated region can extend beyond $0.01R_{\rm vir}$ and account for more than half of the central dark matter density, even for a cosmological ultralight dark matter fraction below $O(10\%)$. This central segregation provides a possible route to stochastic cusp--core diversity, potentially explaining the observed diversity of galaxy centers. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 25 pages and 10 figures

Hypothesis testing between quantum ensembles

Authors: Jian Yao, Quntao Zhuang

Abstract: Quantum state ensembles are important in quantum information processing. For example, quantum $t$-designs model highly entangled states in complex systems, while projected ensembles appear in generative quantum machine learning and studies of thermalization. With their sample state accompanied by a classical label, these ensembles contain operational information beyond their average density operat… ▽ More Quantum state ensembles are important in quantum information processing. For example, quantum $t$-designs model highly entangled states in complex systems, while projected ensembles appear in generative quantum machine learning and studies of thermalization. With their sample state accompanied by a classical label, these ensembles contain operational information beyond their average density operators. Yet an ensemble differs from a classical-quantum state because it is invariant under permutations of labels. We formulate binary hypothesis testing between finite quantum ensembles and derive fundamental limits on error probability. Given an observed label pattern, we show that the joint sampled state can be described by power-weighted ensemble moments. This yields the Bayes-optimal measurement and exact finite-sample error, revealing that discrimination is governed by the full moment hierarchy up to the number of samples. In the many-sample limit, we derive Chernoff bounds and obtain exact error exponents for finite uniform pure-state ensembles. We apply these results to optical communication and $t$-designs. For finite uniform pure-state $t$-designs with large $t$, the maximal discrimination exponent scales sharply as $\sim t^{-2}$, while equal-prior fixed-error testing requires $\sim t^2$ samples. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 5+25 pages, 2 figures

VLASS Discovery of a Luminous Galactic Radio Transient Evolving on Decade Timescales

Authors: Jessie M. Miller, Gregg Hallinan, Dillon Dong, Adolfo S. Carvalho, S. T. Myers, Jean Somalwar, Casey Law, B. M. Gaensler, Vikram Ravi, Laura Chomiuk, Assaf Horesh, Delina Levine, Yuyang Chen

Abstract: We present a multiwavelength analysis of the radio transient VT J1906+0849, discovered as a 70 mJy source in Epoch 1 of the Very Large Array Sky Survey (VLASS), 21 yr after an NRAO VLA Sky Survey (NVSS) non-detection. Radio observations reveal the source was first detected in 2005, peaking at $\gtrsim200$ mJy in 2014, then declining until a late 2025 rebrightening. The transient sits at a Galactic… ▽ More We present a multiwavelength analysis of the radio transient VT J1906+0849, discovered as a 70 mJy source in Epoch 1 of the Very Large Array Sky Survey (VLASS), 21 yr after an NRAO VLA Sky Survey (NVSS) non-detection. Radio observations reveal the source was first detected in 2005, peaking at $\gtrsim200$ mJy in 2014, then declining until a late 2025 rebrightening. The transient sits at a Galactic latitude of $\approx0.74^\circ$ and the properties of its optical-infrared counterpart support a Galactic origin. At $d\gtrsim15$ kpc, the extreme radio luminosity is likely powered by sustained accretion onto a compact object. However, a Swift-XRT non-detection shows it is X-ray faint relative to the Galactic X-ray binary population, and the radio emission is distinct from X-ray binaries in its temporal and spectral behavior. Broadband radio spectra suggest synchrotron self-absorption, but size constraints from equipartition and very long baseline interferometry show little to no expansion in the radio-emitting region over 5+ yr, despite significant spectral evolution. Near-infrared spectroscopy reveals a single broad emission line with a stable centroid but variable width and luminosity. We attribute this feature to blueshifted Br$γ$ tracing a persistent asymmetric $\approx2000$ km s$^{-1}$ outflow. These properties are unlike any previously identified Galactic radio source. One possible interpretation is that VT J1906+0849 is a young analog of the microquasar SS 433, with a dense disk wind confining a continuously powered synchrotron outflow. This jet-wind interaction explains the compact, slowly expanding radio source and may contribute to the absence of bright X-ray emission. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 50 pages, 21 figures

Unified Branch-and-Bound Search for the Steiner Traveling Salesman Problem on Graphs of Convex Sets

Authors: Jingtao Tang, Hang Ma

Abstract: We formalize the Steiner Traveling Salesman Problem (Steiner-TSP) on Graphs of Convex Sets (GCS), which seeks a minimum-cost closed trajectory through required convex sets while allowing optional transit vertices and revisits. To explore the resulting infinite solution space, we propose a unified branch-and-bound search over rooted walk prefixes. Additive lower-bound-graph costs bound committed pr… ▽ More We formalize the Steiner Traveling Salesman Problem (Steiner-TSP) on Graphs of Convex Sets (GCS), which seeks a minimum-cost closed trajectory through required convex sets while allowing optional transit vertices and revisits. To explore the resulting infinite solution space, we propose a unified branch-and-bound search over rooted walk prefixes. Additive lower-bound-graph costs bound committed prefixes, while a cut-separated connected-flow relaxation lower-bounds the residual cost of visiting every remaining target and returning to the root. Under a uniform positive-cost assumption, best-first traversal terminates after finitely many expansions on every feasible instance without an initial incumbent, whereas depth-first traversal does so once a finite incumbent is available. For a user-specified factor $ε\geq1$, a global lower bound certifies that either strategy's incumbent cost is at most $ε$ times the global optimum. We further demonstrate joint sensing-mode, visitation-order, and continuous-trajectory selection for a mobile-manipulator inspection task, including action precedences expressed in linear temporal logic over finite traces (LTL$_f$). Both traversal strategies find feasible solutions on all benchmark instances within 30s with mean certified optimality gaps of 28.1% and 29.7%, respectively, whereas two recent baselines succeed on only about half of the instances △ Less

Submitted 21 August, 2026; originally announced August 2026.

Ultralow-Field Triplon Condensation in a Spin-Ladder Magnet

Authors: Ankit Labh, Ross H. Colman, Jakub Šebesta, Noah Oefele, Elsa Lhotel, Adam Berlie, Paul Steffens, Oksana Zaharko, Pascal Manuel, Iurii Kibalin, Philipp Gegenwart, Dominik Legut, Johanna K. Jochum, Alexander A. Tsirlin, Petr Čermák

Abstract: We realise the first ultralow-field Bose-Einstein condensation of triplons in a spin-ladder magnet, uncovering a quantum critical point at only $μ_0 H_{c1}=0.17$ T in Henmilite ($\mathrm{Ca_2Cu(OH)_4[B(OH)_4]_2}$). Unlike dimer magnets, a ladder retains extended one-dimensional correlations in its gapped parent state, making this limit strongly fluctuation dominated. Thermodynamic, magnetoelastic,… ▽ More We realise the first ultralow-field Bose-Einstein condensation of triplons in a spin-ladder magnet, uncovering a quantum critical point at only $μ_0 H_{c1}=0.17$ T in Henmilite ($\mathrm{Ca_2Cu(OH)_4[B(OH)_4]_2}$). Unlike dimer magnets, a ladder retains extended one-dimensional correlations in its gapped parent state, making this limit strongly fluctuation dominated. Thermodynamic, magnetoelastic, $μ$SR, and neutron-diffraction measurements overturn the previous assignment of zero-field antiferromagnetic order, establishing a quantum-disordered coupled-ladder parent state with persistent low-energy dynamics. The weak low-temperature anomaly instead marks a gap-controlled crossover from the correlated ladder regime into the activated quantum-disordered state. These measurements further reveal an exceptionally asymmetric ordered dome extending to $μ_0 H_{c2}\simeq 8.2$ T. Quantum Monte Carlo simulations for the relevant spin Hamiltonian place Henmilite just on the gapped side of the zero-field ladder-ordering instability, naturally accounting for the strong separation between the exchange and residual-gap scales and the tiny critical field. Our findings extend ultralow-field triplon condensation beyond the dimer paradigm and establish Henmilite as a platform for controlled tuning across quantum criticality in a fluctuation-dominated spin ladder. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 8 pages, 4 figures; Supplemental Material: 13 pages, 7 figures, 1 table

VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains

Authors: Zhongming Yao, Junchang Xin, Yumeng Song, Yusen Mao, Kristian Torp, Yuemin Ding, Divesh Srivastava, Yushuai Li, Christian S. Jensen, Tianyi Li

Abstract: Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited supp… ▽ More Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections

Authors: Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes, Pablo A. Unzueta, Gregory J. O. Beran, Lyndon Emsley, Michele Ceriotti

Abstract: Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a shielding-tensor model trained directly on monomer-corrected ca… ▽ More Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a shielding-tensor model trained directly on monomer-corrected calculations that approximate PBE0, rather than the PBE reference targeted by earlier ShiftML models. ShiftML4 is trained on a diverse set of structures containing 12 of the most common NMR nuclei in molecular organic solids. On experimental benchmark sets, the 13C isotropic RMSE against experiment is 1.67 ppm, compared with 2.34 ppm for GIPAW-PBE on the same geometries. ShiftML4 gives a similar 1H prediction RMSE to ShiftML3 (0.5 ppm) and improves the 15N RMSE from 7.24 to 6.08 ppm. The model also reduces errors in the shielding-tensor anisotropy, with an RMSE of 4.63 ppm on 13C CSA principal components against 5.85 ppm for GIPAW. The improvements in prediction accuracy are retained on better geometries. Basing shift predictions on structures relaxed with PET-MOLS, a recent machine-learned interatomic potential that reaches approximate hybrid-DFT geometries in seconds, lowers the ShiftML4 errors further to 0.48 ppm (1H), 1.49 ppm (13C) and 3.66 ppm (15N). △ Less

Submitted 21 August, 2026; originally announced August 2026.

Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis

Authors: Qisheng Lu, Aoyang Fang, Junjielong Xu, Jin'ao Shang, Songhan Zhang, Yifan Yang, Xiaochuan Yan, Pinjia He

Abstract: Existing evaluations of automated root cause analysis (RCA) for microservices assess diagnostic performance mainly by endpoint correctness: whether a method localizes the responsible service. This criterion enables comparison but does not reveal the evidentiary basis of a diagnosis or the fault-propagation route connecting the source to observed symptoms, both of which an on-call site reliability… ▽ More Existing evaluations of automated root cause analysis (RCA) for microservices assess diagnostic performance mainly by endpoint correctness: whether a method localizes the responsible service. This criterion enables comparison but does not reveal the evidentiary basis of a diagnosis or the fault-propagation route connecting the source to observed symptoms, both of which an on-call site reliability engineer needs to judge whether action is warranted. We therefore treat RCA as an observable diagnostic process. Our trajectory-level framework evaluates agent executions against manually curated service-level fault-propagation paths. Applied to a public microservice RCA benchmark, it analyzes 3,500 diagnostic trajectories, characterizing where agents investigate and how they use retrieved telemetry. We find a disconnect between answer correctness and diagnostic quality: an agent may localize the fault source yet fail to reconstruct its propagation. Successful investigations stay on the fault-impact surface, act on retrieved evidence, and broaden their query repertoire as the search deepens. Failures arise when decisive evidence is omitted, retrieved evidence is misinterpreted, or unsupported inference substitutes for missing evidence. We operationalize this taxonomy as DiagGuard, a two-stage defense-in-depth architecture in which grounding surveys available observations before localization and verification audits the diagnosis against them. In an independent setting with a different model, benchmark, and service topology, DiagGuard raises Acc@1 from 43.5% to 52.5%. These results show that trajectory-level evaluation exposes limitations hidden by final-answer metrics and provides actionable guidance for improving automated RCA. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 13 pages, 7 figures, 5 tables

Rethinking Expressivity and Efficiency in Test-Time Training

Authors: Zeyun Zhong, Joya Chen, Manuel Martin, Frederik Diederichs, Juergen Gall, Juergen Beyerer

Abstract: Test-Time Training (TTT) enables long-context processing via continuous weight updates during inference, but current methods struggle to balance the expressivity of per-token update dynamics with the hardware efficiency of chunk-wise approximations. We propose E$^2$-TTT (Expressive and Efficient TTT) to bridge this gap. Under the standard approximation of taking gradients at the chunk-start weight… ▽ More Test-Time Training (TTT) enables long-context processing via continuous weight updates during inference, but current methods struggle to balance the expressivity of per-token update dynamics with the hardware efficiency of chunk-wise approximations. We propose E$^2$-TTT (Expressive and Efficient TTT) to bridge this gap. Under the standard approximation of taking gradients at the chunk-start weights, we derive a closed-form state transition that exactly reproduces the chunk-end fast-weight and momentum states of the per-token recurrence. This enables fully parallelized chunk-level training while preserving the temporal structure of the update rule that prior chunk-wise methods discard. We validate E$^2$-TTT by training models up to 1.3B parameters from scratch. It performs on par with previous TTT and hybrid attention baselines in language modeling while outperforming them on in-context retrieval. Its advantage is most pronounced in length extrapolation: on the standard ``Needle in a Haystack'' passkey test, it retains over 90% accuracy at $8\times$ the training context length. Meanwhile, E$^2$-TTT can match the training throughput of efficient chunk-wise methods, demonstrating that it effectively reconciles expressivity with efficiency. The code is available at https://github.com/zeyun-zhong/E2-TTT. △ Less

Submitted 21 August, 2026; originally announced August 2026.

SPARCL: Spectral Partitioned Analytic Continual Learning

Authors: James Hartley, Zeropy Surio, Daniel Whitmore, Hannah Clarke, Thomas Reed

Abstract: Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-form ridge updates. Yet the usual forgetting narrative, centered on stochastic gradient overwriting, does not explain why analytic methods still drift on old classes despite exact recursive solvers. We identify the culprit… ▽ More Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-form ridge updates. Yet the usual forgetting narrative, centered on stochastic gradient overwriting, does not explain why analytic methods still drift on old classes despite exact recursive solvers. We identify the culprit as spectral interference: the joint ridge classifier for all tasks shares the inverse autocorrelation operator $(R+λI)^{-1}$, so incoming task samples that load onto old dominant eigendirections dilute the spectrum and perturb old-class logits even when old labels are never revisited. Based on this view, we propose SPARCL, a spectral partitioned analytic continual learner that decomposes the running autocorrelation into a high-energy core and a residual complement, freezes old-class classifier components in the core subspace, and updates only the residual block through recursive least squares with an optional residual random-projection expansion. This yields a simple closed-form update with a provable invariance guarantee for the core contribution of old logits. Across CIFAR-100, CUB-200, ImageNet-R, and ImageNet-A under a frozen ViT-B/16 protocol, SPARCL closes most of the gap from classical analytic learners to strong representation matchers, while remaining complementary to sparse feature-decorrelation approaches such as Fly-CL. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Re$^3$Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning

Authors: Haonan Jia, Shichao Dong, Zenghui Sun, Jiawen Zheng, Ziqi Miao, Gege Shi, Qiuyu Zhao, Jinsong Lan, Xiaoyong Zhu, Bo Zheng

Abstract: Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads to a performance gap between RL and Supervised Fine-Tuning (SFT). In this paper, we argue that multi-modal retrieval can serve as an effective reasoning signal for caption refinem… ▽ More Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads to a performance gap between RL and Supervised Fine-Tuning (SFT). In this paper, we argue that multi-modal retrieval can serve as an effective reasoning signal for caption refinement. Based on this insight, we present the Retrieval-Guided Refinement for Image Captioning (Re$^3$Cap), a retrieval-guided reasoning strategy that enhances image captioning without requiring additional annotations. Instantiated by Caption Refinement Suggester (CRS) and Caption Quality Assessor (CQA), this strategy identifies hallucinations and omissions in image captions, leading to more accurate and detailed descriptions. Extensive experiments demonstrate the superiority of our method in image captioning, even compared with Supervised Fine-Tuning. Especially, Re$^3$Cap outperforms GRPO with an average improvement of 8.64% in relation reasoning on the COCO-LN500 benchmark. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: Accepted to EMNLP 2026 Main Conference

Random quantum circuits, chaos and quantum thermalization

Authors: J. T. Chalker

Abstract: These notes accompany lectures given in June 2025 at the summer school \emph{Fundamental Problems in Statistical Physics XVI}. They offer a short introduction to random quantum circuits as simple models for generic many-body quantum systems. They give an outline of the motivation for introducing these models, starting from ideas of random matrix theory. They also provide a sketch of calculations o… ▽ More These notes accompany lectures given in June 2025 at the summer school \emph{Fundamental Problems in Statistical Physics XVI}. They offer a short introduction to random quantum circuits as simple models for generic many-body quantum systems. They give an outline of the motivation for introducing these models, starting from ideas of random matrix theory. They also provide a sketch of calculations of some of the quantities of most physical interest, based on an average over an ensemble of systems. These quantities give insights into operator spreading, entanglement dynamics and spectral correlations. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 18 pages, 23 figures

Journal ref: J. Stat. Mech. (2026) 064003

Fault Diagnosis of Dynamic Systems Under Unknown Operating Conditions: A Condition-Guided Selective Adaptation Approach

Authors: Jiaming Liu, Zeyi Liu, Hongshuo Zhao, Pengyu Han, Xiao He

Abstract: Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition a… ▽ More Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition adversarial learning strategy with progressive training is developed in the offline stage to enhance the generalization ability of the diagnostic model. During online deployment, residual operating-condition responses are exploited to identify reliable unlabeled samples from streaming data, which are then used to update the diagnostic model. Extensive experiments on real-world gearbox and motor datasets have demonstrated that the proposed framework outperforms state-of-the-art methods in diagnostic accuracy while maintaining relatively low test-time, showing its potential for practical industrial applications. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Human-AI Collaboration in Requirements Engineering: Evidence of the Negative Effect of LLMs on Requirements Inspection

Authors: Giovanna Broccia, Julian Frattini, Chetan Arora, Maurice H. ter Beek, Alessandro Fantechi, Andreas Vogelsang, Alessio Ferrari

Abstract: Background. Requirements inspection (RI) is a well-established practice for detecting potential defects in requirements artifacts early in the software lifecycle. Recent advances in large language models (LLMs) have stimulated interest in their potential to support requirements engineering (RE) tasks. However, empirical evidence on the effects of LLMs when used as collaborative assistants in human… ▽ More Background. Requirements inspection (RI) is a well-established practice for detecting potential defects in requirements artifacts early in the software lifecycle. Recent advances in large language models (LLMs) have stimulated interest in their potential to support requirements engineering (RE) tasks. However, empirical evidence on the effects of LLMs when used as collaborative assistants in human-performed RI remains scarce. Aims. We aim to investigate the impact of LLM support on human-performed RI, considering inspection effectiveness in terms of smell identification and severity classification (i.e., nocuous vs innocuous), as well as inspection duration. Method. We conducted a controlled crossover design experiment with 34 participants, who inspected textual specifications with and without LLM support, identifying and classifying requirements smells while recording inspection time. We analyzed the data using one Bayesian regression model per outcome variable, accounting for validity threats induced by the crossover design as well as covariates and mediators. Results. Results show that LLM support negatively affects smell detection accuracy but has no significant effect on smell classification or task duration. A learning effect is present across experimental periods, but reduced when RI is first performed with LLM support. Conclusions. Our findings provide empirical evidence that LLM support does not necessarily improve performance and may, instead, hinder it for novice inspectors. Moreover, the results suggest that learning RI with LLM-support from the beginning may slow down the skill acquisition process, implying threats for LLM-supported learning. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Neural quantum states in condensed matter: advances, best practices, and prospects

Authors: Jonas B. Rigo, Björn J. Wurst, Rajah Nutakki, Markus Schmitt, Dante Kennes

Abstract: Neural quantum states provide flexible variational representations of quantum many-body wave functions by combining neural-network parametrizations with Monte Carlo sampling. In this perspective, we review recent advances in their application to condensed-matter systems, focusing on frustrated quantum magnets, interacting lattice fermions, and non-equilibrium dynamics. We discuss the architectures… ▽ More Neural quantum states provide flexible variational representations of quantum many-body wave functions by combining neural-network parametrizations with Monte Carlo sampling. In this perspective, we review recent advances in their application to condensed-matter systems, focusing on frustrated quantum magnets, interacting lattice fermions, and non-equilibrium dynamics. We discuss the architectures, symmetry constraints, optimization methods, and sampling strategies underlying state-of-the-art calculations, and summarize practical guidelines for reliable simulations. We also examine the principal remaining challenges, including learning non-trivial sign and phase structures, controlling variational bias, enforcing physical symmetries, scaling optimization to large networks, and achieving stable real-time evolution. Finally, we outline promising directions in which neural quantum states may extend the reach of classical simulations of strongly correlated quantum matter. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 42 pages, 9 figures; perspective article

Oblique Bragg-Doppler scattering at relativistic grating created by backward Raman amplification in hydrogen

Authors: Wenhong Lai, Jiapeng Huang, Haozhe Guo, Philip St. J. Russell

Abstract: We report the first observation of relativistic "Bragg-Doppler" reflection, which occurs when a light beam is incident on a fine-period phase grating moving at close to the speed of light. The grating is created in hydrogen-filled hollow-core fibre by seeded backward stimulated Raman scattering, which creates an intense coherence wave of molecular vibrations at 125 THz, with wavelength 299 nm, mov… ▽ More We report the first observation of relativistic "Bragg-Doppler" reflection, which occurs when a light beam is incident on a fine-period phase grating moving at close to the speed of light. The grating is created in hydrogen-filled hollow-core fibre by seeded backward stimulated Raman scattering, which creates an intense coherence wave of molecular vibrations at 125 THz, with wavelength 299 nm, moving at ~c/8. When this grating is obliquely probed at a generalized Bragg angle, a reflected beam emerges, Doppler-shifted by 125 THz. The effect is also uni-directional, as phase-matching is strongly violated when the probe beam is reversed, so may be viewed as an ultra-high frequency Bragg-cell frequency-shifter. The wide transparency window of hydrogen allows frequency shifting of radiation from the visible to the vacuum UV, simply by tuning the incident angle. The results open up new opportunities for spatio-temporal studies of coherence wave dynamics, frequency conversion in difficult-to-access spectral regions such as the deep and vacuum UV, and quantum-state-preserving frequency conversion of single photons. △ Less

Submitted 21 August, 2026; originally announced August 2026.

WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition

Authors: Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin, Ziang Liu, Max Whitton, Madelyn Hair, Liam Gutierrez, Haozheng Yu, Kristin Branson, Vivek Jayaraman, Michael A. Gil, Andrew M. Hein, Jennifer J. Sun

Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging this data is the high cost of expert annotation. While computer vision offers a potential solution, current models frequently fail when deployed in complex marine environments. To characterize these failures, we introduce WildFin, a novel benchmark… ▽ More Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging this data is the high cost of expert annotation. While computer vision offers a potential solution, current models frequently fail when deployed in complex marine environments. To characterize these failures, we introduce WildFin, a novel benchmark for fish behavior recognition collected and annotated by ecologists.WildFin spans two critical real-world paradigms: stationary cameras monitoring groups of fish and dynamic divers following individual subjects. The dataset represents a massive curation effort, involving 1,350 hours of fieldwork and 600 hours of expert annotation to produce 9 hours of behavioral data with over 2 million frame-by-frame labels. We benchmark modern vision foundation models and quantify tradeoffs between static and spatiotemporal architectures, revealing the substantial gap that remains between current model capabilities and the demands of real-world underwater behavioral analysis. Project website: https://team-wildfin.github.io/. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 31 pages, 4 figures ECCV Marine 26 Workshop

The [Ne V] AGN Diagnostic in SDSS-IV/eBOSS: An Anticorrelation Between Ionization State and AGN Luminosity in Low-Redshift Coronal-Line Galaxies

Authors: Owen S. Matthews Acuña, Christy A. Tremonti, Nikko J. Cleri, Kyle B. Westfall, Bee R. Erena, Jacob B. Stimac, Britt Lundgren, Drake Miller III, Aleksandar M. Diamond-Stanic

Abstract: Traditional narrow-line diagnostics fail at high redshift, where low metallicity drives star-forming galaxies and Active Galactic Nuclei (AGN) into overlapping regions of the Baldwin-Phillips-Terlevich (BPT) diagram. Coronal lines, with ionization potentials exceeding 100 eV, offer a robust alternative: they cannot be produced by normal stellar populations, arise almost exclusively from AGN or fas… ▽ More Traditional narrow-line diagnostics fail at high redshift, where low metallicity drives star-forming galaxies and Active Galactic Nuclei (AGN) into overlapping regions of the Baldwin-Phillips-Terlevich (BPT) diagram. Coronal lines, with ionization potentials exceeding 100 eV, offer a robust alternative: they cannot be produced by normal stellar populations, arise almost exclusively from AGN or fast shocks, and are insensitive to abundance evolution. Among these, the [Ne V] doublet is the brightest optical coronal line tracer of AGN activity. Existing coronal line catalogs contain only about 1750 objects, too few to statistically assess the completeness and purity of [Ne V]-selected AGN samples. We identify 33,817 galaxies with [Ne V] S/N > 5 at z = 0.147-1.12 in the Sloan Digital Sky Survey IV extended Baryon Oscillations Spectroscopic Survey (SDSS-IV/eBOSS), exceeding the combined literature total by more than an order of magnitude. [Ne V] preferentially selects rapidly accreting, relatively unobscured AGN; only 41.5% of BPT AGN show detectable emission. By stacking spectra that lack [Ne V] on a grid of black hole mass and [O III] luminosity, we recover [Ne V] in the majority of bins, implying that non-detections reflect limited survey sensitivity rather than a deficit of coronal emission. We find [Ne V]/[Ne III] is only weakly correlated with [O III]/[O II]. Galaxies with high [Ne V]/[Ne III] for their [O III]/[O II] ratio have lower Eddington ratios and are more likely classified as LIERs or Composites. The [Ne V]/[O III] ratio shows a strong anticorrelation with [O III] luminosity, with the most extreme coronal-line strengths found among the lowest-luminosity AGN, in analogy with the accretion states of stellar-mass black holes. Comparison with a sample of z=2-9 [Ne V]-detected galaxies suggests some, but not all, high-redshift AGN follow the same relations. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 23 pages, 16 figures, 5 tables. Submitted to The Open Journal of Astrophysics. For the eBOSS-DAP catalog and associated data products, see https://datalab.noirlab.edu/data/sdss#sdss-iv-eboss-dap-value-added-catalog ; for the eBOSS-DAP source code, see https://github.com/owenmatthewsa/ebossdap

The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization

Authors: Derek R. Benham, Joshua G. Mangelson

Abstract: Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work, we present two complementary localization frameworks that exploit shoreline and water-surface geometry for GPS-denied autonomous surface vessel localization. The first framework leverages LiDAR observations of the water surface to estimate roll, pitch… ▽ More Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work, we present two complementary localization frameworks that exploit shoreline and water-surface geometry for GPS-denied autonomous surface vessel localization. The first framework leverages LiDAR observations of the water surface to estimate roll, pitch, and heave (vertical motion), while recovering global position and heading through direct registration of shoreline observations against a satellite-derived coastline map. The second framework relies solely on passive imagery to detect the shoreline and horizon through semantic segmentation. Using the proposed coastal scene geometry, shoreline distance is inferred from monocular imagery. Shoreline observations are accumulated into short-duration local submaps, registered against the same satellite-derived coastline map, and fused within a hierarchical factor graph. Evaluated across three real-world coastal datasets, the LiDAR pipeline consistently improves trajectory accuracy over standard baselines, while the monocular architecture maintains bounded long-term drift. In addition, we establish that modern zero-shot foundation models can reliably extract shoreline observations across diverse coastal environments. Together, these results demonstrate that coastal geometry provides a powerful and dependable source of globally referenced information for GPS-denied maritime localization. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 22 pages, 13 figures, 7 tables

Betti and Bass numbers of relative Frobenius maps

Authors: Jenna Judd

Abstract: Motivated by refinements of Kunz's theorem involving the growth of Betti numbers, we study Betti and Bass numbers associated to the relative Frobenius. Under appropriate hypotheses on a local ring homomorphism $\varphi \colon R \to S$, we obtain bounds on the growth of the Betti and Bass numbers of relative Frobenius pushforwards of homologically finite complexes in terms of the corresponding inva… ▽ More Motivated by refinements of Kunz's theorem involving the growth of Betti numbers, we study Betti and Bass numbers associated to the relative Frobenius. Under appropriate hypotheses on a local ring homomorphism $\varphi \colon R \to S$, we obtain bounds on the growth of the Betti and Bass numbers of relative Frobenius pushforwards of homologically finite complexes in terms of the corresponding invariants of the residue field of the closed fiber $S/\mathfrak m S$. These results extend previous work on Betti numbers to coefficients and provide a dual perspective via Bass numbers, contributing to the study of how Frobenius actions encode information about ring homomorphisms. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 16 pages long

MSC Class: 13A35; 13B10; 13D02

Generation of TeV Photons by PeV Neutrinos in Dense Astrophysical Environments

Authors: Jun-Chen Wang, Hanlin Song, Hao Li, Jie Zhu, Bo-Qiang Ma

Abstract: Recent observations by IceCube and KM3Net of PeV-scale ultra-high-energy (UHE) neutrinos, together with detections of TeV-PeV photons from various sources such as the Crab Nebula, the Galactic Center, and gamma-ray burst by ground-based observatories including Tibet AS$γ$, MAGIC, Carpet-3, and LHAASO, point to the existence of extreme astrophysical environments capable of accelerating particles to… ▽ More Recent observations by IceCube and KM3Net of PeV-scale ultra-high-energy (UHE) neutrinos, together with detections of TeV-PeV photons from various sources such as the Crab Nebula, the Galactic Center, and gamma-ray burst by ground-based observatories including Tibet AS$γ$, MAGIC, Carpet-3, and LHAASO, point to the existence of extreme astrophysical environments capable of accelerating particles to ultra-high energies. These findings motivate investigations of possible connections between UHE neutrinos and photons in such environments. Theoretically, dense regions surrounding compact objects can efficiently produce UHE neutrinos. In this work, we calculate the production of UHE photons from neutrino-nucleon interactions, and note that if these interactions occur in the outer, optically thin regions of dense environments, the resulting photons could potentially be observed. In our model, an incident neutrino scatters off a nucleon, generating secondary partons that hadronize into pions and subsequently decay into UHE photons. We calculate the resulting photon energy spectra and find that for incident (anti)neutrinos with energies above 1 PeV, the probability of producing photons with energies exceeding 1 TeV is greater than 13%. As a concrete application, we show that this mechanism can quantitatively account for the preburst TeV photons observed in GRB 221009A, providing a natural explanation for both their energies and lead times. These findings establish a plausible mechanism linking UHE neutrino events to gamma-ray observations, providing new insights into hadronic processes in extreme astrophysical environments and supporting multi-messenger astronomy studies. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 14 pages, 7 figures, final version for journal publication

Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

Authors: Simeng Zhang, Yilong Chen, Wenyuan Zhang, Zhenyu Zhang, Yao Chen, Junyuan Shang, Tingwen Liu

Abstract: Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression shortens generation, yet aggressive compression may disrupt logical coherence and degrade performance. We formalize this trade-off as the \textit{Context-Generation Substitution Law}, where explicit reasoning context sub… ▽ More Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression shortens generation, yet aggressive compression may disrupt logical coherence and degrade performance. We formalize this trade-off as the \textit{Context-Generation Substitution Law}, where explicit reasoning context substitutes for part of decode-time generation. Based on this principle, we propose \textit{Memory-Augmented Compression}, a training-free framework that constructs reusable reasoning memories from historical traces and retrieves them as prefill-side scaffolds. Rather than using raw demonstrations, these memories summarize reusable reasoning patterns, key constraints, and critical operations to compensate for information lost during compression. Experiments show that Memory consistently improves prompt-based Chain-of-Draft (CoD) compression across mathematical reasoning, complex reasoning, and science question answering tasks, yielding accuracy gains of 21.4, 28.0, 29.5, and 6.61 points over CoD on GSM8K, MATH, BBH, and MMLU-Sci, while achieving a 1.14--1.49$\times$ latency speedup over standard CoT. Memory is also compatible with token-level, reasoning-trace-level, and inference-state compression mechanisms. Further analyzes show that the gains come from relevant reasoning memories rather than simply increasing context length. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Exponential-in-$N_c^2$ cost reduction of product-formula-based quantum simulations of quantum chromodynamics

Authors: Zohreh Davoudi, Jesse R. Stryker

Abstract: Quantum algorithms for simulating quantum chromodynamics (QCD) have matured steadily since the pioneering work of Byrnes and Yamamoto [PRA 73, 022328 (2006)]. The most popular strategies for Hamiltonian simulation involve product-formula decompositions. However, the application of product-formula methods to SU($N_c$) lattice gauge theories by Byrnes and Yamamoto leads to $O(Λ^{8(N_c^2-1)})$ gate c… ▽ More Quantum algorithms for simulating quantum chromodynamics (QCD) have matured steadily since the pioneering work of Byrnes and Yamamoto [PRA 73, 022328 (2006)]. The most popular strategies for Hamiltonian simulation involve product-formula decompositions. However, the application of product-formula methods to SU($N_c$) lattice gauge theories by Byrnes and Yamamoto leads to $O(Λ^{8(N_c^2-1)})$ gate complexity per Trotter step, where $Λ$ is the bosonic cutoff in the electric (i.e., irreducible-representation) basis. A seminal work by Kan and Nam [arXiv:2107.12769 (2021)] significantly improves over such an undesirable cost and reports an $O\big(Λ\text{polylog}(Λ)\big)$ scaling, yet it still calls for an unrealistically large number of quantum gates. Here, we illuminate one of the reasons behind this high cost estimate and show that a factor of size $O(2^{4(N_c^2-1)})$ can be removed from the per-Trotter-step cost estimate by Kan and Nam. We specifically show that, by using methods developed in our past works [PRD 112, 014508 (2025); Quantum 7, 1213 (2023)], exponentiated-Hamiltonian decomposition---a necessary step in the application of product-formula algorithms---can be performed far more efficiently than previously thought. Our method reduces the T-gate cost estimate of QCD simulations using a second-order product formula by a factor of nearly $10^{14}$, independent of simulation parameters and sizes. Focusing on simulations in the electric basis, we further contrast our results with other methods: the local-multiplet basis approach of Ciavarella, Klco, and Savage [PRD 103, 094501 (2021)] and the near-optimal algorithm of Rhodes, Kreshchuk, and Pathak [PRX Quantum 5, 040347 (2024)]. This work highlights the importance of continued algorithmic improvement to bringing the quantum-simulation cost of QCD within reach of realistic quantum computers. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 16 pages + bibliography, 3 figures

Report number: UMD-PP-026-06

Model independent lensing sub-structure detection with multiply-imaged star clusters constellations

Authors: Leo W. H. Fung, Tom Broadhurst, Sung Kei Li, Jeremy Lim, Giorgio Manzoni, George F. Smoot

Abstract: A broad class of dark matter (DM) models predicts the existence of sub-structures residing in DM haloes on sub-resolvable angular scales. Techniques to extract such a generic feature from diffraction-limited observations are lacking. In this work, we propose a model-independent 'super-resolving' method applicable to strong gravitational lens systems that is fully data-driven, without reference to… ▽ More A broad class of dark matter (DM) models predicts the existence of sub-structures residing in DM haloes on sub-resolvable angular scales. Techniques to extract such a generic feature from diffraction-limited observations are lacking. In this work, we propose a model-independent 'super-resolving' method applicable to strong gravitational lens systems that is fully data-driven, without reference to any lens models. This method relies on a specific way of applying the optical Liouville theorem across multiple scales in the imaging data, a technique we refer to as geometrical-duality. We test this method using realistic simulations and apply it to a 'constellation' consists of 11 compact star clusters seen in two giant arcs in the lensing cluster SMACS0723 imaged by JWST as a proof-of-concept. We find reasonable self-consistency with the expectation of non-detection given the statistical sensitivity, except for one pair of star clusters. Such an outlier can be explained in the context of CDM as sub-haloes lensing in the mass range $M_{\rm sub} = 10^8 - 10^9\,M_\odot$, for which the corresponding Einstein radius is smaller than the diffraction limit of JWST. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 18 pages, 9 figures (+3 extra figures in the appendix)

EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering

Authors: Xuanyu Meng, Jiashuo Sun, Jash Rajesh Parekh, Jiawei Han

Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw chunks and retrieve them through embedding similarity. Their performance degrades when chunk boundaries separate entities from supporting evidence or when a que… ▽ More Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw chunks and retrieve them through embedding similarity. Their performance degrades when chunk boundaries separate entities from supporting evidence or when a question requires multi-hop reasoning across the corpus. We propose EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index. Each record (e, t, k, v) represents an entity e, its type t, a semantic category k in {property, relation, aspect}, and a value v, while retaining links to the original source passages. At query time, these records serve as retrieval handles, and an LLM synthesizes the retrieved passages into the final answer. This design separates evidence localization from answer synthesis while preserving traceable source evidence. Across Loong and Oolong, EnSI-RAG achieves an average accuracy of 78.24. Relative to the published baseline scores used as references, this is 6.62 points higher, suggesting its effectiveness across these settings. The code is available at https://github.com/RamonMeng/EnSI-RAG. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 21 pages, preprint

Emergent Vibronic Spectral Hierarchy in a Kagome Flat-Band Insulator

Authors: Jun Shu, Jun Shen, Yanmin Zhang, Hong Du, Qingsong Wang, Zeyuan Wang, Bin Wang, Zeliang Xu, Dengjing Wang, Hengfu Lin, Zunming Lu, Lei Qin, Jie Yuan, Jinbo Peng, Zhida Song, Fedor V Kusmartsev, Anna Kusmartseva, Kui Jin, Ruidan Zhong, Ge He

Abstract: Electron-phonon coupling is usually understood in terms of electronic quasiparticles interacting with dispersive lattice vibrations. Much less is known about the complementary limit in which the relevant phonon mode is itself localized or weakly dispersive. Here we investigate this regime in the kagome compound Rb$_{2}$Ni$_{3}$S$_{4}$, which undergoes an unconventional insulating transition near… ▽ More Electron-phonon coupling is usually understood in terms of electronic quasiparticles interacting with dispersive lattice vibrations. Much less is known about the complementary limit in which the relevant phonon mode is itself localized or weakly dispersive. Here we investigate this regime in the kagome compound Rb$_{2}$Ni$_{3}$S$_{4}$, which undergoes an unconventional insulating transition near $T^{*} \approx$ 260-280~K. Combining polarization-resolved Raman spectroscopy with temperature-dependent x-ray diffraction, scanning tunneling microscopy, and electrical, thermal, and magnetic measurements, we show that the transition involves electronic localization without a conventional structural or magnetic order parameter. Raman spectra reveal a giant Franck-Condon progression associated with a nearly dispersionless 333.7~cm$^{-1}$ phonon, decorated by an equally spaced comb-like fine structure with a characteristic spacing of 40.6~cm$^{-1}$. The comb spacing is insensitive to magnetic field, whereas its spectral weight is strongly field tunable. Rather than treating either hierarchy alone as pure phonon effect, we interpret their nested coexistence as evidence for a strongly coupled electron-vibrational manifold involving a localized lattice coordinate. These results identify dispersionless phonons as an active route to vibronic correlations in solids and suggest that such electron-vibrational self-trapping is closely associated with the insulating phase of Rb$_{2}$Ni$_{3}$S$_{4}$. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 10 pages, 4 figures

Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models

Authors: Zhuoyuan Li, Rui Zhao, Jin Wang, Hanwei Zhu, Cong Zhang, Giuseppe Valenzise, Weisi Lin, Kin-Man Lam

Abstract: Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-cache reuse widely adopted in vision-language models and recently explored for embodied agents. In embodied agents, tokens not only support perception and semantic understanding but also directly affect latency-sensitive closed-loop robot action pred… ▽ More Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-cache reuse widely adopted in vision-language models and recently explored for embodied agents. In embodied agents, tokens not only support perception and semantic understanding but also directly affect latency-sensitive closed-loop robot action prediction. Existing schemes typically guide compression using redundancy or importance cues, such as visual similarity, attention scores, and saliency. However, these cues only indirectly measure the key factor for safe compression: how much a token can change before causing an unacceptable deviation in downstream actions. This receiver-dependent tolerance is closely related to the principle of just noticeable difference (JND). Classical JND characterizes signal tolerance in the human visual system, while machine-oriented JND extends this concept to downstream machine responses. Building on this progression, we introduce Action-JND, which extends JND modeling to embodied perception by defining noticeability through the language-conditioned action response of a vision-language-action (VLA) policy in closed-loop control. A token change is considered admissible only when the induced action deviation remains within a tolerated margin. To realize this concept, we develop a lightweight token-wise JND estimator in deep visual-feature space to predict the maximum tolerable perturbation while preserving policy responses. The resulting action-tolerance score serves as a plug-and-play criterion for VLA compression paradigms, including stale-KV reuse and token pruning, prioritizing action-tolerant tokens for compression. Experiments on the LIBERO benchmark with OpenVLA and OpenVLA-OFT demonstrate that Action-JND consistently improves compression reliability, especially under aggressive compression ratios. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 15 pages, 5 figures

A VLM Answer Is Not an Anomaly Score: Rank Compression in Training-Free Video Anomaly Detection

Authors: Inpyo Song, Jangwon Lee

Abstract: Vision-language models enable training-free video anomaly detection by answering questions about video segments. VAD benchmarks, however, require a scalar anomaly score for each segment and evaluate the resulting ranking using the AUROC or AP. A VLM-based detector should therefore define an answer interface: the answer scale specifies the admissible answers, and the readout rule maps the model's o… ▽ More Vision-language models enable training-free video anomaly detection by answering questions about video segments. VAD benchmarks, however, require a scalar anomaly score for each segment and evaluate the resulting ranking using the AUROC or AP. A VLM-based detector should therefore define an answer interface: the answer scale specifies the admissible answers, and the readout rule maps the model's output distribution to a score. Because this interface can change the evaluated ranking, it is part of the detector rather than a formatting detail. The generated readout uses only the most likely answer, whereas the probability readout uses the full distribution over admissible answers. Across four 7-8B VLMs, the probability readout outperforms the generated readout for every tested combination of answer scale, benchmark, and metric, with average gains ranging from 5 to 13 points across the four benchmark-metric pairs. The gap arises because the generated readout keeps only one answer value per segment, so segment with different answer distributions can receive the same score and lose their relative order. We call this loss of relative order generated-answer rank compression. Even when the answer scale allows 91 answers, the generated readout produces only 4-18 distinct scores, whereas the probability readout retains substantially finer score resolution. The advantage persists under every decoding strategy, prompt wording, and joint scoring-explanation prompt we test. The answer interface is therefore a consequential component of VLM-based VAD and should be explicitly specified and evaluated. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: Preprint

Affective Context Amplifies Sycophancy in LLM Responses

Authors: Jiayi Li, Sanjana Menon, Brett Frischmann, Shomir Wilson, Sarah Rajtmajer

Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model's independent evaluation and its user-facing r… ▽ More As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model's independent evaluation and its user-facing response, elicited by presenting the same content as either a third-party account or the user's own disclosure. Across seven LLMs and two Reddit datasets (r/AmItheAsshole and r/TrueUnpopularOpinion), we find that this divergence is systematic and strongly one-directional. User-facing responses consistently soften or withhold negative or oppositional judgments. Affective context further amplifies this divergence with negative states, particularly loneliness and distress, producing the largest effects. These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most, often through evasive sycophancy, in which models retreat toward non-committal responses rather than outright agreement. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)

Authors: Victorita Dolean, Jemima Tabeart

Abstract: These lecture notes form the first part of a master's-level course on advanced numerical linear algebra. Their aim is not only to present the classical algorithms, but to show why the subject has become considerably more central than it was a generation ago. Numerical linear algebra grew up alongside the numerical solution of partial differential equations, and for a long time that is where its la… ▽ More These lecture notes form the first part of a master's-level course on advanced numerical linear algebra. Their aim is not only to present the classical algorithms, but to show why the subject has become considerably more central than it was a generation ago. Numerical linear algebra grew up alongside the numerical solution of partial differential equations, and for a long time that is where its large sparse systems came from. Ranking the nodes of a network, assimilating observations into a weather forecast, and fitting a model to a large noisy data set now lead to problems of the same kind: too large to factorise, structured, and accessible only through matrix-vector products. Strikingly few ideas are needed for all of them. Each chapter therefore develops a standard topic and then puts it to work outside its original setting. We treat norms, factorisations, conditioning and floating-point arithmetic; sparse matrices arising from finite differences, from graphs and from machine learning; stationary iterations and the smoothing property; the conjugate gradient and Lanczos methods, with spectral clustering and regularisation by early stopping; Arnoldi and GMRES, with PageRank and large least squares; and finally preconditioning, Schwarz domain decomposition and multigrid. We assume a first course in linear algebra. Every section closes with a summary of what should be retained and every chapter with exercises, several drawn from past examinations. Accompanying Python code reproduces the numerical illustrations. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 101 pages, 24 figures. Lecture notes; Part I of a two-part master's course. Generative AI (Claude Opus 5, Anthropic) was used to help identify seminal references, improve the language, and improve the graphical content; all statements, proofs, and references have been checked by the authors, who take full responsibility for the contents. Code: https://github.com/vicdolean/scicomp_examples

MSC Class: Primary 65F10 Secondary: 65F08 65F15 65F50 65N22 65N55 ACM Class: G.1.3

Anabelian Geometry in Families

Authors: Tim Holzschuh, Alexander Schmidt, Jakob Stix

Abstract: Anabelian geometry as an attempt to describe geometry in terms of étale topological data has addressed so far mainly categories of varieties over a field. In this paper we work over a normal base scheme $S$ of finite type over a sub-$p$-adic field and show that families of hyperbolic curves over $S$ are anabelian among smooth $S$-schemes with respect to dominant morphisms. Anabelian geometry as an attempt to describe geometry in terms of étale topological data has addressed so far mainly categories of varieties over a field. In this paper we work over a normal base scheme $S$ of finite type over a sub-$p$-adic field and show that families of hyperbolic curves over $S$ are anabelian among smooth $S$-schemes with respect to dominant morphisms. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: Comments very welcome

MSC Class: 14D10 (Primary) 14F35 (Secondary)

Anchoring Instruction Outside Mask: Exact Reference Caching for Efficient In-Context Diffusion Transformers

Authors: Yangshuai Liu, Zheming Li, Jiaao Li, Kang He, Ziliang Lai, Zhitai Liu, Chengru Song

Abstract: Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. It allows diffusion transformers to process text instructions and visual references in a shared attention sequence. However, each reference image introduces thousands of tokens. Computation therefore grows rapidly with the number of references. Existi… ▽ More Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. It allows diffusion transformers to process text instructions and visual references in a shared attention sequence. However, each reference image introduces thousands of tokens. Computation therefore grows rapidly with the number of references. Existing methods reduce computation through structured sparse attention, which limits interactions between reference and target tokens. This structure also makes the reference K and V independent of the denoising target, allowing them to be computed once and reused across steps. However, it blocks visual references from attending to the text instruction. This substantially degrades instruction following and reference fidelity in multi-reference editing. To resolve this conflict, we jointly redesign the token sequence and attention mask. Our beyond-mask design uses static text anchors to connect the instruction to the reference branch. It preserves exact K and V reuse without adding parameters. However, this direct architectural conversion degrades generation quality. We recover the lost performance through teacher-forced velocity distillation, followed by a short on-policy stage in which the teacher supervises student-visited states. To our knowledge, this is the first use of on-policy distillation for architectural recovery in diffusion models. Across three image-editing benchmarks, our method matches full-attention generation quality. With five reference images, it accelerates the complete 40-step denoising process by 3.92x, while static text anchors introduce negligible runtime overhead; the speedup reaches 5.47x at ten references in our scaling study. △ Less

Submitted 21 August, 2026; originally announced August 2026.

The Continuum Model for Uniaxially Strained Bilayer Graphene Moiré Systems

Authors: Tong Liu, X. R. Wang, Jiansheng Wu

Abstract: We construct a continuum model for a one-dimensional moiré superlattice formed by stretching one layer of AB-stacked bilayer graphene along the x direction by a factor s. Following the spirit of the Bistritzer-MacDonald model for twisted bilayer graphene, we treat the interlayer coupling as hopping between several Dirac points. At a critical stretch factor s ~ 1.018 the two bands near the Fermi le… ▽ More We construct a continuum model for a one-dimensional moiré superlattice formed by stretching one layer of AB-stacked bilayer graphene along the x direction by a factor s. Following the spirit of the Bistritzer-MacDonald model for twisted bilayer graphene, we treat the interlayer coupling as hopping between several Dirac points. At a critical stretch factor s ~ 1.018 the two bands near the Fermi level touch, forming two degeneracy points along the k_y direction. This gap closing is accompanied by a topological phase transition, in which the Chern number changes from 1 to -1, and by a sign change of the Berry-curvature dipole, which we propose can be detected through the nonlinear Hall effect. We find that uniaxial strain modulates inter-Dirac-valley coupling, which drives band gap collapse and subsequent topological number inversion. This opens a route to engineer topological transport and quantum anomalous Hall effects via strain engineering of moiré heterostructures. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 17 pages, 13 figures

Ontology-supported AI Model and Dataset Management

Authors: Jan Novacek, Ali Ahari, Tobias Müller, Sebastian Reiter, Alexander Viehl, Oliver Bringmann

Abstract: Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant fo… ▽ More Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: Published in: 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)

Journal ref: 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), Beijing, China, 2024, pp. 1-6

Development of A Novel Compton Camera for MeV Gamma-Ray Measurement in Space

Authors: Pingwei Sun, Wenxiang Fang, Guorong He, Zhen Wu, Jinghe Yang, Jiancheng Zeng, Yiyu Pan, Jiacheng Ding, Enzhao Qi, Jiahao Su, Haoran Yang, Bowen Zhu, Mengjiao Xiao

Abstract: The astrophysical gamma rays in the MeV energy region have not yet been well-explored due to the limitation of detection technology in the past decades, and the famous gamma-ray "MeV gap" exists. Opening the window of MeV gamma-ray is not only critical for the gamma astronomy but also essential for rich frontier researches in astro-particle physics, such as detecting light dark matter, probing the… ▽ More The astrophysical gamma rays in the MeV energy region have not yet been well-explored due to the limitation of detection technology in the past decades, and the famous gamma-ray "MeV gap" exists. Opening the window of MeV gamma-ray is not only critical for the gamma astronomy but also essential for rich frontier researches in astro-particle physics, such as detecting light dark matter, probing the primordial black hole and better understanding of nucleosynthesis. Using the novel scintillators, a three-layer Compton camera with the energy resolution better than 4% and position resolution of ~2 mm is developed. Here we show the design, detailed calibration and validation results of the novel Compton camera, and demonstrate its good ability of MeV gamma-ray source imaging for the upcoming in-orbit mission. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 11 pages, 10 figures

Differential Harnack Estimates for the Filtration Equations on Riemannian Manifolds via Nash--Moser Iteration

Authors: Jian-Hua Hao, Yu-Zhao Wang

Abstract: We prove local differential Harnack estimates for smooth positive solutions of the Filtration Equations $u_t=ΔF(u)$ on complete Riemannian manifolds in uniformly parabolic ranges. A Bochner--discriminant argument gives a coercive positive-part inequality, which is closed by Nash--Moser iteration. We derive Harnack and Liouville consequences, recover the Aronson-Bénilan estimates for porous… ▽ More We prove local differential Harnack estimates for smooth positive solutions of the Filtration Equations $u_t=ΔF(u)$ on complete Riemannian manifolds in uniformly parabolic ranges. A Bochner--discriminant argument gives a coercive positive-part inequality, which is closed by Nash--Moser iteration. We derive Harnack and Liouville consequences, recover the Aronson-Bénilan estimates for porous medium equation and fast diffusion equation, and describe a class of genuinely non-power filtration laws. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 25pages

MSC Class: 58J35; 35K10; 53C21; 35B45

Ramification ideals for products of pure subgroups

Authors: Josnei Novacoski

Abstract: Let $\mathcal E=(L/K,v)$ be a finite Galois extension of henselian valued fields. We study the ramification ideals $I_H$, for subgroups $H\leq {\rm Gal}(L/K)$, when the Galois group is a product of subgroups $H_i$ such that $L/K_{H_i}$ is pure (depth one). We first recall an explicit formula for ramification ideals of pure extensions and use it to obtain a lower bound for the ideals attached to ar… ▽ More Let $\mathcal E=(L/K,v)$ be a finite Galois extension of henselian valued fields. We study the ramification ideals $I_H$, for subgroups $H\leq {\rm Gal}(L/K)$, when the Galois group is a product of subgroups $H_i$ such that $L/K_{H_i}$ is pure (depth one). We first recall an explicit formula for ramification ideals of pure extensions and use it to obtain a lower bound for the ideals attached to arbitrary subgroups of a product of pure subgroups. A natural question is whether every $I_H$ coincides with one of the ideals coming from the pure factors. We show that this is not true, already for a defectless extension with Galois group $C_p\times C_p$. The counterexample is a compositum of two Artin--Schreier extensions with different ramification breaks; the failure comes from choosing a decomposition which is not compatible with the ramification filtration. Motivated by this example, we introduce ramification-adapted decompositions and prove a filtration-theoretic substitute for the conjectural statement in elementary abelian $p$-extensions. Since every flag of $\mathbb F_p$-vector spaces admits an adapted basis, every elementary abelian $p$-extension admits such a decomposition, and every subgroup ideal is represented by one adapted cyclic factor. If the adapted factors are pure, this representation can be written in the distance-set form occurring in the original conjecture. We also prove that, for an adapted decomposition $\mathcal G=H_1\times\cdots\times H_r$, all ramification ideals are principal if and only if every degree-$p$ extension $L/K_{H_i}$ is defectless. Finally, we discuss the degree-$p^2$ example constructed by Kuhlmann in \cite[Section 3.5]{Topics}. Consequently every basis is ramification-adapted, while principality of all ramification ideals still does not characterize defectlessness. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Personalized Privacy Control in LLMs via Attention Head Intervention

Authors: Junseok Kim, Nakyeong Yang, Kyomin Jung

Abstract: The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLMs regulate information disclosure according to context-dependent norms. However, acceptable disclosure boundaries may vary across users even within the same context. To address this limitation, we introduce \textit{personalized privacy}, which inco… ▽ More The rise of agentic AI enables LLMs to access diverse user data, raising critical privacy concerns. Prior work on contextual privacy studies whether LLMs regulate information disclosure according to context-dependent norms. However, acceptable disclosure boundaries may vary across users even within the same context. To address this limitation, we introduce \textit{personalized privacy}, which incorporates user-specific disclosure preferences into privacy control. We further present P3Bench~(\textbf{P}ersonalized \textbf{P}rivacy \textbf{P}reservation \textbf{Bench}mark), a novel benchmark extending contextual privacy policies with personalized disclosure policies. Experiments show that prompt-based policies fail to reliably enforce personalized privacy policies, with Qwen2.5-7B and Gemma3-4B showing average policy ignorance ratios of 51.25\% and 74.28\%, respectively. Finally, to address this problem, we propose \textsc{Repair}, a robust inference-time attention head intervention method that adjusts disclosure behavior toward policy-consistent responses. Our method significantly improves adherence to user-specific privacy preferences by reducing cases where the model fails to follow the given policy. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: EMNLP 2026

Workplace Surveillance and Insider Threat Risk Management: Legal Limits and Privacy Harms

Authors: Haywood Gelman, John D. Hastings, Suvineetha Herath, Quentin Covert

Abstract: Workplace surveillance is used by organizations to protect corporate assets and monitor employee productivity. This research presents two central arguments on workplace surveillance: although surveillance serves legitimate organizational purposes, over-surveillance can violate legal requirements and data privacy principles; and a primary security objective of workplace surveillance is the detectio… ▽ More Workplace surveillance is used by organizations to protect corporate assets and monitor employee productivity. This research presents two central arguments on workplace surveillance: although surveillance serves legitimate organizational purposes, over-surveillance can violate legal requirements and data privacy principles; and a primary security objective of workplace surveillance is the detection of insider threats (InT). InT are comprised of individuals with authorized resource access whose intentional or unintentional actions may damage or compromise corporate assets. This paper investigates InT personas to understand behavioral and psychological detection criteria. Employee surveillance tools and techniques are reviewed to characterize the employee surveillance landscape. Workplace privacy laws, examples of over-surveillance, and the resulting privacy harms are addressed. The review identifies research gaps related to over-surveillance, including the generation of excessive alerts that may obscure meaningful InT indicators. The paper concludes with recommendations to improve workplace surveillance transparency, implement InT training programs to improve organizational detection capabilities, and tune InT tools to detect relevant psychological and behavioral indicators. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: 9 pages, 2 tables

Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning

Authors: Varun Giridhar, Anant Khandelwal, Jeremy A. Collins, Ignat Georgiev, Animesh Garg

Abstract: Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a policy that fails cannot learn from that failure without additional human demonstrations. Reinforcement Learning fine-tuning offers a path to self-improvement but has proven difficult to scale to the multi-billion-parameter models underpinning modern robo… ▽ More Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a policy that fails cannot learn from that failure without additional human demonstrations. Reinforcement Learning fine-tuning offers a path to self-improvement but has proven difficult to scale to the multi-billion-parameter models underpinning modern robot policies. We propose Q-Planning, which equips a large visuomotor BC policy with a small off-policy Q-function. Because a Q-function estimates value rather than imitates actions, it can be trained on the same successful demonstrations as the BC policy and later absorb both successful and failed deployment rollouts, an asymmetry BC does not have. We exploit this asymmetry to enable value-guided action selection at inference (a single-step Q-weighted average over BC draws) and online self-improvement that fine-tunes only the Q-function, leaving the BC weights untouched. On LIBERO and bimanual RoboTwin, ten iterations of self-improvement lift every benchmark score we tested (LIBERO-10 93% to 99%, RoboTwin 83.8% to 91.4%) and shorten successful episodes on the near-ceiling suites (LIBERO-Object, LIBERO-Goal). On two contact-rich bimanual real-robot tasks, the same loop (BC frozen, no human intervention) improves purely from its own deployment rollouts: stack-cups 40% to 90% and insert-wallet 25% to 80% in five iterations, whereas SFT on successful rollouts alone stalls at 55% and 30%. Under an identical online budget Q-Planning is the only method, among Best-of-N, filtered SFT, IBRL, DSRL, and DAWR, that improves stably from failures without training an auxiliary actor. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Comments: Project page with videos: https://varungiridhar.github.io/qplanning/