Published: 2026-07-13 17:59:58
Authors: Han Gil Choi, Juan Urrutia, Miguel Zumalacárregui
Categories: astro-ph.CO, astro-ph.HE, hep-ph
Abstract:
Cold Dark Matter predicts a population of low-mass halos which are sensitive to its fundamental nature and the primordial power spectrum, yet remain undetected. Although elusive, their discovery may be possible thanks to wave-optics lensing of gravitational waves (GWs) by the superposition of many halos along the line of sight. We study the statistical properties of stochastic diffractive lensing, which imprints correlated fluctuations on the amplitude and phase of the original waveform. The stochastic distortions can be described by an orthogonal basis that captures the dominant ''tones'' associated with the dark matter properties, or dark timbre, which is not degenerate with binary source parameters. LISA is most sensitive to halos of $O(10\text{--}10^4\,M_\odot)$, and because the imprint recurs in every source, stacking $\sim(50,500)$ loud binaries could confirm them at the $(2,5)σ$ level ($\sim0.2$ to $\gtrsim4σ$ for realistic merger rates and different concentration estimations). The per-event signal is only $O(10^{-3})$ in cold dark matter, demanding major advances in waveform accuracy and data analysis. Even short of that reach, stochastic diffraction places stringent bounds on models that enhance small-scale structure, such as axion miniclusters and primordial black holes.
Published: 2026-07-13 17:58:50
Authors: Shikai Qiu, Marc Finzi, Yujia Zheng, Kun Zhang, Andrew Gordon Wilson
Categories: cs.LG
Abstract:
Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization. Large neural networks may learn functions far simpler than their parameter counts suggest, but it is challenging to construct codes that realize this simplicity. Parameter-based methods such as quantization produce code lengths that scale with model size, insensitive to how much information the parameters store. Prequential coding bypasses this issue by compressing the training trajectory, but codes the exact data sequence regardless of how much the model learns, yielding large codes when the data has high entropy. We introduce requential coding, where a teacher model selects training samples drawn from the student's own distribution. The student's code records only these selections, which cost bits only where teacher and student disagree. The resulting code length is independent of parameter count and data entropy, and often orders of magnitude shorter than the prequential counterpart, with an advantage that grows with scale. This compression sheds light on phenomena inaccessible to prior compressors. Holding loss fixed, larger models and ensembles compress to much smaller sizes despite more parameters. Plugged into a PAC-Bayes bound, the requential code yields state-of-the-art generalization guarantees for billion-parameter LLMs, outperforming bounds built on aggressive post-training quantization even granted zero error. The bound tightens with scale in the compute-optimal regime, as models become increasingly compressible relative to dataset size. The same code predicts that models gradually overfit when trained for multiple epochs. It also isolates the learnable information in a dataset from its unpredictable, random content, revealing that lower-entropy text holds far more learnable structure than higher-entropy image data.
Published: 2026-07-13 17:57:45
Authors: Liwei Jiang, Ke Tang, Zhe Zhang
Categories: math.OC
Abstract:
We study deterministic first-order minimization of a convex function without prior knowledge of the objective's growth, smoothness regime, or associated parameters. We develop anytime, parameter-free bundle-level methods that adapt simultaneously to these unknown properties and attain best-known oracle complexities. For nonsmooth Lipschitz objectives satisfying quadratic growth, the proposed bundle-level W-certificate method (BLW) achieves the optimal complexity without requiring the growth modulus or target accuracy as input. We then introduce an accelerated variant, A-BLW. Without knowing the Hölder smoothness parameters, the quadratic-growth modulus, or the target accuracy, A-BLW attains the optimal rates in the nonsmooth, weakly smooth, and smooth regimes. Central to both methods is an affine W-certificate, a condition based on the descent-slowness of an affine minorant that converts the geometry of a bundle model into an optimality-gap guarantee under quadratic growth. A stopping-time analysis further shows that the same A-BLW algorithm, without modification, achieves the corresponding best-known rates for general convex objectives and for objectives satisfying Hölder growth of order at least two. Numerical experiments illustrate the practical performance of the proposed methods.
Published: 2026-07-13 17:53:30
Authors: Kejun Liu
Categories: quant-ph, math-ph, math.CV
Abstract:
The memory kernel of an open quantum system obeys Kramers--Kronig (KK) relations if and only if its Laplace transform is analytic in the upper half-plane -- a property known as Hardy-space analyticity. Here we show that non-unitary exchange statistics, the defining property of paraparticles, intrinsically breaks Hardy-space analyticity. The metric $η$ that guarantees a real closed-system spectrum for these particles necessarily differs from the physical Born inner product ($\|η- I\|_F / \|I\|_F = 0.51$) -- a mathematical consequence of the R-matrix's non-unitarity, not a parameter choice. This metric is a "shadow metric": Schur's lemma forces it to commute with every bilinear observable, making the distortion physically invisible in the closed system. But when the paraparticle is coupled to a bath, any coupling operator that lies outside the symmetry algebra -- that is, any interaction that sees the internal flavour structure -- exposes the distortion. The memory kernel then develops upper-half-plane poles at coupling $g_c \approx 0.1$, breaking standard dispersion relations before the closed-system spectrum complexifies. Fermions and bosons, whose exchange is unitary ($η= I$ as an analytic fact of the canonical anticommutation algebra), are immune at any coupling, because there is no distortion to expose. The violation is intrinsic: it distinguishes non-unitary exchange statistics from ordinary particle statistics at the level of the memory kernel's analytic structure.
Published: 2026-07-13 17:52:24
Authors: Weikun Zhu, Natalie Ngoh, Shelly Ben-David, Maxwell Conte, Teddy Hsieh, Sarah O. Spector, Tara Sverko, Patricia Jastrzebska-Perfect, Will Jack, Jinwoo Sim, Peter F. Satterthwaite, Farnaz Niroui
Categories: cond-mat.mtrl-sci, physics.optics
Abstract:
Quantum light sources capable of generating single photons are fundamental building blocks for photonic quantum technologies. In the ongoing search for an ideal quantum emitter, inorganic halide perovskite nanocrystals have emerged as a promising source of single photons. Their unique optical response, with an unmatched ease of synthetic tunability, stands out amongst the competing platforms. However, their stochastic dispersion in solution challenges the deterministic and stable integration of individual emitters with photonic structures that is required for practical technologies. Notably, resolution and material compatibility constraints make conventional top-down fabrication processes insufficient for such heterogeneous integration. Here, we report direct writing of perovskite quantum dots (QDs) with individual-emitter resolution. By inducing a nanoscale-confined formation volume using a thermal scanning probe method, we achieve site-selective synthesis down to a single atomic-scale QD with spectral tunability and < 25 nm spatial control. As a result, we demonstrate high-yield arrays of CsPbI3 single-photon emitters with narrow linewidths and high single-photon purity up to 98% at room temperature, performance consistent with that of their state-of-the-art colloidal counterparts. Through such deterministic control, we uniquely realize the precise, on-demand coupling of these emitters to photonic cavities, as evidenced by a measured enhancement in the spontaneous emission rate. This represents a key advancement toward addressing the longstanding integration obstacles of these materials. Overall, by combining the atomic-scale tunability of chemical synthesis with the spatial control of additive manufacturing, our work opens new emitter engineering strategies to realize the untapped potential of colloidal materials for next-generation quantum technologies.
Published: 2026-07-13 17:50:31
Authors: Junnosuke Koizumi
Categories: math.CO, math.AT
Abstract:
We completely determine the magnitude homology of tope graphs of real hyperplane arrangements. Their ranks can be described as the Hilbert functions of the Stanley--Reisner rings of certain simplicial complexes naturally associated with the arrangements. For Coxeter arrangements, this gives a computation of the magnitude homology of the Cayley graph of the corresponding Coxeter group. We also prove the homological reciprocity for central arrangements conjectured by Koizumi--Liu. The proof combines poset combinatorics, the Edelman--Walker theorem, and Alexander duality.
Published: 2026-07-13 17:38:07
Authors: Sabyasachi Maulik, Soumen Pari
Categories: hep-th
Abstract:
In this paper, we study codimension-two holography in a de Sitter (dS) wedge setup, based on the idea of wedge holography. We consider a $d+1$-dimensional Anti-de Sitter (AdS) bulk spacetime bounded by two end-of-the-world branes with $d$-dimensional de Sitter geometry. We propose that this configuration is holographically dual to a conformal field theory (CFT) living on a $d-1$-dimensional sphere. Our computations of the partition function and holographic entanglement entropy support this duality and indicate that the dual CFT is non-unitary. We also analyze the mass spectrum in dS wedge holography. We verify the first law of entanglement entropy within this framework. Finally, we make use of the island prescription to study the Page curve in a simplified model within our dS wedge holography framework.
Published: 2026-07-13 17:24:28
Authors: Thea Sukianto, Donata Giglio, Mikael Kuusela
Categories: stat.AP, physics.ao-ph
Abstract:
Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.
Published: 2026-07-13 17:23:47
Authors: Huy Che, Dinh-Duy Phan, Duc-Lung Vu
Categories: cs.CV
Abstract:
License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantial computational overhead, limiting their applicability to resource-constrained devices. In this paper, we propose MicroCharNet, an ultra-lightweight model specifically designed for license plate character detection. The proposed architecture employs a compact backbone composed of C2f blocks, integrated with CoordAtt module to enhance feature extraction while preserving spatial information. A lightweight C3k2-based neck fuses multi-level features, followed by a single-level anchor-free detection head that enables end-to-end prediction. Experiments conducted on the UFPR-ALPR dataset demonstrate that MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, while outperforming several recent YOLO-based baselines. Hardware-level evaluations further confirm its efficiency for real-time deployment on edge devices. These results indicate that carefully designed ultra-lightweight architectures can effectively balance accuracy and efficiency in license plate character detection. The source code is available at https://github.com/chequanghuy/MicroCharNet.
Published: 2026-07-13 17:18:53
Authors: Matteo Beccaria, Eleonora Alfinito
Categories: hep-th
Abstract:
We study the low-temperature expansion of the disk partition function $Z(β)$ of the double-scaled SYK model (DSSYK) at fixed coupling $λ=2p^{2}/N$, where $N$ is the number of Majorana fermions and $p$ is the number of fermions in each interaction term, both taken to infinity. We show that the exact Bessel-function representation of $Z(β)$, expanded at large argument (corresponding to low temperature), can be organized in terms of the classical ring of quasi-modular Eisenstein series $E_{2},E_{4},E_{6}$ and their differential identities. Exploiting the modular $S$-duality properties of this ring, we derive the semiclassical (small $λ$) low-temperature expansion of $Z(β)$, splitting it into a perturbative tower and a non-perturbative sector controlled by $\widetilde q=e^{-4π^{2}/λ}$. At each order in $\widetilde q$, we determine the non-perturbative correction in closed form up to second order in $λ$; the resulting series resums into a compact expression in the same Eisenstein series, extending previous semiclassical results beyond their strict $β\to\infty$ limit. We further show that this entire structure follows from a single, exact differential equation coupling a modular derivative to derivatives with respect to temperature. Finally, we prove that the non-perturbative sector of $Z(β)$ is exactly supported, to all orders in $λ$, on the same exponents as the on-shell actions of known bilocal-Liouville saddles of the DSSYK Schwarzian limit, pointing to a well-defined bulk origin for these non-perturbative corrections.
Published: 2026-07-13 17:18:24
Authors: Romain Amigon
Categories: cs.LG, cs.AI, cs.NE
Abstract:
Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days. In this paper, we propose a frugal and memetic NAS framework designed to democratize architecture design on consumer-grade hardware. Our approach combines the global macro-search capabilities of an autoregressive Transformer controller, trained via Reinforcement Learning (RL), with the local micro-exploitation of an Artificial Bee Colony (ABC) algorithm. To prevent premature convergence during the RL phase, we introduce a dynamic entropy mechanism that forces topological exploration upon detection of performance stagnation. Evaluated on a standard GPU (NVIDIA RTX 3060), our hybrid method effectively resolves the "cold-start" problem inherent in metaheuristics. By algorithmically penalizing network depth, our framework actively mitigates model bloat: on the CIFAR-10 dataset, it discovers an efficient architecture reaching 84.85% accuracy with only $\sim$174,000 parameters (significantly smaller than standard baselines like ResNet-20) in 3 hours of search time. Furthermore, we demonstrate the framework's flexibility by applying it to credit card fraud detection, directly optimizing the F1-Score on highly imbalanced tabular data to reach a F1-Score of 0.71 with a compact network of $\sim$4,600 parameters. These results suggest that our approach can yield tailored, accessible, and highly parameter-efficient deep learning models suitable for edge deployment.
Published: 2026-07-13 17:13:09
Authors: Kaixin Ma, Di Feng, Alexander Metz, Jiarui Lu, Eshan Verma, Afshin Dehghan
Categories: cs.CV, cs.AI
Abstract:
We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ tools across 16 application domains, supporting multi-image, multi-turn tasks where agents must ground progressively arriving visual inputs into executable tool calls while handling realistic conversational phenomena (goal revisions, error corrections, state mutations). An automated scenario generation pipeline produces diverse, visually grounded scenarios through information-flow-guided planning and multi-stage quality filtering, yielding 258 human-verified nominal scenarios and 50 variants targeting interactive UI applications. Evaluating 12 state-of-the-art models, from 4B open-weight to frontier proprietary systems, shows that current models still lack robust visual tool-calling capability: even the best model achieves below 50% success rate. Our failure analysis further reveals that visual precision, not only planning, is a primary bottleneck for capable models: 53% of failures stem from incorrect information extraction from images despite otherwise correct task workflows. A planning-to-precision crossover emerges with scale: smaller models fail at deciding what to do, while larger models fail at perceiving what they see, suggesting fundamentally different research directions for improving models at different capability levels. The framework and the benchmark are publicly available at https://github.com/apple/ml-mmtoolsandbox
Published: 2026-07-13 17:10:24
Authors: Xuhui Yang, Guofu Cao
Categories: hep-ex, physics.optics
Abstract:
Characterizing emission and decay time spectra in multi-component fluorescent media is essential for identifying intrinsic material properties and optimizing detectors. However, wavelength evolution from the secondary inner filter effect (IFE) distorts these observable spectra. While Monte Carlo (MC) ray-tracing can simulate this distortion, accumulating adequate tracking statistics requires long computation times, which hinders parameter optimization within high-dimensional spaces. This paper presents an analytical Markovian transport model based on spatiotemporal decoupling. A Laplace transform converts the multi-nested convolution integrals over continuous domains into a discrete Markov transition matrix, reducing the computational complexity from an exponential scale with respect to wavelength bins $N_λ$ and cascade order $n$, $\mathcal{O}(N_λ^n)$, to a linear scale, $\mathcal{O}(N_λ + n)$. The resulting algebraic solutions evaluate transient decay time spectra as a continuum superposition of Gamma wave packets and predict steady-state wavelength spectrum distortions driven by the IFE within a sub-second timescale. Validations across orthogonal and front-face spectrometer configurations show that the calculated spectra match MC simulations in lineshape. This model can serve as a fast forward engine to accelerate parameter space screening, provide early-stage detector design references, and act as a physics-constrained input for event vertex reconstruction algorithms.
Published: 2026-07-13 17:10:15
Authors: Daiki Saito, Atsushi Naruko
Categories: astro-ph.CO, gr-qc
Abstract:
We investigate the long-wavelength evolution of linear perturbations in a homogeneous and anisotropic background with a scalar field coupled to a vector field. Using the spatial gradient expansion in the uniform-$\mathcal{N}$ gauge in which the number of $e$-folds is unperturbed, we derive the complete set of superhorizon solutions and establish their correspondence with infinitesimal variations of the homogeneous anisotropic background. This extends the separate-universe picture, previously known for isotropic FLRW cosmology, to anisotropic spacetimes despite the mixing of scalar, vector, and tensor perturbations induced by broken rotational symmetry.
We show that the long-wavelength equations form a self-consistent system and identify a conserved quantity that generalizes the conserved Wronskian of isotropic cosmology. Unlike the isotropic case, the superhorizon modes sourcing the curvature perturbation are governed by three independent conserved channels associated with the scalar field, the background shear, and the gauge-field tilt, together with an additional dynamical shear contribution originating from the anisotropic geometry. This reveals that the evolution of curvature perturbations around anisotropic background is intrinsically richer than in isotropic multi-field models.
Our formulation provides a practical prescription for computing the final curvature perturbation directly from horizon-crossing fluctuations, thereby establishing the anisotropic generalization of the $δN$ formalism. We further derive an explicit relation between curvature perturbations and primordial gravitational waves, demonstrating how anisotropic expansion couples scalar and tensor sectors on superhorizon scales. Our framework provides a practical basis for predicting statistical anisotropies in primordial scalar and tensor perturbations.
Published: 2026-07-13 17:05:25
Authors: Giulia Di Fede, Salvatore Andolina
Categories: cs.HC
Abstract:
Large Language Models (LLMs) can make exploratory search more efficient but may undermine the reflection and iterative sensemaking needed in unfamiliar domains. Existing LLM tools often prioritize rapid answers over supporting users in tracking how their understanding evolves and how well their strategies align with their goals. We present TrailLM, a system that helps users reconstruct and revisit their exploration paths to support reflection and metacognitive engagement during information seeking. By aligning LLM assistance with users' sensemaking workflows, TrailLM aims to preserve the benefits of LLM-based search while enhancing opportunities for critical reflection on one's own search process.
Published: 2026-07-13 16:53:39
Authors: Alfonso M. Ganan-Calvo
Categories: physics.flu-dyn
Abstract:
Gas-focused liquid microjets -- the flow-focusing sample delivery on which serial femtosecond crystallography depends -- reach speeds several times the pressure-driven (Bernoulli) bound, unexplained by continuum, local-equilibrium models that do not resolve the rarefied, hypersonic expansion of the focusing gas. We resolve that expansion with a deterministic kinetic (Shakhov--BGK) solver and couple it to the slender liquid jet. The jet is \emph{shear-driven}, not pressure-driven: the tangential stress of the hypersonic gas supplies nearly all of the axial momentum, accounting for the anomalous speed. The gas does not become ballistic behind the near field -- its stress decays as a power law and it stays coupled -- and its constitutive regime is set by a single rarefaction parameter $δ=D/\ell_0$, the orifice diameter over the source mean free path, through the thermodynamic Deborah number $De_θ\simeq K\!n\,M$ (Knudsen times Mach), whose $De_θ=1$ surface maps where the Newtonian-gas closure fails: the small-$δ$ vacuum corner where crystallography jets operate. The kinetically computed surface stress is the input for the fully non-Newtonian (viscoelastic-liquid) sequel.
Published: 2026-07-13 16:53:08
Authors: Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu, An-Yu Cheng, Hung-yi Lee
Categories: cs.SD, cs.AI
Abstract:
Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content. Improving this without the cost of retraining calls for an effective inference-time intervention, yet most existing methods intervene only after the audio encoder and operate at a relatively coarse granularity. The encoder itself, where acoustic information is first extracted from the waveform, remains largely unexplored, especially at the level of individual neurons. We introduce IAAN, Identifying and Amplifying Acoustic Neurons, a training-free and label-free method that scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference lacking the real audio's acoustic information. IAAN then amplifies a small set of the highest-scoring neurons at inference. Across ten non-semantic speech attributes, IAAN improves average accuracy by 25.7 points on Audio-Flamingo-3, 21.4 on Qwen2.5-Omni, and 9.7 on Kimi-Audio. It also improves a model already explicitly fine-tuned to prioritize acoustic evidence. In controlled comparisons, both the encoder locus and neuron-level selectivity prove necessary for this gain. Intervening after the encoder, at the decoding side or inside the language model, yields little to no improvement, or even deteriorates accuracy. The improvement also depends on which specific neurons are amplified, not merely on their number, confirming that IAAN's acoustic score succeeds in identifying the neurons that matter. These results show that a small, precisely targeted intervention inside the audio encoder is an effective and largely untapped way to strengthen the acoustic understanding of LALMs, opening a new direction for inference-time methods that improve acoustic perception through neuron-level access to the encoder.
Published: 2026-07-13 16:50:22
Authors: Rajam Elancheliyan, Jean Marc Fromental, Edouard Chauveau, Domenico Truzzolillo
Categories: cond-mat.soft, cond-mat.mtrl-sci
Abstract:
Yield-stress fluids undergo a singular solid-to-liquid transition at a critical stress threshold. While conventionally investigated under steady shear, large-amplitude oscillatory tests force these materials to cyclically navigate between arrested and fluidized states. Here, we uncover a hidden universality in their non-linear oscillatory response: at sufficiently low frequencies their first-harmonic viscoelastic moduli collapse onto master curves against strain amplitude. This collapse reflects an invariant intra-cycle stress plateau, showing that the material rearranges almost instantaneously to maintain a constant stress state governed by a unique temporal trajectory of its relaxation time. We capture this phenomenology using a new fluidity model derived from a Lyapunov function exhibiting symmetry breaking. Our framework reveals that recoverable elastic energy, fragility, and the entropy produced during stress inversion are fundamentally intertwined, defining a single viscoplastic parameter that governs yielding abruptness and provides a novel thermomechanical foundation for the dynamic yield stress.
Published: 2026-07-13 16:48:56
Authors: Shahaf Aharony Shapira, Gene Chen, Marija Vucelja, Oren Raz
Categories: cond-mat.stat-mech
Abstract:
The Mpemba effect is the counterintuitive phenomenon in which an initially hotter system cools faster than a colder, otherwise identical system. It has been experimentally demonstrated in various classical overdamped systems. Here, we explore the existence of the same effect in a regime where inertia cannot be neglected, namely, the underdamped regime. We consider the underdamped dynamics of a Brownian particle in a potential. We show perturbatively that, if the effect exists in the overdamped limit, it persists for sufficiently large but finite damping. In the ultra-weak-damping limit, we show that the effect cannot occur for smooth confining single-well potentials with canonical initial states, but can arise in more complex potentials. We demonstrate our results numerically using double-well potentials, the canonical setting for the Mpemba effect in the overdamped limit.
Published: 2026-07-13 16:47:38
Authors: Himadri Halder, Rohit Kumar
Categories: math.CV
Abstract:
The aim of this paper is twofold. First, we establish a Riesz conjugate theorem for weighted harmonic Bergman spaces. More precisely, we prove that if $f=u+iv$ is a harmonic $K$-quasiregular mapping in $\mathbb{D}$ and the real part $u$ belongs to the weighted harmonic Bergman space $a_α^p$, $0
Published: 2026-07-13 16:37:17
Authors: Landon Liu, Mary Kelly, Alan Tsang
Categories: cs.MA, cs.CL, cs.GT, cs.HC
Abstract:
Shared meaning in language requires people to learn and agree on categories. We ask how characteristics of agents' memories change the emergence and evolution of shared meaning. Without a coordination game, models of conceptual semantics cannot explain how shared meaning emerges and changes in groups of people; however, existing games assume that players share payoffs in a partnership setting. We model conceptual alignment as a non-partnership game and illustrate differences in actual and perceived conceptual convergence from counterfactual simulations using agents with varying levels of adaptiveness and memory degradation. We found that adaptive players achieved actual convergence faster and had closer final conceptual regions than non-adaptive players, while non-adaptive players perceived convergence earlier. Weighing novel information less over time resulted in more stable agreements than fixing the weight of novel information. Memory features are critical to the emergence and evolution of actual and perceived convergence.
Published: 2026-07-13 16:37:00
Authors: Haomin Sun, Plamen G. Ivanov, Justin Ball, Stephan Brunner, Bhavin S. Patel
Categories: physics.plasm-ph
Abstract:
Equilibrium sheared $\vec{E}\times \vec{B}$ flow, a standard cure for plasma turbulence, can backfire. In gyrokinetic simulations of a newly identified regime, imposed shear comparable to the intrinsic zonal shear destroys the self-generated zonal flows regulating the turbulence: transport rises sharply before stronger shear quenches it. A reduced fluid model traces this to the incompatibility of imposed and zonal shear layers. Simulations of spherical tokamak discharges place the inferred rotation shear at, or just below, the threshold of the sharp transport increase, implying that the toroidal rotation may be limited mainly by the heat, not momentum, injection.
Published: 2026-07-13 16:36:59
Authors: Elmira Salari, Hazem Amamou, José Victor de Souza, Shruti Kshirsagar, Maria Nunes Delfino, Anderson Avila
Categories: cs.CL
Abstract:
Retrieval-Augmented Generation (RAG) has been increasingly adopted to reduce hallucinations and strengthen the factual grounding of large language models (LLMs). While robustness to errors in the retrieval process has been explored, the impact of ideological bias on LLM outputs has been overlooked. For instance, if the retrieved material contains ideological positions, the RAG may transmit, amplify, or suppress such ideological discourses in its outputs. In this study, we address this issue by examining the influence of the RAG framework, comprising ideological discourses, in LLM-generated answers. To this end, we applied Lexical Multidimensional Analysis (LMDA) on a corpus of 1,117 COVID-19 treatment articles, identifying three ideological discourses. This corpus is then used as the external knowledge source for the RAG. We assessed several LLMs by having the models answer ideological questions at different sampling temperatures. The generated texts were assessed semantically and lexically based on their similarities with ideological reference texts. Our findings show that the RAG framework is prone to transferring ideological discourses into LLM responses, with sampling temperature having a measurable impact on the strength of this transfer. Discoursive alignment between generated answers and the reference text is highest at moderate temperatures, where models balance stochasticity with retrieval grounding, and drops at low temperatures, indicating that overly deterministic sampling suppresses discourse transfer.
Published: 2026-07-13 16:33:11
Authors: K. V. Shuddhodan, V. Srinivas
Categories: math.AG
Abstract:
Let \(X\) be a proper reduced scheme over a finite field \(k\), let \(\ell\) be a prime different from \(\operatorname{char} k\), and write \(\ol X=X\times_{k}\ol k\) for its base change to an algebraic closure \(\ol k\) of \(k\). Call a class in \(\rH^{2}_{\et}(\ol X,\bbZ_{\ell}(1))\) \emph{Zariski-locally trivial} if it vanishes on a Zariski-open cover of \(\ol X\). We prove that the first Chern class map identifies \(\NS(\ol X)\otimes\bbZ_{\ell}\) with the group of Zariski-locally trivial classes whose image in \(\rH^{2}_{\et}(\ol X,\Ql(1))\) has weight zero. This is the finite-field analogue of a theorem of Barbieri-Viale--Rosenschon--Srinivas for proper seminormal complex varieties. In the finite-field setting neither seminormality nor irreducibility is needed.
Published: 2026-07-13 16:23:57
Authors: Alexander F. Zakharov, Predrag Jovanovic, Dusko Borka, Vesna Borka Jovanovic
Categories: gr-qc, astro-ph.CO, astro-ph.GA
Abstract:
Along with a whole range of alternative theories of gravity, variants of the massive theory of gravity, i.e. the theory of gravity, in which the graviton has mass, have been actively discussed in recent years. Theorists have proposed versions of massive gravity theories that address the shortcomings of early versions of such theories. Astrophysicists and experimental physicists have been discussing limitations on the graviton mass from various astronomical observations. In particular, in the first LIGO paper, where the discovery of gravitational waves from binary black holes was reported, a limit on the mass of the graviton was obtained from the analysis of the profile of the gravitational wave signal. In the paper graviton mass constraints were obtained by analyzing the trajectory of a bright star in the vicinity of the center of our Galaxy, using observations from the GRAVITY and Keck groups. Briefly other astronomical ways to limit a graviton mass were discussed.
Published: 2026-07-13 16:13:10
Authors: Iago Nascimento Barros, Marcelo Lobato Martins, Celia Anteneodo
Categories: cond-mat.stat-mech, physics.soc-ph
Abstract:
We develop a Fokker-Planck description of the dynamics of wealth distribution in a stochastic multiplicative economic growth model with taxation and redistribution, as introduced by P.M.C. de Oliveira. Extending the original formulation, our theoretical framework includes general redistribution protocols, encompassing a broad class of state-dependent transfer mechanisms. As a particular case, we investigate a two-state protocol designed to emulate conditional cash transfer programs. Analytical expressions for the stationary wealth distributions are derived, revealing how the interplay between multiplicative noise, taxation, and redistribution shapes the emergence of inequality. The theoretical results are corroborated by agent-based simulations. To quantify and compare the impact of the different protocols, we employ the Gini index as a measure of inequality. Our analysis highlights how specific nonuniform redistribution schemes can significantly mitigate wealth disparities.
Published: 2026-07-13 16:08:46
Authors: Yibo Hu, Ren Wang
Categories: cs.CR, cs.LG, cs.MA
Abstract:
As multi-agent, tool-using LLM systems are deployed, a common safety net is a runtime monitor that checks each message, tool call, or step on its own. We show this net has a fundamental hole. A distributed backdoor splits a harmful payload across agents, so every local check passes while the assembled object is the attack. The monitor can be right on every step and still miss the attack. The problem is not splitting itself: split fragments can still leak suspicious tokens or provenance edges. The hard case is \emph{local benignness}. No fragment carries the harm, and what is left looks like ordinary benign traffic. We formalize this as an \emph{observability boundary}: a monitor catches only what its view can tell apart from benign traffic. We prove that once the fragments look benign in the monitored view, no detector on that view can catch them, however strong it is. Across a controlled testbed, an external benchmark, and end-to-end agent runs, local monitors lose the signal exactly as local evidence disappears, and it returns only when the monitor sees the assembled object. A monitor trained only on benign traffic recovers the attack's code structure across held-out encodings (0.874 mean AUROC). A decoded-view gate, given the encoding family, blocks every tested attack. But seeing more is not enough: full-trace monitors and decoders still fail unless they reach the representation where the payload is exposed. Local safety is not global safety when harm is compositional, and the open problem is finding that representation.
Published: 2026-07-13 16:05:16
Authors: Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, Pierpaolo Mori, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele
Categories: cs.LG, cs.AR
Abstract:
With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential. These methods rely on real feedback from the target hardware to tailor DNN architectures for efficient deployment. While the search can be parallelized, latency measurements via hardware-in-the-loop (HIL) remain a bottleneck due to their sequential nature. Recent approaches use latency predictors to replace costly HIL feedback, but challenges persist: (1) platform-specific predictors often require tens of thousands of samples, and (2) inaccurate predictions can mislead the NAS process. To address this, we introduce HiFi-LLP, a high-fidelity, low-cost latency predictor based on graph attention networks, augmented with a confidence metric. HiFi-LLP outperforms prior platform-specific predictors by up to 9 percentage points (p.p.) in the 10% accuracy bound and achieves a Spearman's rank correlation of up to 0.996 across six devices in the LatBench dataset. We further propose a hybrid NAS framework that routes low-confidence predictions to HIL, achieving up to 8.6$\times$ speedup compared to typical NAS while maintaining a competitive Pareto front.
Published: 2026-07-13 16:00:41
Authors: Ziyang Zhang, Boyang Zhou, Zesong Yang, Haocheng Peng, Zeming Gai, Xiao Liang, Yujun Shen, Danping Zou, Ruizhen Hu, Hujun Bao, Zhaopeng Cui
Categories: cs.RO
Abstract:
Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors are inherently multimodal because multiple paths may exist around obstacles. In this paper, we formulate navigation as learning a transferable goal-conditioned stochastic path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations. This formulation enables structured sampling of navigation candidates, allowing multiple feasible paths to be explored through sampling without relying on robot-specific motion constraints. To this end, we introduce a goal-aligned canonical state representation that removes in-plane rotational ambiguity and normalizes local geometry with respect to the goal, enabling rotation-invariant path distribution learning. We further develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning. Extensive experiments in dense static environments and dynamic pedestrian scenarios demonstrate that the proposed method achieves consistently high success rates with competitive efficiency while enabling cross-platform transfer of a single path prior learned on differential-drive robots to quadruped platforms without retraining.
Published: 2026-07-13 15:58:32
Authors: Zhen Li, Lotta Jokiniemi, Achim Schwenk
Categories: nucl-th, hep-ph, nucl-ex
Abstract:
We present ab initio calculations of two-neutrino double-beta ($2νββ$) decay of $^{48}$Ca and the related Gamow-Teller (GT) strength functions in $^{48}$Sc using the valence-space in-medium similarity renormalization group (VS-IMSRG) with nuclear interactions and electroweak currents based on chiral effective field theory. We find that the usual $pf$-shell valence space significantly underestimates the nuclear matrix element (NME) of $2νββ$ decay compared to experiment, while an enlarged $d_{3/2}pf$ valence space yields very good agreement with the experimental value without any adjustments. We trace this to an improved description of the involved GT strength distributions, so that the enlarged valence space captures important correlations. The enlarged $d_{3/2}pf$ valence space leads to neutrinoless $ββ$ NMEs of $^{48}$Ca that are twice as large compared to the $pf$-shell calculation. Our findings suggest that studies with different valence spaces and related GT strengths are important for assessing ab initio NME calculations of heavier $ββ$ decays.
Published: 2026-07-13 15:44:08
Authors: Ziren Chen, Hailiang Liu
Categories: math.NA, math.AP
Abstract:
Solving partial differential equations (PDEs) in high dimensions remains challenging due to the curse of dimensionality. We propose a neural-network-based framework that reformulates PDEs as inf--sup optimization problems through the introduction of a Lagrange multiplier. The primal solution and the associated Lagrange multiplier are parameterized by two networks and are computed via an iterative saddle-point optimization procedure. We prove the theoretical equivalence between the proposed optimization formulation and the original PDE problem, and we derive rigorous error estimates that quantify the total approximation error in terms of the network approximation error, statistical (sampling) error, and optimization error. Numerical experiments demonstrate the accuracy, stability, and efficiency of the proposed method for solving high-dimensional PDEs.
Published: 2026-07-13 15:41:04
Authors: Huangrong Sun, Xian Yu
Categories: math.OC
Abstract:
We study a general decision-dependent contextual stochastic program (DD-CSP) in which uncertainty depends on both exogenous contextual information and endogenous decisions. To learn the potentially complex dependence of uncertainty on decisions and contextual information, we employ several nonparametric regression models, including k nearest neighbors (kNN), classification and regression trees (CART), and ReLU neural networks. To account for estimation errors in predicting the uncertainty, we adopt an empirical residuals-based decision-dependent sample average approximation (ER-DD-SAA) framework, which adds empirical residuals to the point predictions from the learned regression models. For each nonparametric regression model, we develop exact mixed-integer programming (MIP) representations that can be seamlessly embedded within the ER-DD-SAA framework. For two-stage ER-DD-SAA problems with kNN, we further propose a tailored decomposition algorithm, named BD-CG, that combines Bender's decomposition with constraint generation. Under suitable assumptions, we prove that the proposed BD-CG converges to a global optimum within a finite number of iterations. From a statistical perspective, we establish the consistency and asymptotic optimality of ER-DD-SAA with all three nonparametric regression models under mild regularity conditions. Numerical experiments on a newsvendor problem with pricing and a two-stage facility location problem demonstrate that the ER-DD-SAA model with nonparametric learning consistently outperforms a parametric benchmark in out-of-sample performance and the proposed reformulations and algorithm substantially improve computational tractability.
Published: 2026-07-13 15:35:29
Authors: Aastha Sharma, Guangjing Wang
Categories: cs.SD, cs.AI
Abstract:
Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) benchmark of 53,628 audio samples generated using 10 contemporary speech synthesis methods and evaluated under 10 standardized post-processing conditions. Using VoxENES 2026, we benchmark eight pretrained detectors without fine-tuning and observe substantial performance degradation: the best model achieves 28.98\% EER overall, while most perform near or below random chance across modern generators and perturbations. Our results highlight the reliance on brittle artifacts in current detectors and establish VoxENES 2026 as a practical testbed for developing robust audio spoofing countermeasures.
Published: 2026-07-13 15:33:45
Authors: Anna Marklová, Jiří Milička, Martina Vokáčová, Rudolf Rosa
Categories: cs.CL
Abstract:
The asymmetry between language production and perception has been well-documented in psycholinguistics. Whether large language models (LLMs) exhibit a functionally analogous distinction remains an open question, particularly given that LLMs rely on the same underlying mechanism (next-token prediction) for both input and output processing. In this exploratory study, we operationalize the production-perception distinction through direct token probability measurements rather than metalinguistic prompting. Using the base Llama-3.1-8B model, we generated poems under a production prompt and re-scored the same tokens under both rephrased production prompts and perception-oriented prompts. Across an extended experiment with four production and three perception prompts, production-perception distances consistently and substantially exceeded production-production distances, with non-overlapping ranges across conditions and an overall average ratio of approximately 1.8. Near-ceiling correlations in the production-production control confirm that the effect is specific to communicative framing rather than prompt surface variation, and we show the effect replicates across five open-weight models (Llama-3.1-8B, EuroLLM-9B, gemma-2-9b-it, Mistral-7B-Instruct-v0.3, and Qwen2.5-7B-Instruct), spanning both base and instruction-tuned variants. Temporal analysis revealed that the perception prompt exerts its strongest influence at the beginning of the sequence, with divergence decaying as generated context accumulates, though the specific shape of this decay varies across prompt pairs. These findings suggest that prompt framing alone induces a production-perception distinction in LLM probability distributions, even within a decoder-only architecture.
Published: 2026-07-13 15:33:05
Authors: Mariano Caruso, Daniel Ruiz, Alejandro Giraldo, Guido Bellomo
Categories: quant-ph, cs.LG
Abstract:
Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning ($\mathtt{QMKL}$) framework, dubbed Next-Gen $\mathtt{Q^2SAR}$, that leverages Quantum Support Vector Machines ($\mathtt{QSVMs}$) to overcome these classical limitations. By encoding molecular descriptors into exponentially large quantum Hilbert spaces, our approach substantially enhances the expressiveness of non-linear modeling. Benchmarking our quantum-enhanced framework on a dataset targeting the $\mathtt{DYRK1A}$ kinase (a critical target for Alzheimer's disease), the $\mathtt{QMKL}$-$\mathtt{SVM}$ achieves an impressive Area Under the Curve ($\mathtt{AUC}$) score of $0.8750$, significantly outperforming classical state-of-the-art Gradient Boosting models ($\mathtt{AUC} = 0.8037$). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels ($\mathtt{PQK}$) and measurement accelerators. As quantum computing architecture matures, this framework paves the way for autonomous cognitive architectures and self-improving drug discovery pipelines, promising to unlock deeper insights across vast chemical spaces and to accelerate the development of life-saving therapeutics.
Published: 2026-07-13 15:31:02
Authors: Julie Gilbert, Francesco Calzaferri
Categories: q-bio.SC
Abstract:
DNA methylation can function as a toxic alkylation reaction exploited by chemotherapeutic agents to induce cancer cell death. However, finely tuned DNA methylation plays a fundamental role in cellular physiology, particularly in the epigenetic regulation of gene expression. Once thought to act solely as a repressor of gene transcription, its functional role has since been elucidated as genomic locus-specific and deeply connected with other epigenetic factors. Following the clinical approval of DNA methyltransferase inhibitors, such as Azacitidine and Decitabine, for the treatment of haematological malignancies, considerable efforts have been devoted to developing pharmacological tools that modulate epigenetic DNA methylation. However, the lack of gene selectivity in these agents limits their therapeutic efficacy and increases off-target toxicity. Moreover, the non-gene-selective nature of current DNA methylation-targeting molecules fails to meet the standards required to discern the nuanced roles of DNA methylation across diverse pathophysiological contexts and genomic loci, particularly in an era where next-generation sequencing and omics technologies enable hi ghresolution epigenetic analyses. In this review, we examine the mechanisms and roles of DNA methylation in epigenetic regulation, evaluate the current landscape of DNA methylation modulators, from traditional DNMT inhibitors to cutting-edge CRISPR-dCas9 fusion systems and protein-protein interaction disruptors, and discuss their clinical relevance. Finally, we emphasise the need for precise, locus-specific tools to advance both cancer research and therapeutic strategies.
Published: 2026-07-13 15:29:24
Authors: Huan Zhu
Categories: cs.AI
Abstract:
Self-refinement often fails to strengthen few-shot inductive reasoning in large language models. Prompting a model to explicitly state its inferred rule does little on its own. What actually matters is a structurally enforced isolation between reasoning stages, so that information can only pass between them as a compressed symbolic state.
We introduce \textbf{Hourglass reasoning}, which enforces strict context isolation between reasoning stages. The frozen LLM acts as a meta-constructor, building for each task a symbolic encoder--decoder: an Induction module compresses the support examples into a schema $φ$ (encoder) and a transient scaffold $z$; a Deduction module derives rule $T$ (decoder) from these and discards $z$; an Implementer compiles $(φ, T)$ into artifacts; an error-driven Refiner revises $(φ, T)$ and regenerates artifacts from scratch. Only $(φ, T)$ crosses stage boundaries, so all refinement stays anchored to the rule.
We evaluate Hourglass across three benchmarks spanning visual abstraction, hardware synthesis, and textual rule induction, using GPT-5.5 and Gemini 3.1 Pro. On ARC-AGI-2, it raises best-of-5 accuracy by up to 14 points over an iterative-refinement baseline. On ChipBench, it nearly doubles Verilog synthesis accuracy with GPT-5.5, from 31\% to 58\%. BBEH-Linguini draws on puzzles from the International Linguistics Olympiad, a setting where prior work has shown that explicit verbalization can hurt performance. Hourglass mitigates this tendency, and on Gemini 3.1 Pro, it reverses the effect entirely.
Ablations confirm that these gains come from the isolation between stages and the quality of the initial induction, not from prompt wording or the particular symbolic form used. It is how information flows through the reasoning process, rather than the language used to express it, that drives inductive reasoning in frozen LLMs.
Published: 2026-07-13 15:22:32
Authors: Jakob Solberg Berntzen, Safia Fatima, Leon Moonen
Categories: cs.RO, cs.LG, cs.SE
Abstract:
Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot. Line following is useful because it only needs one camera and little computing power, but it can fail when the line is blocked or turns sharply and goes out of view. Sensor-rich platforms tolerate this through hardware redundancy (LiDAR, GPS, multiple cameras), but camera-only systems must recover at runtime with no additional infrastructure. This paper presents a lightweight, two-stage recovery approach that restores guideline tracking without LiDAR, GPS, or a GPU. When the line is lost, the robot first turns in place while slowly relaxing its color checks and waiting for confirmation across multiple frames (Stage 1). If the line is still not found, monocular visual odometry moves the robot back to saved breadcrumb positions before it tries again (Stage 2). The system uses a depth-gated HSV line tracker, a YOLOv8n obstacle detector, and a visual odometry breadcrumb mapper, and it runs at 20 Hz on CPU-only hardware. The controller embeds a complete MAPE-K loop within a single 50 ms control tick, with no external adaptation manager required. The approach is evaluated across 119 fault-injected episodes on three Webots simulation courses. The method was successful in 86.6% of cases, with a median recovery time of 3.26 seconds. These results demonstrate that reliable visual recovery is feasible on camera-only UGVs within practical cost and computational limits.
Published: 2026-07-13 15:18:26
Authors: Yuan-Shan Zhang, Masahiko Isobe, Hidenori Takagi, Dennis Huang
Categories: cond-mat.str-el
Abstract:
Ta$_2$NiSe$_5$ continues to be investigated for its phase transition at $T_\textrm{c}$ = 326 K, where it develops both an electronic gap and a distortion of its Ta/Ni chains. One intriguing feature at $T_\textrm{c}$ seen in thermal transport is the giant anisotropic scattering of phonons moving perpendicular to the chains, which is apparently associated with the softening of a transverse acoustic phonon, but whose microscopic origin and significance demand clarification. By tuning the normal-state band overlap/gap with S substitution, we uncover a close connection between this soft-phonon transport anomaly and underlying electronic instabilities: When Ta$_2$Ni(S$_x$Se$_{1-x}$)$_5$ approaches a band insulator at high $x$, and signatures of the electronic transition are suppressed, the soft-phonon transport anomaly concomitantly vanishes. Our results establish the following picture for the Ta$_2$Ni(S$_x$Se$_{1-x}$)$_5$ family: Near the S end, a sole lattice instability gives rise to a weak structural transition with $T_\textrm{c}$ approaching 130 K. Near the Se end, additional electronic instabilities boost $T_\textrm{c}$ up to 326 K and amplify experimental signatures of the transition. The strong interaction between electrons, holes, and the lattice is manifested as a soft-phonon transport anomaly accompanied by electronic fluctuations, which include excitonic and hybridization-gap fluctuations.
Published: 2026-07-13 15:17:41
Authors: Isaev Roman
Categories: math.AG
Abstract:
We refer to the variety $\operatorname{Susp}(X, f, k_1, \dots, k_m) = \mathbb{V}(y_1^{k_1} \dots y_m^{k_m} - f(x)) \subset X \times \mathbb{A}^m$ as an $m$-suspension over affine variety $X$, constructed via a local slice $f(x) \in \mathbb{K}[X]$, if there exists a locally nilpotent derivation $δ$ on $X$ such that $δ(f) \neq 0, δ^2 (f) = 0$. In this paper, we determine the sufficient conditions under which such a variety is generically flexible and those under which it is flexible. Furthermore, for a flexible $X$ we propose a construction of a local slice $f$ that guarantees the flexibility of the suspension $\operatorname{Susp}(X, f, 1, k_2, \dots, k_m)$.
Published: 2026-07-13 15:09:42
Authors: Maksim A Kazanskii
Categories: cs.LG
Abstract:
Grokking is a phenomenon in which neural networks initially memorize training data and only later exhibit strong generalization after prolonged optimization. Despite extensive recent study, the factors influencing the emergence and timing of grokking remain incompletely understood. We investigate the relationship between representation geometry and delayed generalization. We find that dimensionality collapse consistently precedes the onset of grokking in all evaluated settings. Motivated by these observations, we introduce Geometric Dimensionality Regularization (GeomDR), a simple spectral regularizer that modifies the effective dimensionality of hidden representations during training. Across modular addition, modular division, and permutation composition tasks, GeomDR consistently alters grokking dynamics and can substantially accelerate the onset of generalization depending on the intervention schedule and target dimensionality. In several settings, grokking is accelerated by up to 52 times relative to standard AdamW training. Similar qualitative effects are observed in both multilayer perceptrons and transformers. Together, these results suggest that representation geometry can serve as an effective control signal for grokking and provide evidence that geometric interventions offer a practical approach for studying and influencing delayed generalization in neural networks.
Published: 2026-07-13 14:50:32
Authors: Motti Goldberger
Categories: cs.LG, stat.ML
Abstract:
In fixed-budget best-arm identification, also known as ranking and selection, an algorithm has a sampling budget to distribute across $K$ arms. Each sample provides noisy feedback about that arm's mean, and the goal is to identify the arm with the largest mean. A common performance benchmark is the static oracle: a non-adaptive strategy that knows the means in advance and chooses fixed sampling proportions to maximize the exponential decay rate of the probability of incorrect identification. Several adaptive algorithms have been constructed such that their sampling proportions converge to the static oracle proportions. However, it has remained open whether any algorithm could match the static oracle's error decay rate uniformly across all problem instances. We answer this in the negative. For any $K\ge 3$ and for rewards drawn from any one-parameter natural exponential family, we show that for any algorithm, there is at least one instance where the error decay rate is at most $\left(1 + \frac{\log(K)}{8}\right)^{-1}$ times that of the static oracle. This also answers the open question posed by Qin (2022), showing that fixed-budget best-arm identification does not admit a complexity.
Published: 2026-07-13 14:46:43
Authors: Sujoy Majumder, Goutam Haldar, Abhijit Banerjee, Shantanu Panja
Categories: math.CV
Abstract:
In this paper, we introduce and study the class $\mathcal{W}_{\mathcal{H}_n^0}(α)$ of normalized pluriharmonic mappings, characterized by a suitable bound on their second-order partial derivatives. We establish a one-to-one correspondence between this pluriharmonic class and an associated class of holomorphic functions, thereby extending a result of Ghosh and Vasudevarao \cite{Ghosh-Allu-2019} to the setting of several complex variables. Furthermore, we obtain sharp coefficient bounds, growth estimates and a convex combination theorem for functions in $\mathcal{W}_{\mathcal{H}_n^0}(α)$. Finally, we introduce sections (partial sums) of pluriharmonic mappings and investigate their properties for functions belonging to $\mathcal{W}_{\mathcal{H}_n^0}(α)$.
Published: 2026-07-13 14:40:28
Authors: Xavier Fernández-Real
Categories: math.AP
Abstract:
We construct homogeneous solutions with non-Lipschitz Hessian for finite, constant-coefficient Bellman equations. First, for every $σ\in(0,1)$, we find two uniformly elliptic matrices $A_1,A_2\in\mathcal{S}^4$ and a nonzero $(2+σ)$-homogeneous solution $u$ of \[\max\bigl\{{\rm tr}\,(A_1D^2u),{\rm tr}\,(A_2D^2u)\bigr\}=0 \qquad\text{in }\mathbb{R}^4.\] Second, in $\mathbb{R}^2$ we construct three matrices satisfying ${\rm Id}_2\leq A_j\leq3{\rm Id}_2$ for which the corresponding Bellman equation admits a homogeneous solution with a non-Lipschitz Hessian. In particular, solutions to convex fully nonlinear uniformly elliptic equations are not in $C^{2,1}$, and not even in $C^{2, 1-\varepsilon}$ for $\varepsilon > 0$ small.
Published: 2026-07-13 14:40:17
Authors: Gleb Arutyunov, Hrachya Babujian, Minghao Gao
Categories: hep-th, math-ph
Abstract:
We develop a non-nested Bethe ansatz description of rational $\mathfrak{gl}_\ell$ spin chains in the vector representation. Starting from the quantum spectral curve and the separation-of-variables framework, we derive closed systems of Bethe equations involving only the momentum-carrying Bethe roots. The construction is worked out explicitly for the $\mathfrak{gl}_3$ and $\mathfrak{gl}_4$ spin chains and then generalized to arbitrary rank. A central result of this work is the identification of a recursive hierarchy associated with the fundamental transfer matrices. The hierarchy is generated by regularity conditions of the lower transfer matrices and closes through a universal rank-$\ell$ equation $\mathcal{R}_{\ell}=0$. This equation replaces the final level of the conventional nested Bethe ansatz and eliminates all auxiliary Bethe roots. Consequently, the complete spectral data of an eigenstate are encoded solely in the first Baxter polynomial $Q_{1}(u)$. We further obtain explicit expressions for the eigenvalues of all fundamental transfer matrices in terms of the momentum-carrying roots alone. The resulting formulation provides a compact characterization of the spectrum of rational $\mathfrak{gl}_\ell$ spin chains and reveals a direct connection between the quantum spectral curve, transfer-matrix fusion relations, and a truncated $Q$-system underlying the non-nested description. Finally, we investigate the quasi-classical (Gaudin) limit of the non-nested Bethe equations. For the $\mathfrak{gl}_3$ spin chain, we show that the leading non-trivial contribution gives rise to Gaudin equations whose pole-free form naturally defines a scalar third-order $\mathfrak{gl}_3$ oper.
Published: 2026-07-13 14:39:12
Authors: Maximiliano Escayola
Categories: math.DS
Abstract:
We construct a $C^1$-diffeomorphism of the circle having an invariant Cantor minimal set such that, when the lengths of the connected components of its complement are arranged in decreasing order, the ratios of consecutive lengths do not converge to 1. This gives a negative answer to a longstanding question posed by Dusa McDuff.
Published: 2026-07-13 14:37:35
Authors: Kazuki Kusama, Honglin Shu, Masanari Kondo, Tao Xiao, Yasutaka Kamei
Categories: cs.SE
Abstract:
Runtime logs are an important source of information that supports software maintenance. To obtain useful logs, developers spend significant effort identifying appropriate log locations, assigning correct severity levels, and writing concise yet informative messages. Therefore, end-to-end automated log statement generation can help reduce this burden, and prior work has proposed many methods for this task. However, existing methods still exhibit limited accuracy. To address this problem, we propose ThinkLog, an LLM-based end-to-end log statement generation method. The core idea of ThinkLog is to incorporate reasoning that helps LLMs make decisions about log insertion, severity level assignment, and message generation, thereby improving log statement generation accuracy. ThinkLog injects reasoning into prompts as few-shot examples and guides LLMs to generate appropriate log statements. Evaluated on 9,619 Java methods extracted from public GitHub repositories, ThinkLog achieves 20.55% log statement generation accuracy, representing a 15.4% improvement over the best existing method. Moreover, these improvements were achieved at approximately 50% of the inference cost (USD) compared to the best existing method. These results show that leveraging reasoning is an effective and cost-efficient way to improve the accuracy of end-to-end log statement generation.
Published: 2026-07-13 14:37:24
Authors: Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov, Yuri Kuratov, Lyudmila Rvanova, Mikhail Katkov, Ilia Sochenkov, Misha Tsodyks, Timothy Baldwin, Mikhail Burtsev, Artem Shelmanov
Categories: cs.CL, cs.AI
Abstract:
Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling. In this work, we investigate the Associative Recurrent Memory Transformer (ARMT) as a practical approach for enabling long-context processing in LLMs, constant memory scaling, and better efficiency. We make three main contributions. First, we construct two domain-specific long-context datasets designed to evaluate realistic workloads, focusing on narrow-domain fine-tuning scenarios. Second, we propose a comprehensive training recipe for ARMT-based context extension, combining continued pre-training, synthetic long-context data generation, curriculum learning, and selective integration of associative memory into chosen model layers. Third, we present an extensive experimental study demonstrating that ARMT-augmented models: (i) process inputs well beyond their original context limits without degrading performance relative to in-limit baselines; (ii) generalize more effectively to out-of-distribution context lengths; and (iii) need 30% less FLOPs while preserving baseline performance within the original context window.
Published: 2026-07-13 14:36:25
Authors: Valdemar Bille-Lauridsen, Jesper Mørk
Categories: physics.optics
Abstract:
Dielectric bowtie nanocavities can concentrate light into subwavelength regions without the ohmic losses of plasmonic metals. We show that this enhancement is the finite-geometry realization of a classical electromagnetic edge singularity. Unlike an isolated dielectric wedge, the scaling in a bowtie is governed by an exponent determined by a collective four-sector singularity. In a finite structure, this scale-free singular field is regularized by the gap size, while the bowtie length sets the outer scale. The tip radius, gap, and bowtie length therefore play distinct physical roles: curvature cuts off the local wedge singularity, the gap cuts off the collective bowtie singularity, and the outer length sets the range over which the field can build up. Electrostatic simulations confirm the predicted scaling laws, while three-dimensional quasinormal-mode simulations show how the same near-field mechanism is accessed and limited by realistic dielectric nanocavities.
Published: 2026-07-13 14:33:47
Authors: Marvin Jaeger, Philipp Luetke, David Kopyto, Georg Frederik Riemschneider, Omar Jabi, Alexander Koelpin
Categories: eess.SP
Abstract:
5G and Beyond-5G standards require digital predistortion (DPD) algorithms to operate on increased signal bandwidths. Wideband laboratory test hardware is cost-intensive, and openly available solutions lack flexibility. The OwnDPDLab provides a highly flexible, affordable, open-source, and openly accessible system. It is based on the RFSoC 4x2 and supports full control of center frequency, sampling mode, output power, and input attenuation at a signal bandwidth of up to 1 GHz. The system's capability is demonstrated by linearizing a laboratory power amplifier using a 196.608 MHz orthogonal frequency division multiplexing (OFDM) signal with 256-QAM modulation using both a memory polynomial and an augmented real-valued time-delay neural network in the first and second Nyquist zone. The system achieves a normalized mean squared error improvement of up to 23 dB and an adjacent channel leakage ratio improvement of up to 11 dB, using DPD.