September 2026 arXiv papers

Yongzhi Huang, Jiayi Zhao, Kaishun Wu

Many headphone users risk irreversible hearing damage during high-load listening, yet current protection still relies on sound-pressure limits. Clinical tests detect damage after it becomes measurable, leaving the risk-accumulating process unobserved. The acoustic reflex (AR), an involuntary response to high acoustic load, marks when the ear enters a protect

Anh L Tran, Arjee Javellana Restar, Bereket Kindo

Alzheimer's disease (AD) remains a major public health challenge, and early identification is crucial for timely intervention and care planning. Recent work has shown that speech-based digital biomarkers can capture subtle changes in vocal production, prosody, and timing that often precede or accompany cognitive decline. In this study, we investigate whe

Ziming Zhou, Shenggui Zhang

We systematically explore the relationship between edge-connectivity and Lin--Lu--Yau curvature for graphs. The intuition is that a connected graph with large Lin--Lu--Yau curvature should also have large edge-connectivity, and vice versa under suitable conditions. We prove that the edge-connectivity of a connected graph is bounded lower by the product of it

Junaid Jawaid, Matteo Spezialetti, Gabriele Libardi, Giovanni Stilo

Proton therapy offers a dosimetric advantage over conventional radiotherapy through the Bragg peak, enabling precise dose delivery to the tumour while sparing surrounding tissue. Monte Carlo (MC) simulation provides the most accurate dose calculation but is computationally expensive for clinical workflows. A promising compromise is to simulate with a reduced

Tianle Hu, Chen Peng, Yi-Hsin Tsai, Takshing Andy Tung

Identifying suicide risk from social networking services (SNS) posts is important for detecting suicide-related signals in online environments. However, risk classification alone provides limited insight into the textual evidence and psychosocial factors behind a prediction. Based on the IEEE BigData 2026 Explainable Suicide Risk Detection Challenge, this st

A. Errehymy, Y. Khedif, M. Daoud, B. Turimov

Quantum modifications of black hole spacetimes can lead to observable departures from the predictions of classical general relativity, particularly in the strong-gravity regime near the event horizon. In this context, the coherent quantum black hole model introduces a sine-integral correction to the gravitational potential, characterized by a coherence scale

Guang Yang, Homa Hosseinmardi, Fengchen Liu, Amir Ghasemian

Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back. This behavior, called sycophancy, is usually reported as a single rate per model, which says little about when it happens or how a user can avoid it. We study the conditions that produce it with 103,939 graded replies from ten configurati

Hanrui Zhou, Gaoyuan Zhang, Yixiang Chen, Yujie Xing

Language training relies on a corpus constructed by a large number linguistic materials. AI-powered voice clones provide a way to construct the corpus with relatively low cost. Singing voice conversion (SVC) model is used to generate synthetic voices. This study compares participants' performances on natural and synthetic speech in two experiments, simil

Vincent Siu, Glenn Grant-Richards, Vlad Pavlovich, Yizhou Sun

Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions remains poorly understood. We decompose refusal steering into component-level interventions across four open-weight models, identifying the sparse subsets of attention and MLP components whose steering suffice

Priyank Jayraj, Poonam Goyal, Navneet Goyal

Retrieval-augmented generation grounds language models in external context, but for long documents flat top-$k$ retrieval can cluster on a single region and miss complementary evidence. RAPTOR-style summary trees address this by recursively clustering chunks and using a language model to summarize each cluster at indexing time, then ranking summary nodes alo

Vadim Nikolayev, Iaroslav Nekrashevych

In this communication, we discuss results obtained with the numerical in-house PHP simulation code CASCO (French abbreviation for Advanced PHP simulation code). The aim is to study the impact of bubble generation (boiling) on the established oscillation of a PHP with a high turn number (10-turn water-copper tubular PHP with independently heated turns in the

Yujian Zhuang, Dehong Gao, Qichao Zhang, Qijing Lai

Large language models (LLMs) are increasingly used in binary decompilation to refine the C-like pseudocode produced by traditional rule-based decompilers. While this pseudocode is useful, it is a heuristic and lossy abstraction rather than a faithful copy of the source code. It often contains decompiler errors, especially for aggressively optimized binaries

Andres Medina, Wei Wei

Catastrophe bonds have become a central instrument for transferring catastrophic risk to capital markets, yet their payout structures are typically specified exogenously rather than optimized. In this paper, we treat the indemnity function as the decision variable and study the optimal design of CAT bonds from the sponsor's perspective. We establish the

Avyay Sadhu, Patrick Cooper

Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it applicable to causal-temporal video question answering. We study what RLVR teaches video-language models about time. We fine-tune four open models (Qwen3-VL-8B/4B, Qwen2.5-VL-7B, Gemma-3-12B) with group relative

Zoltan Bartha

This paper examines the post-2010 expansion of the Hungarian construction sector as a case study of large-scale government intervention in a single market. A political economy framework conceptualises the intervention as two mutually reinforcing policies - expansionary demand (public procurement and housing subsidies) and preferential allocation towards poli

So Katagiri, Tatsuaki Wada

We investigate the relationship between the dissipation potential $S_{\mathrm{NB}}$ and information entropy in stochastic Nambu nonequilibrium thermodynamics (NNET). Starting from pure diffusion, gradient-flow systems, and systems with Hamiltonian rotational currents, we derive the stationary distribution of the Fokker--Planck equation including a Nambu flow

Michael A. Idowu

Paul Erdős conjectured that the only powers of two whose ternary expansions contain no digit 2 are 1, 4, and 256. Despite extensive computational verification, the conjecture remains open. We reformulate the problem using generalised base-4 repunits. Any admissible exponent is even, say \(n=2s\), and \[ 2^{2s}=4^s=3R_s(4)+1,\qquad R_s(4)=\frac{4^s-1}{3}. \]

Ofer M. Shir

We revisit an established Quantum Control objective, characterize its exact local metric geometry, and reposition it as a rigorous benchmark for Search and Optimization. The trap-free topology of the landscape, established two decades ago, guarantees unhindered optimal pathways, but global topology alone does not govern convergence speed. Although the so-cal

Maksim Lavrov

We study the one-dimensional Dirichlet spectrum and obtain new results concerning the origin $μ$ of the ray contained in the spectrum and its isolated points. First, we prove the improved upper bound $μ\leq (448875+1611\sqrt{67773})/46982 = 18.4809\ldots$. We also exhibit an explicit gap $(L,R)$ in the Dirichlet spectrum, which yields the improved lower boun

Haofang Sun, Min Yan

We study tilings of closed surfaces by congruent most curvilinear polygons. By relating such tilings to branched coverings determined by planar diagrams and vertex types, we establish necessary and sufficient conditions for a given closed topological surface to admit a metric of constant curvature supporting a tiling with prescribed combinatorial data and nu

Rafik Sahbi

For a positive integer $k$, let $β_k(G)$ be the maximum cardinality of a vertex set inducing maximum degree less than $k$, and let $SF_k(G)$ be the maximum cardinality of a minimal $k$-star-forming set. The known inequality $β_k(G)\le SF_k(G)$ suggests asking when equality holds. We place this question in the framework of upper domination: at $k=1$, $β_1(G)=

Dishant Sisodia, Sarika Jalan

There have been persistent efforts to uncover low-dimensional macroscopic dynamics in large systems of interacting microscopic units, in which underlying physical principles govern their collapse onto low-dimensional manifolds. Here, focusing on interacting phase oscillator systems, we show that an unsupervised autoencoder framework can determine their intri

Mahidhar Reddy Patukuri, B. Veena S. N. Rao, S. M. Mallikarjunaiah

Three neural networks are compared as solvers of nonlinear ordinary differential equations: a multilayer perceptron (MLP), a Kolmogorov-Arnold network (KAN) whose edges are B-splines, and a KAN whose edges are Chebyshev series. Each network is trained only on the residual of the equation and on its initial or boundary conditions. The exact solution, or a num

Farkhondeh Khodabandeh, Zhengmao Lu

Gas bubbles impose large penalties in water electrolysis at industrially relevant rates. Capillary-fed electrolyzers greatly mitigate them by wicking electrolyte to electrodes and venting gas from the backside, but only below a threshold current density. We show that the separator pore size sets this threshold through two opposing effects: smaller pores rais

Philippe Gille

This paper studies flasque resolutions for linear reductive group schemes over arbitrary bases, extending Colliot-Thelene's theory. A flasque resolution exists for any linear reductive group scheme over a connected base scheme (resp. a quasi-compact, quasi-separated base scheme). The construction relies on Borovoi's fundamental group and descent theo

Fan Zhang, Weicong Cheng, Yuheng Chen, Hiuseut Kung

Time-dependent, three-dimensional (3D) oceanic multi-variables define coherent states of the evolving ocean to facilitate ocean diagnosis and advance ocean science to better inform environmental and hazard management. However, extracting quantitative evidence from these variables requires substantial and complex analytical effort. We introduce OceanMind, a m

Subrat Parida

We study spontaneous scalarization of static, spherically symmetric black holes in scalar--Gauss--Bonnet gravity with a Ricci coupling $F=1 βϕ^{2}/4+γβ^{2}ϕ^{4}+O(ϕ^{6})$ that nonlinearly completes the quadratic one. For arbitrary couplings and potential, we derive the near-horizon regularity condition and horizon curvature invariants in closed form; scalari

Yang Xiao, Qian Zeng, Jin Wang

In general, measurement error constrains the performance of information engines. However, this may not hold universally. In this work, we propose a class of two-reservoirs information engines (TRIEs) with a two-level system and demonstrate that the monotonic relationships between the work output, the efficiency, as well as the work fluctuations of the TRIEs

Yesom Park, Kelvin Kan, Qifan Chen, Thomas Flynn

Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as observed measurements and physical laws. However, existing constrained samplers often face a trade-off: \textit{enforcing constraints can substantially displace samples from the pretrained data distribution}. To

Amit Rajula

Online feature stores decouple feature computation from model serving, materializing features from upstream event streams into a low-latency store that inference reads at request time. This decoupling introduces a freshness gap - the interval between an event occurring at the source and its effect becoming visible in the served feature vector - that is widel

Vaneet Aggarwal

We study online convex optimization with one unbiased stochastic subgradient per round and an unknown finite conditional $p$th noise moment, $1<p\le2$. For every fixed interval $I$ of length $n$ and comparator path with $Λ_I=1+P_I/D$, one learner achieves \[ E[Regret_I(u)]\le\min(GDn, C[GD\sqrt{n(Λ_I+\log^2(2T))} +σDn^{1/p}(Λ_I+\log^2(2T))^{(p-1)/p}]). \] Th

Jón Hákon Garðarsson, Paolo Perrone

A dagger on a category assigns to every morphism $f \colon X \to Y$ a morphism $f^\dagger \colon Y \to X$, contravariantly and involutively, in the way the transpose reverses a real matrix. We show that on the categories of linear algebra and differential geometry, daggers encode metric and Riemannian structures. As is known, on a single vector space, an ant

Xinyi Yi, Moy Yuan, Ioannis Lestas

Electricity price forecasting (EPF) supports scheduling, bidding, and risk management in electricity markets, yet existing benchmarks focus mainly on numerical inputs, leaving the forecasting value of forecast-time textual context insufficiently evaluated. We introduce TRACE, a reproducible benchmark of 7,300 zone--day instances pairing prices from five zone

Ahmad Shahi, Mamehgol Yousefi

Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with complex phenomena such as regime shifts, cross-domain knowledge transfer, and multimodal data integration. This paper introduces Multi-Agent Collaborative Time Series Forecasting with Emergent Memory (MACTS-EM), a

Everett Rush, David J. Icove, Ari Kim, Byung H. Park

Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its ability to model causal relationships, the binary scale of factors, and resulting hierarchical representation. Traditionally, the modeling process is performed by repeated interactions with subject matter exper

Jia-He Yao

The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization. In this work, we study approximation of the unit ball of $W^{n,\infty}([0,1]^d)$ by ReLU networks whose edges are retained independently with probability $p$. The approximation error is measured uniformly o

Dingling Yao, Kahaan Gandhi, Valentin Duruisseaux, Boris Bonev

Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on observed data yet fail on such what-if queries when correlated inputs are varied independently. A common remedy is to add controlled simulation data in which these factors are explic

Marcello Bullo, Yanxiao Liu, Öykü Sıla Güner, Arpan Mukherjee

Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law. Recent tree-based methods allocate the parallel compute budget more effectively than single-chain drafts, as demonstrated by Diffusion Greedy Rejection Sampling (D-GRS). D-GRS generates $K$ conditionally independent candida

Guy Pelletier

In two previous articles, we have explained how super-massive Kerr Black Holes in the nucleus of an active galaxy or quasar can generate a large amount of axion particles in their magnetosphere. In that third part, we explore the possibility of observable dynamics resulting from some interaction of an axion wind with the baryonic matter and the axionic Dark

Ling Min Serena Khoo

It has recently been shown that nearest-neighbour retrieval provides a strong baseline for molecular fingerprint prediction from MS/MS spectra, with several variants matching or outperforming current deep learning models (Khoo and Barzilay, 2026; Liu et al., 2026; Gupta et al., 2026). Importantly, &#34;nearest neighbour&#34; encompasses a family of retrieval

Anidipta Pal

Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded irrigation booms, dam-operation shifts, sensor recalibrations) into their state-transition matrices, silently biasing NDVI, LST, and crop phenology predictions long after the physical cause ends. This paper int

Nam H. Le, Douglas Blackiston, Michael Levin, Josh Bongard

Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated tasks by describing what they want rather than specifying how to do it. Extending this interface to living systems is harder: unlike code or images, a biological intervention has no closed-form linguistic meaning,

A. Acar, M. Bashkanov, D. P. Watts, M. Nicol

In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique provides a statistically rigorous means of separating signal and background using discriminating variables, but its conventional formulation is generally applied to a

Angshuman Chakravertty, P V V Raj

Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic James Webb Space Telescope (JWST) spectra but collapses when it comes to real reduced spectra. The flow posterior collapsed on real WASP-39b (importance-sampling effective sample size (ESS) = 1, best-fit \c{hi}

Sun-Kai Leung

Adapting the circle method developed by Frantzikinakis--Klurman--Moreira, we establish a sufficient criterion for a set of natural numbers to be multiplicatively recurrent. We apply this criterion to Piatetski--Shapiro sets with small exponents and to regular irrational nil-Bohr sets. We also study the value distribution of unimodular completely multiplicati

Brendan Mackey, Levi Smith, Bosco Yu

Volumetric additive manufacturing (VAM) is a promising field of advanced manufacturing which reduces production times of conventional 3D printing while retaining application to complex geometries. Current VAM systems are limited to photo-polymerizing resins, which reduces the general applicability across industry. This work presents exploratory finite elemen

Oliver Knitter, Jonathan Mei, Sang Hyub Kim, Chi Chen

For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum neural networks (QNNs) and existing classical models, which encompass fundamentally different function classes, often fails to provide broader insight into the difference between the two. Inspired by the techn

Grayson Wycliffe Storer, Julia Witte Zimmerman

Transformer-based large language models (LLMs) such as RoBERTa represent text using contextual word embeddings (CWEs), which alter the embeddings associated with each token based on surrounding context. We construct token-wise incremental trajectories by repeatedly recomputing a token&#39;s CWE as successive words are added to a sentence, yielding a represen

Shuze Liu, Kaixiang Zhao, Runyang Xu, Jingzhi Chen

Large language models (LLMs) deployed through text-only APIs face model extraction risks, as adversaries can collect their responses to train surrogates that reproduce their capabilities. While prior work has developed diverse attacks and defenses, evaluations remain fragmented across access assumptions, model configurations, query budgets, and security obje

Xinchen Du, Zhengze Zhou, Wenhui Zhu, Han Yu

Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error can cause task failure, while trajectory-level rewards provide limited guidance for assigning credit to individual decisions. To address this limitation, we introduce Segment-level Hindsight Advantage Reweightin

Shuo Pang

For every constant-width CNF, we show that linear degree in polynomial calculus (PC) implies exponential size in resolution over constant-degree polynomials, over the same prime field. Applications include exponential lower bounds for CNFs in $\operatorname{Res}(\operatorname{PC}_r/\mathbb{F}_p)$ and hence in $\operatorname{Res}(\oplus_p)$, separations betwe

Tyler Cohen, Paul B. Demorest, Jeremy G. Baier, Ryan S. Lynch

Pulsar timing array (PTA) experiments have seen evidence for a nanohertz-frequency gravitational wave background (GWB) through coordinated radio timing observations of millisecond pulsars. The significance of this evidence is expected to grow and the GWB&#39;s progenitor(s) uncovered with continued observations, especially as the next generation of radio tel

Jiawei Yu, Jian Liu

Text-to-music generation models are trained on massive music collections, creating a growing need for data attribution methods that can quantify the contribution of individual training samples. However, existing attribution methods are difficult to rigorously evaluate due to the lack of reliable ground truth, making it challenging to reliably assess their ac

Wensen Wu

ARC-AGI-3 evaluates agents in interactive environments whose rules and objectives must be inferred from observation. We present Kepler, an open-source harness that represents hypotheses as executable world models and validates them through retrospective transition checks and conditional prediction checks. Under one frozen Claude Opus 5 configuration, Kepler

Sachin Gupta

Fluent LLM explanations may not follow the evidence from a structured system. We present VERITYGATE, a four-gate checker for declared evidence IDs, entities, numbers, and claim types. It checks a fixed schema; it does not verify every fact in the prose. At r=0 and r=1, we test 900 instances per setting (450 grounded-ungrounded pairs) with GPT-4o-mini, Llama-

Yunge Jiang, Yehan Yu, Lili Song, Yan Mu

Remote state preparation is a fundamental quantum-information protocol that exploits prior knowledge of a target state to reduce the communication resources required for quantum-state distribution. Here, we propose a remote-state-preparation protocol for a stationary electron-spin qubit using hybrid entanglement between the electron spin and a coherent-state

Jiamu Zhang, Tianze Yang, Yucheng Shi, Evan Chen

A typed decision is a choice among a fixed set of options, returned as a probability rather than as text. Systems that need typed decisions today use models trained for that purpose. This report describes AnyJev, which reads a typed decision from one prefill of a pretrained instruction-tuned language model. The readout restricts the next-token distribution a

Zeyuan Gao, Cheol W. Lee, Oleg Zikanov

This paper develops a new moment-matching order reduction method for multi-input quadratic-bilinear descriptor systems. The proposed approach accommodates multiple inputs acting on both the differential and algebraic equations and constructs separate projection spaces associated with different input channels and input combinations. The method uses a matrix-f

Kerem Kat

Let $K_t$ be obtained by attaching a pendant path of length $t$ to a fixed rooted graph $K$. We prove that $\mathrm{Mo}(L(K_t))-\mathrm{Mo}(K_t)$, the difference between its line-graph and original Mostar indices, is exactly affine on each parity class beyond a sharp uniform cutoff. The slope depends on distance-level neighbor counts and is positive for ever

Jin-Yi Cai, Zhuxiao Tang

We prove a complexity classification of counting planar graph homomorphisms with non-negative weights. For a real symmetric matrix $M$ with non-negative entries, the problem $\PlGH(M)$ is either (1) P-time computable over all graphs, or (2) \#P-hard in general but P-time computable over planar graphs, or (3) \#P-hard over planar graphs. Furthermore, $\PlGH(M

Sinead Williamson, Jiaxuan Li, Nick Foti, Russ Webb

Large language models carry an internal notion of uncertainty in their sampling distribution, i.e., the probabilities they place on generating one answer rather than another. They can also be asked to state a confidence, in words or as a number: a verbalized uncertainty. Prior work suggests that internal probabilities track relative frequencies in the traini

Siqi Mo, Ehud Altman, Samuel J. Garratt

We study mixed-state entanglement in Gibbs states with strong $\mathrm{SU}(2)$ symmetry, focusing on locally interacting spin systems with ferromagnetic interactions. Our main result is to show that finite-temperature ordering transitions are associated with entanglement transitions, and therefore the steady state of strongly symmetric Lindbladians can exhib

Rui Liu, Bhavin Jawade, Haoqi Li, Shivam Mehta

Dubbing quality control requires a reference-free judge that can determine whether a candidate text line matches a speaker&#39;s visible articulation in both content and timing, using only silent video and text because dubbed audio may not yet exist. Existing visual speech recognizers and video-language models are poorly suited to this setting: even when fin

Tao Yang, Ben J. Sekely, Yashas Satapathy, Abraham Tishelman-Charny

Gain layer doping concentration determines the electric field profile in a low gain avalanche detector (LGAD) and is therefore the primary design parameter. This work reports its influence on the charge collection and timing behavior of 4H-SiC LGADs. Two mesa-isolated devices with cold He ion implanted termination differ only in gain layer doping, $4\times10

Stephen McCrory, Beomyeong Park, Nicholas Kitchel, Nehar Poddar

We present a planning and control approach to reactively use hand contacts to stabilize a humanoid in low stability scenarios, where only using feet contacts may result in a fall. Candidate contacts are sampled within the robot&#39;s reachable workspace, and a preview is computed by rolling out the centroidal dynamics through pre-impact, impact and post-impa

Amirhossein Mollaei Khass, Athanasios Cosse, Qiyu Sun, Nader Motee

Share the light, not the map. We study next-best-view selection for a team of robots, each of which builds its own 3D Gaussian Splatting map and keeps it private. A robot picks the view with the largest expected information gain (EIG) about the splats along its own path. This gain depends on the other maps. Their splats occlude its own and shine behind them,

Samuel Liu, Youngsun Kim, Martin Matak, Gilwoo Lee

Simulation can expand scarce real demonstrations for co-training, yet how world fidelity and similarity to human behavior affect policy performance remains unclear. We distinguish world grounding, which aligns simulation with the real system, and behavior grounding, which aligns simulated trajectories with human motion. We build a real2sim2real pipeline that

Fabio J. Fehr, Philip Torr

General-purpose models can adapt to many tasks from context, while specialised models can execute individual functions with less capacity. Yet obtaining such specialists requires task-specific training or adaptation. We ask whether they can instead be generated directly from a few demonstrations. Using ARC-1D as a controlled testbed, we show that individual

Youngsun Kong, Ki H. Chon

Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals. Amplitude- and frequency-modulated (AM-FM) representations are well suited to characterizing such dynamics and have shown broad utility in biomedical signal analysis. Recent approaches have incorporated neural networks to learn mode deco

Ayesha Tanveer, Khandakar Ahmed, Assefa Teshome, Oyetunde Gbadeyan

Ambulance ramping, the delay between hospital arrival and patient handover, is a critical operational bottleneck in Emergency Medical Services (EMS), yet its systemic magnitude and dynamics remain inadequately characterised at scale. This paper quantifies the scale, trajectory, and operational correlates of ramping across an entire statewide EMS system, anal

Sandipan De, Jin Wang, Vivek Gupta

Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploration, feature engineering, and business intelligence. Although numerous join discovery methods have been proposed, existing studies rely on method-specific benchmark construction, mak

Maximilian Tornes

We show that the spectral radius introduced by Shalit and Shamovich uniquely determines the underlying operator space structure: If E_1 and E_2 are operator space structures on C^d such that the corresponding spectral radii ρ_{E_1} and ρ_{E_2} agree on every d-tuple of operators, then the identity map is completely isometric. This answers a question of Scher

Nizhang Li, Ian G. Harris

Recent studies suggest that not every token needs to pass through all Transformer layers, motivating token-level adaptive models that selectively skip layers to reduce computation. Our experiments show that these execution choices also affect safety: existing token-level adaptive reasoning models exhibit higher harmful-response rates than their reference bac

Yang Ba, Michelle V. Mancenido, Rong Pan

Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data must add information relevant to the target task without introducing errors that offset their benefit, and the value of an example can change as the training set grows. We develop a linear theory that characteriz

Noah Ferich, Robert Royer, Cody Shakespeare, Jason H. Steffen

Studies suggest that protoplanetary disks around stars with high carbon-to-oxygen (C/O) ratios can produce terrestrial planets with carbon-dominated compositions instead of silicon-dominated ones. While previous work studied the evolution of these disks and the planets that form within them, none analyze in detail how the differences in the surface density p

Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni, Eun Woo Im

Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative priors learned through large-scale pretraining, pretrained video models often fail to reliably follow human intent, maintain temporal coherence, or satisfy physical and safety constr

Giacomo Dimarco, Axel Klar, Theresa Köfler, Lorenzo Pareschi

This work introduces a hyperbolic relaxation system designed for the simulation of kinetic equations in the low-Mach number limit. The methodology is built upon a micro-macro decomposition of the scaled BGK model, which reformulates the kinetic distribution function into a coupled system consisting of a macroscopic equilibrium part and a microscopic non-equi

N. A. Zimbovskaya

Fine features in the Fermi surface geometry, such as nearly cylindrical or nearly paraboloidal strips or local flattenings, are examined as regards their effect on the electronic, mainly galvanomagnetic, properties of metals. It is shown that under certain conditions, these features may significantly change the way a conventional normal metal or a layered st

Zhixi Zhu, Kristina Gligoric

Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one frame per second requires millions of tokens. To reduce costs, researchers rely on heuristics such as sampling a subset of frames, compressing videos into image grids, or using only a single modality. However, i

Max A. Alekseyev, Dmitry I. Khomovsky

Let $A$ and $B$ be polynomials with integer coefficients. We study nonzero integer solutions of $$y\mid A(x),\qquad x\mid B(y),$$ using quotient transformations that may change the polynomial pair. We call this process \emph{congruence jumping}. We establish a general reciprocal-polynomial rule governing such jumps, without coprimality, monicity, or unit ass

Mingyang Gong, Adiesha Liyanage, Braeden Sopp, Muzhou Chen

Motivated by accessing crop plots in a field, where each crop plot can only be visited by a given pair of entry/exit points (we have three different types, each having four cases), we study the corresponding Minimum Spanning Tree (MST) problem of such a planar set of axis-aligned unit squares. It turns out that this crop plot distance does not satisfy triang

Ayham Yousef, Qiang Ye, Qiang Cheng

Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed from the trajectories themselves, and how they are constructed determines what a forecast means. We study $201{,}710$ novel patent technologies (first-time IPC code pairings, USPTO

Kamran Ullah Afaq, Basit Raza

Editing a chest radiograph requires completing the requested change while preserving unrelated content. We study a latent diffusion editor with an instruction-independent source trajectory and an instruction-conditioned editing trajectory. A learned gate mixes their post-sampler candidates at each executed step, and a separate image-space mask composites the

Artur Tursunbaev

We prove that if $F$ is a noncyclic free group, $R\triangleleft F$ and $S\le F$, then the inclusion $R&#39;\le S&#39;$ implies $R\le S$. Thus the normality hypothesis on the second subgroup can be omitted from the Auslander--Lyndon theorem. No restrictions are imposed on ranks or indices.

Lance J. Dixon, Claude Duhr, Francesca C. Fernandes, Victor Mishnyakov

Antipodal self-duality (ASD) is a remarkable property that relates an amplitude to itself, after performing a suitable kinematic map and reversing the letters of the symbol (more precisely, acting with the antipode of the Hopf algebra of multiple polylogarithms). ASD has been seen in only one process in planar $\mathcal{N}=4$ super-Yang-Mills theory, and its

Tien Dat Vu, My Nguyen Bach, Phuoc Vinh Nguyen, Minh Doan

This paper develops a distributed adaptive neural interval observer for unknown nonlinear systems with locally incomplete measurements. Each sensor node constructs lower and upper state estimates using its local output and neighboring observer information, while unknown nonlinear dynamics are approximated by adaptive neural models. A distributed adaptation m

Yize Liu, Ke Wang, Mac Schwager, Yiqing Xu

A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has already completed. Current vision-language-action (VLA) policies often fail once the information needed for action disappears from the current observation, making memory critical for long-horizon robot behavior.

Tien Dat Vu, My Nguyen Bach, Minh Doan

This paper develops an event-triggered fixed-time integral reinforcement learning framework for optimal control of unknown nonlinear systems. An integral data-driven identifier is first used to reconstruct the unknown dynamics, after which an inverse-optimal formulation is employed to construct a fixed-time running cost. A learning law satisfying the practic

Guillaume Huber, Pavaman Bilgi, Charles-Antoine Claveau, Shane Jacobson

Sensor noise is a fundamental barrier to low-flux astronomical imaging and spectroscopy, where the ideal sensor would unambiguously distinguish individual photon arrivals. While optical semiconductor arrays can reach single-photon sensitivity, comparable performance in the infrared has largely required superconducting detectors. These must be operated at tem

Haoyu Liu

We prove that the graph of a standard one-dimensional Brownian motion, $Γ_B=\{(t,B_t):t \in [0,1]\}$, is almost surely not conformally removable. This resolves the question left open by Doherty and Miller in their study of the Sobolev removability of the Brownian graph~\cite{DM26}. We also construct non-removable $α$-Hölder graphs for every $α\in (0,2/3)$, i

Corinn Tiffany, Wen Zhang, Eugene Bagdasarian, Lillian Tsai

Contextual security defenses prevent AI agents from taking rogue actions by synthesizing a task-specific policy and enforcing it on the agent&#39;s tool calls. In multi-step tasks, however, which actions are valid often depends on what the agent has already done and learned. We present Sapien, a policy engine for enforcing stateful contextual policies. A Sap

Laura Toma

This chapter surveys terrain visibility models, algorithms, and applications. It introduces basic definitions, from line-of-sight, single-viewpoint viewsheds to cumulative and total viewsheds, on both Triangulated Irregular Network (TIN) and regular grid terrains. Reviews exact and approximate algorithms and highlights key application domains including archa

Umair A. Zaidi, An-Lun Wu, Wei-Chun Lin, Thomas S. Hwang

Objective: To assess the accuracy and efficiency of a large language model (LLM) using two prompt strategies to extract structured data from ophthalmic diagnostic PDF reports. Methods: Twenty deidentified reports across four types (Visual Field, OCT Glaucoma Overview, OCT retinal nerve fiber layer Single Exam, and OCT Thickness Map; n = 5 each) were processe

Yee Chit Wong, Marcel F. Langer, Reinhard J. Maurer, Nicholas D. M. Hine

Machine learning interatomic potentials (MLIPs) have advanced rapidly in recent years, yet the majority of models remain semilocal in nature and neglect long-range electrostatic interactions. A growing number of long-range models have emerged to address this limitation, but systematic benchmarks comparing their performance on physically realistic systems rem

J. H. Ramirez-Gonzalez

We consider the estimation of drift, diffusion, and noise covariance from discrete observations of stochastic differential equations driven by Gaussian processes. For a fixed observation horizon $T>0$ and a known initial state $x_0\in\mathbb R$, we study \begin{equation*} dX_t=a(X_t)\,dt+σ(X_t)\,dZ_t^{β,f}, \qquad X_0=x_0,\quad 0\leq t\leq T. \end{equation*}

Prakriti Biswas, Kobi Abayomi, Upmanu Lall

Weather Jiu-Jitsu is a control paradigm for extreme climatological events, inspired by chaos theory. As a proposition, small, precise, targeted, and cost-inexpensive perturbations can redirect trajectories of a large dynamical system. This strategy has been demonstrated analytically in the Lorenz-63 system, where a naturally chaotic trajectory switching betw

Terry Dorsey, Kevin Huggins

Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirements. These structures accumulate over time, creating representational complexity that must be maintained by the enterprise and interpreted by information consumers and AI systems. This paper introduces Enterpri

Gaute Linga, Tanguy Le Borgne, Joris Heyman

Recent advances have linked mixing in porous media to the deformation of infinitesimal fluid elements. However, these Lagrangian frameworks generally neglect or simplify interactions with solid surfaces. We consider transport of thin solute blobs (strips or sheets) near single generic obstacles, in particular along separation lines (separatrices) which conne

Karol Herrera, Andrés Rubiano

We introduce source-anchored algebraic amenability for Hausdorff ample groupoids, a Følner condition formulated in the Steinberg algebra and localized at compact open subsets of the unit space. The unanchored bisectional condition is exactly algebraic amenability of the Steinberg algebra, whereas the anchored version recovers ordinary amenability for one-uni

Bahar Acu, Puttipong Pongtanapaisan, Ritikaa Singhal

In this paper we define the ascending number and bridge number for virtual and welded spatial graphs. We prove that the former admits a lower bound in terms of the latter. This relationship allows us to compare different notions of unknotting virtual spatial graphs by crossing changes.