Published: 2026-08-04 14:58:09
Authors: Ziyang Pan, Fan Bu
Categories: stat.ME, stat.AP
Abstract:
Safety surveillance increasingly involves repeated monitoring of many exposure-outcome signals in observational healthcare data, where sparse information, dependence across related signals, and systematic error can complicate inference. Existing frameworks typically focus on either correcting residual bias using negative controls or borrowing information across exposure-outcome pairs, but not both. We propose a multi-signal Bayesian sequential surveillance framework that integrates empirical bias correction with low-rank latent factor modeling. At each analysis time, a hierarchical Bayesian model learns exposure-specific bias distributions from negative control outcomes assumed to have null latent effects. Conditional on these distributions, low-rank latent factors are estimated across exposures and outcomes of interest to share information across correlated signals. As new data accrue, posterior inference is updated sequentially, yielding bias-corrected posterior summaries of effect sizes across multiple monitored signals. We illustrate the method in a postmarket vaccine safety surveillance study using a large US insurance claims database.
Published: 2026-08-04 14:54:14
Authors: Tong Ling, Hang Lei, Feng Xiao, Changhui Sun, Jiahang Xie, Hao Liu, Lu Liu, Yanlong Du
Categories: cs.CL, cs.AI
Abstract:
Masked diffusion language models (MDLMs) enable parallel generation and bidirectional context modeling, but their positional context differs fundamentally from that of autoregressive (AR) models. Whereas AR decoding exposes a contiguous prefix, MDLM denoising produces dynamic, non-contiguous configurations of revealed and masked tokens. Conventional positional encodings such as RoPE capture sequence order and pairwise displacement but remain insensitive to this evolving token-availability structure. To address this limitation, we propose MDLMPE, a positional encoding designed specifically for masked diffusion. To the best of our knowledge, MDLMPE is the first method to make positional representations explicitly aware of the changing revealed/masked configuration. It represents token availability as a binary sequence, applies distance-aware Gaussian weighting, and projects the resulting pattern through a cosine basis to obtain distribution-aware positional features. These features are added to token embeddings and mapped by a lightweight MLP to angular offsets that modulate the standard RoPE phases. Extensive experiments on LLaDA and DREAM demonstrate that MDLMPE generally outperforms conventional positional encoding methods across supervised fine-tuning, pretraining, zero-shot evaluation, and block-diffusion settings. Further ablations show that the complete combination of availability state, Gaussian locality, spectral basis, and embedding injection yields the strongest result. These results establish the evolving token-availability distribution as a useful positional signal for masked diffusion language models.
Published: 2026-08-04 14:53:37
Authors: Jie Li
Categories: math.DS
Abstract:
We study the composition of temporal ergodic averaging with spatial averaging over shrinking metric balls, and determine sharp Orlicz endpoints for the corresponding unrestricted joint limit. For consecutive Birkhoff averages normalized by $NΛ_q(N)$ ($q\ge 0$), the sharp Orlicz endpoint is $L\log_{q+1}L$. In particular, the ordinary case $q=0$ yields an $L\log L$ local joint convergence theorem under the Lebesgue differentiation property alone, while $L^1$ fails even on the Euclidean interval, answering two questions of Young. The same $L\log_{q+1}L$ endpoint holds for prime averages for every $q\ge 1$. For arbitrary time sequences, $L\log_qL$ always suffices at the same normalization $NΛ_q(N)$ ($q\ge 1$), and this endpoint is sharp in the Orlicz sense for fixed-base exponential sequences $k^n$ ($k\ge 2$) and for sequences with polynomial ratio separation, including $n!$.
The positive results rest on a local stability principle: under the ball Lebesgue differentiation property, pointwise temporal convergence lifts to the local joint limit whenever the associated temporal maximal function admits an $L^1$ majorant. The additional logarithm in the regular-time case comes from lifting restricted logarithmic maximal estimates from sets to general functions. The arbitrary-sequence theorem uses a dyadic decomposition instead. The lower bounds are local $\infty$-sweeping out constructions, obtained by weighted local sweeping out for polynomial-growth regular times, by residue constructions for polynomially ratio-separated sequences, and by digit constructions for fixed-base exponentials.
Published: 2026-08-04 14:53:35
Authors: Sora Miyashita
Categories: math.AC
Abstract:
Let $(R,\mathfrak m)$ be a Noetherian local ring admitting a canonical module $ω_R$. We prove that $R$ is quasi-Gorenstein if and only if $\operatorname{Fitt}_1^R(ω_R)\congω_R$. When $R$ is Cohen-Macaulay, the same condition characterizes the Gorenstein property, confirming an expectation of Eisenbud, Ficarra, Herzog, and Moradi and extending their result for canonical ideals in local Cohen--Macaulay domains of type at most two.
Published: 2026-08-04 14:51:56
Authors: Leijun Zhou, Zhihao Liu, Xiang Qu, Chenxu Liu, Yifei Liu, Yanke Yu, Jingzhe Xu, Xuejun Wu, Buyue Qian, Xi Chen, Yaowei Zheng, Junhao Hu
Categories: cs.AI
Abstract:
Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economically valuable task domains, do not always design training and test tasks such that test-time gains can be attributed to training experience, and remain vulnerable to data contamination. We present GDPevo, an evolution-native benchmark grounded in GDP-related enterprise workflows, together with the fully automated data pipeline that generates it. Its core mechanism, rule hybridization, decomposes each enterprise workflow into atomic business rules, distributes subsets of these rules across training tasks, and recombines them in held-out test tasks so that test-time gains are attributable. GDPevo spans CRM, ERP, finance, healthcare, legal, and data-centric workflows. Its V1 release contains 120 tasks in 12 groups, with five training and five held-out test tasks per group. Full automation enables the pipeline to expand the suite to 240 tasks in 24 groups (V2) within two days, providing a practical response to contamination. Using GDPevo, we evaluate four agents, each comprising a harness and a model, under four supervision types. Self-evolution consistently improves held-out accuracy by up to 16.44 percentage points. But the best evolved agents remain far below the fully informed oracle ceiling of 91.6%, indicating that the self-evolution ability of current agents remains far from fully realized. We publicly release the pipeline, benchmark, and full evaluation results at https://github.com/Prism-Shadow/GDPevo.
Published: 2026-08-04 14:48:51
Authors: Emmanuel A. Olanrewaju, Santosh Adhikari, Zhiyin Niu, Michael Nikolaou, Jeremy C. Palmer, Jeffrey D. Rimer, Mingjian Wen
Categories: cond-mat.mtrl-sci
Abstract:
Zeolites are microporous crystalline materials with diverse frameworks, widely used in industrial applications such as petroleum refining and molecular separation. Unlike most zeolites, EMT can be synthesized under mild conditions (at low temperatures and without the use of organic structure-directing agents), making it attractive for cost-effective and environmentally sustainable production. However, the specific synthesis conditions that selectively produce EMT rather than similar frameworks like FAU are not yet well established. In this work, we develop machine learning (ML) models to guide the discovery of synthesis conditions for EMT zeolites. Our dataset comprises 174 experimental synthesis attempts, recording reaction time, temperature, silica and alumina sources, Si/Al stoichiometric ratio, and other synthesis parameters. We apply both classical ML methods and pretrained foundation models to predict zeolite framework outcomes from these synthesis parameters. Feature importance analysis identifies critical parameters for EMT formation, validating known synthesis principles. Leveraging the ML models, we explore the synthesis space and identify six promising new conditions for EMT formation. Experimental validation confirms EMT crystallization in five cases, including two with Si/Al stoichiometric ratios outside the training dataset's range. Evaluation on independent literature-reported synthesis conditions further demonstrates the generalizability of the model. This work demonstrates a data-driven approach to accelerating zeolite synthesis, closing the loop between ML prediction and experimental validation.
Published: 2026-08-04 14:46:52
Authors: Hung Tong, Antonio Punzo, Cristina Tortora
Categories: stat.ME
Abstract:
Missing values present a common challenge in statistical modeling, so handling them properly is an important research direction. Among the various mechanisms that can generate missing values, the most common is the missing-at-random (MAR) mechanism, in which the probability of missingness depends only on observed data and not on unobserved data.
This paper addresses the problem of estimating a multivariate linear regression model with multiple random covariates in the presence of MAR values in both the response and covariate spaces using a maximum likelihood (ML) framework.
The proposed methodology models the joint distribution of responses and covariates through a conditional-marginal factorization of a multivariate Gaussian distribution. This formulation can be interpreted as a reparameterization of the multivariate normal distribution when the variables can be naturally partitioned into responses and covariates.
Parameter estimation is performed using the expectation-maximization (EM) algorithm, which facilitates the imputation of missing values while preserving the distinct roles of responses and covariates.
We extend this framework to the model-based clustering setting by considering a mixture of multivariate linear regressions with multiple random covariates. This extension enables soft clustering under incomplete data and accommodates MAR values in both the multivariate responses and covariates. Hence, it represents one of the most general model-based clustering solutions for regression data currently available in the literature.
The effectiveness of the methodology is demonstrated through a simulation study, and the advantages of the proposed reparameterization are illustrated using the Automobile dataset, which contains missing values.
Published: 2026-08-04 14:44:22
Authors: Hao Dai, Yue Zhang
Categories: quant-ph
Abstract:
Gaussian states are fundamental in continuous-variable quantum information, yet characterizing non-Gaussianity remains challenging due to the non-convexity of the Gaussian set. Existing witnesses typically rely on Wigner negativity or other information-theoretic quantities. In this work, we develop a group-theoretic, multi-copy approach to detect non-Gaussianity in bosonic systems. We study passive linear optical transformations that mix copies of a quantum state and analyze their commutation with identical Gaussian unitaries applied to each copy. Orthogonal copy-mixing transformations commute with the symplectic part of the Gaussian action, while the displacement part restricts the symmetry to the stabilizer of the collective mode. This structure yields a family of witnesses satisfied by all single-mode Gaussian states. Fixing the thermal reference parameter via the purity, violation of these identities certifies non-Gaussianity. We illustrate the method with several single-mode examples and present an experimental protocol based on passive interferometry and photon-number-resolved detection, showing that the relevant multi-copy expectation values can be estimated from bounded phase observables. Finally, we extend the construction to multi-mode systems and discuss how the same symmetry framework may lead to quantitative measures of non-Gaussianity.
Published: 2026-08-04 14:43:52
Authors: Mohammad Yahyavi, Sina Gholizadeh, Sohrab Behnia, Bilal Tanatar
Categories: nlin.CD
Abstract:
Intermittency represents a fundamental route to chaos in nonlinear dynamical systems. In this work we introduce an adaptive control strategy in which the control parameter of an intermittent system is promoted to a dynamical variable that evolves autonomously under an auxiliary nonlinear map drawn from the same functional hierarchy as the system itself. The construction eliminates the need for orbit identification, local linearization, and trajectory-triggered perturbations, which are central ingredients of conventional feedback schemes. The theoretical framework is developed within a class of one-dimensional nonlinear ergodic maps with exactly known invariant (Sinai--Ruelle--Bowen) measures, for which we derive in closed form (i) the dynamics and invariant measure of the evolving control parameter, (ii) the invariant measure of the coupled system, and (iii) the $q$-generalized Lyapunov exponents before and after control. The generalized Lyapunov spectrum serves as an analytical order parameter for the control process: the collapse of its positive regions provides a quantitative and initial-condition-independent signature of chaos suppression, and yields the sensitivity to initial conditions in explicit form. To establish the physical relevance of the approach beyond low-dimensional maps, we apply the same construction to a cluster of three interacting ultrasound-driven microbubbles described by the Keller--Herring model, promoting the experimentally accessible acoustic driving frequency to a dynamical variable. Systematic bifurcation and Lyapunov analyses, performed over wide ranges of driving pressure, frequency, and equilibrium radii, demonstrate that intermittent chaotic radial oscillations are progressively suppressed and replaced by stable periodic motion.
Published: 2026-08-04 14:36:44
Authors: Chao Peng, Ruida Hu, Ajitha Rajan, Tegawendé F Bissyandé, Jacques Klein, Cuiyun Gao
Categories: cs.SE, cs.AI, cs.LG
Abstract:
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools. Yet they lack a dedicated testing methodology. We survey 197 real-world TUI applications: only 12% of test code exercises the interface, and 45% of those tests never send input, checking a static frame instead. We turn these applications into a headless benchmark spanning ratatui/Rust, bubbletea/Go, textual/Python, and ink/TypeScript, packaging each as an instrumented Docker image. We record line and widget coverage where reliable, rendered terminal states, and crashes. Under equal wall-clock budgets, we compare four frontier LLMs with random exploration. No model dominates. Random is a strong time-budgeted baseline, but its crash advantage comes from higher throughput: per interaction, LLM guidance is more efficient and uniquely reaches input-gated faults. Automatically deriving launch inputs yields the largest practical gain, enabling applications that otherwise never start. Line coverage poorly predicts crash discovery, weakening it as a proxy for test effectiveness. Automated TUI testing is feasible but far from solved, and honest baselines matter more than model choice. We release the coverage tool tuicov at https://github.com/tui-testing/tuicov and the testing framework tuibot at https://github.com/tui-testing/tuibot.
Published: 2026-08-04 14:36:34
Authors: Sandy Abdo, Bill Kapralos, Priyamvada Tripathi, KC Collins, Adam Dubrowski
Categories: cs.SD, cs.AI
Abstract:
Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability. Artificial intelligence (AI)-driven audio generative models are rapidly growing in popularity and have the potential to transform the way sound is synthesized and used across various applications. In response to this growing momentum, this chapter reviews and analyzes recent AI-based generative models for sound effect synthesis, with a focus on how different input modalities (text, visual, audio, and multimodal) affect the quality, controllability, and contextual relevance of the generated audio. It examines 30 peer-reviewed articles sourced from Google Scholar, IEEE Xplore, and the ACM Digital Library, exploring the evolution of AI generative models over the past five years. The results show that multiple models achieved state-of-the-art performance, producing high-fidelity, semantically aligned, and increasingly temporally coherent sound effects across tasks. However, despite these advances, the review identifies persistent challenges, including limitations in temporal synchronization for complex multi-event scenarios, gaps between objective metrics and human perception, and trade-offs between controllability and generative diversity. Overall, the chapter highlights that AI-driven sound effect generation is progressing toward more adaptive, scalable, and context-aware systems, offering significant implications for future sound design workflows and interactive media applications.
Published: 2026-08-04 14:32:43
Authors: Shuo Ren, Yaohui Han, Libo Shen, Zhiqiang Jia, Rongliang Fu, Bei Yu, Tsung-Yi Ho
Categories: cs.AI
Abstract:
As Moore's law slows, the industry is turning to three-dimensional integration; yet in merged 3D-IC flows, routed designs expose bond-level defects with no 2D analogue, and post-route engineering change orders (ECO) remain manual, expertise-bound work. Worse, the standard edit-then-fully-reroute practice entangles a repair with router churn, so a signoff number cannot be attributed to the edit that motivated it. We present AgenticECO, an evidence-gated tool-using agent workflow for 3D-IC ECO on the open-source TaiWei flow, paired with EcoRoute, a minimal-disturbance ECO-routing layer that drives the unmodified pinned router so a repair is attributable to its edit. Across nine matched natural defect cases under identical budgets, AgenticECO clears seven versus two for both full reroute and stock repair, at 0.66\% mean disturbance over cleared cases and zero clock nets touched, and a cross-backbone rerun under the same sealed contract clears all nine. Controlled studies show that the repair moves are necessary under preservation, that occupancy-aware choice buys legal landings rather than repair success, and that under tightened clocks minimal disturbance flips accept versus reject. Three preregistered visual studies localize the pixel instrument's edge to contested landing sites, and a preregistered blind diagnostic exactly restores every held-out injected defect, the only arm with zero wrong edits. Every accepted result passes routing, fresh extraction, max/min timing, DRC, and structural-equivalence gates. Code, environment, and per-episode audit artifacts are released as supplementary material.
Published: 2026-08-04 14:19:59
Authors: Hendrik Vincent Koops, Hao Hao Tan, Elio Quinton
Categories: cs.SD
Abstract:
Recent audio restoration increasingly relies on large-scale conditional latent generative modeling, including diffusion, Schrodinger Bridges, and Flow Matching variants, to invert degradations such as bandwidth limitation or noise. We present an analysis of the performance of various state-of-the-art methods compared to simple arithmetic transformations in the latent spaces of multiple neural codecs for musical bandwidth extension. We show that estimating a single transport vector between the clean and degraded latent centroids on a reference set, and adding it to degraded latents, can yield restoration performance competitive with large diffusion models. This suggests, first, that some neural codec latent spaces exhibit structure aligned with audio bandwidth; and second, that in such cases complex conditional models may offer only limited gains over a simple vector addition. We argue that these findings reveal an interesting avenue for future research whereby models could take advantage of the latent space structure in order to offer greater training and parameter efficiency, and overall better performance. Additionally, we propose to consider this simple arithmetic transformation as a baseline for music bandwidth extension research, as it allows an assessment of the contribution of learnable parameters towards restoration performance.
Published: 2026-08-04 14:18:55
Authors: Georgy Lukyanov
Categories: econ.TH
Abstract:
Actions determine not only payoffs but what can be learned. I study communication across successive decision makers when hidden effort governs public evidence and a sender motivates her successor while underweighting effort cost. Common sender rankings can force every message into a Bayes-unavoidable nonidentifying region. If experimentation is costly, strong motivation can make persuasive encouragement incredible and produce inactivity after a confounded failure. If maximal effort is technologically saturated, the same bias can produce universal overexertion after a bad pooled history, which is equally uninformative. At such nodes collapse is monotone: once strong motivation eliminates the identifying action, strengthening it cannot restore information. Moderate motivation instead preserves causal distinctions. In a threshold application, stronger motivation raises measured success yet lowers welfare because society fails to learn that less effort would suffice. A Kullback--Leibler decomposition shows that certification directly adds no information after entry into a fixed recursively nonidentifying region. Whether it prevents entry depends on attribution--selection alignment: do certificate-induced posteriors preserve the identifying action? The corrective action payment changes sign across technologies---subsidize effort when agents do too little, but subsidize restraint when maximal effort is saturated. Motivation selects the costly action, not the informative one.
Published: 2026-08-04 14:18:04
Authors: Galvin Ng, Luca Mungo, Damien Bertrand, François Lafond
Categories: econ.GN
Abstract:
A good representation of the population of firms and households is essential for large-scale economic models. While there exist good methods to create synthetic populations of households, creating synthetic populations of firms and, crucially, their supply chain links, is typically much harder. Here, we introduce a flexible method to create synthetic supply networks that match both the known properties of firm-level supply networks and the properties of aggregated input-output tables used in macroeconomic models. Our method is fast, and because it uses only publicly available data, it is fully reproducible and can be easily extended.
Published: 2026-08-04 14:17:35
Authors: Andreas Sauter, Sumit Sourabh, Drona Kandhai, Erman Acar
Categories: cs.LG, cs.CE
Abstract:
Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads. Yet CIR models capture only \emph{correlated} co-movement, not \emph{causal} influence between series, so they cannot answer the system's response when one series is externally shocked, which observational conditionals confound with historical co-movement. We make two contributions. First, an amortized model for distributional causal effect estimation that frames trajectories as time-stamped observations and predicts the calibrated multi-horizon shock response without retraining per scenario. Second, a causal multivariate CIR data-generating process that supplies the paired observational and interventional ground truth that real markets cannot. We instantiate and calibrate the framework on CDS spreads as a testbed. CIR-ACTIVA's validity is established on synthetic ground truth, independent of how well the simulator matches reality, while practical grounding is assessed by backtesting the generated traces against real CDS data. Against observational and amortized causal-inference baselines, CIR-ACTIVA leads on both causal selectivity in the joint distribution and horizon-resolved calibration, retaining its selectivity once the interventional law varies over the horizon, with gains concentrating at short horizons. This opens up a class of what-if queries on coupled spread systems, CDS stress testing among them, that observational forecasters cannot answer.
Published: 2026-08-04 14:10:39
Authors: Gabriel Cuomo, Simone Giombi, Luigi Tizzano
Categories: hep-th, cond-mat.str-el
Abstract:
The lightlike Wilson line cusp underlies the Sudakov double logarithm and is a central ingredient in gauge-theory factorization. We ask what replaces this behavior for general conformal line defects in Lorentzian signature. We argue that, when the defects admit local endpoint operators, the cusp anomalous dimension has a finite large-boost limit fixed by the scaling dimensions of the corresponding defect-creation operators. We further analyze the distinct analytic continuations of the cusp, elucidate their physical interpretations, and propose a positivity bound on the Lorentzian cusp anomalous dimension. We test these predictions in several perturbative examples, including pinning-field defects and spin impurities, and discuss their possible implications for gauge theories.
Published: 2026-08-04 14:06:59
Authors: Anastasia Pipi, Valentin Duruisseaux, Emily Been, Xuecheng Tao, Taylor L. Patti, Anima Anandkumar, Prineha Narang
Categories: quant-ph
Abstract:
Quantum optimal control is a key tool for steering quantum dynamics, but its computational cost grows rapidly with the Hilbert space dimension. Here, we introduce a Fourier Neural Operator (FNO)-based framework for learning high dimensional molecular quantum dynamics and accelerating the inverse design of control protocols. Given an initial molecular population distribution, laser frequency, and polarization, the FNO predicts molecular-motional population dynamics up to $10^7$ times faster than GPU-accelerated numerical propagation with CUDA-Q Dynamics. Using this fast and differentiable surrogate, we develop the FNO stochastic pulse-measurement planner (FNO-SPMP), which constructs pulse sequences to purify an initially mixed Boltzmann distribution. We demonstrate the protocol in an 888-dimensional subspace of the hydronium molecule at 20 K, achieving a target-state population of 0.98 with a sequence success rate of up to 86.2%. In a shared discrete control space, FNO-SPMP achieves nearly twice the success rate of a reinforcement-learning baseline while using roughly half as many quantum control pulses and reducing pulse-sequence generation time from approximately 10 hours to 10-20 minutes. These results show that operator-learning surrogates can enable inverse design in quantum systems whose Hilbert spaces are too large for conventional direct optimization.
Published: 2026-08-04 13:59:58
Authors: Natan de Isídio, Paola Popesso, Sandro Tacchella, Anna Pasquali, Ilaria Marini, Daudi Mazengo, Victoria Toptun, Sean McGee
Categories: astro-ph.GA
Abstract:
Satellite galaxies dominate the quenched population at low stellar masses ($M_\star \lesssim 10^{10}~\rm M_\odot$), yet identifying which processes shut down their star formation, their relative importance, and on what timescales, remains a central problem in galaxy evolution. We use MaNGA-like mock galaxies from IllustrisTNG to dissect different satellite quenching pathways, paying special attention to the role of the circumgalactic medium (CGM) during quenching phase. We reconstruct the baryonic, dark matter, structural, and chemical histories of $\sim$7 300 galaxies (2 800 satellites), using time since infall as the physical axis along which quenching unfolds. Satellites retain rotation-supported stellar kinematics throughout quenching, with disturbed velocity fields confined to systems with $M_\star \lesssim 10^{10.5}~\rm M_\odot$. For the first time, we present the coupled time evolution of the depletion of both the hot and cool gas reservoirs after infall: satellites lose $\sim$90% of their hot CGM within $\sim$$4.2^{+0.6}_{-0.6}$ Gyr, increasing with residence time and independent of stellar mass. The hot gas mass correlates strongly with SFR, establishing the CGM as the long-term fuel reservoir, unlike quenched centrals, which retain massive hot halos likely maintained by AGN feedback. Present-day quenched satellites were accreted earlier than star-forming ones (6.5$^{+0.3}_{-0.3}$ vs. 4.3$^{+0.3}_{-0.3}$ Gyr ago), forming stars for at least $\sim$3 Gyr after infall before declining sharply, consistent with a delayed-then-rapid quenching scenario. Losing little stellar mass, yet with their gas depleted and their dark matter and metal-poor stellar outskirts tidally stripped, satellites emerge more compact and metal-rich than centrals at fixed mass. Our results suggest the gradual erosion of the hot CGM as the key link connecting infall to the slow shutdown of star formation.
Published: 2026-08-04 13:54:53
Authors: Alexander Perry
Categories: math.AG
Abstract:
For any uncountable algebraically closed field $k$ of characteristic $0$ and any $d \geq 3$, we construct a variety over $k$ of dimension $d$ with a Brauer class which violates the period-index conjecture for Hodge-theoretic reasons. When $d = 3$, our construction works even without the assumption that $k$ is uncountable; in particular, the period-index conjecture fails over $\overline{\mathbf{Q}}$.
Published: 2026-08-04 13:54:23
Authors: Chao-Nan Tong, Hong-Bin Tan, Ruo-Yu Liu
Categories: astro-ph.HE
Abstract:
Recently, the Large High Altitude Air Shower Observatory (LHAASO) detected ultrahigh-energy (UHE; photon energy E>100TeV) $γ$-ray emission toward SS 433, the microquasar embedded in the W50 nebula, making it a promising Galactic PeVatron candidate. We reanalyze the archival XMM-Newton observations covering the bipolar jets and the thermal X-ray shell north of SS 433, and derive spatially resolved profiles of the nonthermal X-ray intensity and photon index along both jets. The jet emission softens with distance from the source, implying a correspondingly evolving electron population. In particular, a hard electron component appears close to the jet bases, which can account for the UHE emission from SS 433 via inverse Compton radiation if the magnetic field remains approximately uniform along the jets. The result, however, is highly sensitive to the magnetic field profile. For flux-conserving configurations in which the field decreases as the jet expands, the stronger field required in the inner regions may reduce the number of X-ray-emitting electrons and suppress their inverse Compton emission. Furthermore, electron transport calculations show that injection only at the jet bases cannot reproduce the observed intensity and spectral evolution, particularly the downstream re-brightening features, indicating additional particle injection and/or re-acceleration within the jets.
Published: 2026-08-04 13:41:21
Authors: Filip Cano, Thomas A. Henzinger, Konstantin Kueffner
Categories: cs.AI
Abstract:
Safety shields are runtime enforcement mechanisms that restrict the actions of a controller to guarantee safety. Classical shields are usually synthesised for state predicates: the current physical state is either safe or unsafe, and the shield disables precisely those actions that can force the system into an unsafe state in the future. In many cyber-physical applications this view is too coarse. A vehicle approaching an obstacle should not only avoid collision, but also respect speed regulations, force limits induced by acceleration, and jerk limits to prevent injuries. From a physical perspective, these requirements are predicated over the derivatives of the state. This paper develops a finite-state safety-game construction for such high-order smoothness constraints. We define differential safety properties using finite differences over a discretised state space, characterise their expressiveness, and reduce shield synthesis to an ordinary safety game over a history state space. We give a synthesis algorithm whose shields store exactly $k$ past states for properties of order $k$ and prove that this memory is necessary. We describe an iterative synthesis procedure for a maximally permissive shield that operates over hierarchies of derivative constraints. The algorithm solves constraints iteratively in increasing order and uses the solution at each iteration to prune the state space for the next constraint. This makes shield synthesis more efficient in practice, as the algorithm refrains from exploring large regions of the state space that are known to be unsafe.
Published: 2026-08-04 13:39:36
Authors: Abraham Camelo-Guerrero, Jairo Diaz-Rodriguez
Categories: cs.CL, cs.AI
Abstract:
Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers align with both conference decisions and human reviewing priorities within the same controlled setting. We compare reviews from OpenAI GPT-5.4, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.6 with human reviews and final decisions for 300 topic-matched ICLR 2026 submissions, equally divided among oral, poster, and rejected papers. Each model reviewed every paper using identical instructions and rating scales after decision information was removed. Our study contributes a cross-provider analysis of three complementary dimensions: alignment with broad and fine-grained decision categories, differences in recommendation-scale usage, and thematic agreement in identified weaknesses. All three LLMs distinguished accepted from rejected papers, but none reproduced the oral versus poster distinction present in human ratings. Scoring patterns were provider-specific: Gemini assigned systematically higher ratings, while OpenAI and Claude were closer to humans for rejected and poster papers but more critical of oral papers. Human and LLM reviews also differed in emphasis, with LLMs more frequently identifying missing baseline comparisons and humans more often raising computational-efficiency concerns. These results show that broad decision alignment does not imply agreement with finer human judgments or reviewing priorities.
Published: 2026-08-04 13:34:52
Authors: Zhijing Hu, Changjun Fan, Yufan Deng, Zhiguang Cao
Categories: cs.AI
Abstract:
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficiency, and are usually designed manually by researchers. Existing large language model based automatic heuristic design methods can generate and screen candidates, yet they have difficulty further transforming candidate quality or failure states during execution into structural-level guid- ance for subsequent generation. We propose AutoSND, a three stage tree search framework for complete network dismantling pro- grams. Stage I broadly explores from simple heuristics and archives execution evidence. Stage II compiles candidate records into struc- tural policies concerning local signals, neighborhood access, and state update ranges. Stage III continues tree search conditioned on these policies and obtains the final quality prioritized and speed prioritized candidates, AutoSND-Q/S. Experiments on 12 real world networks and 3 large real world networks show that AutoSND achieves better search performance and stability and discovers more competitive and structurally interpretable network disman- tling programs. The final candidates form an interpretable structure that uses residual degree as the backbone, adjusts node order with bounded local signals, and restricts the state update range. Code is available at https://github.com/MirrorNew/AutoSND.
Published: 2026-08-04 13:30:09
Authors: Iris Bree, Federico Gasparotto, Sebastian Pögel, Xing Wang, Stefan Weinzierl, Xiaofeng Xu
Categories: hep-th, hep-ph, math-ph
Abstract:
In integration-by-parts reduction of Feynman integrals, the order relation in the Laporta algorithm determines a set of master integrals. In this paper we investigate the intersection matrices of the integrands of the master integrals that are obtained from a geometric order relation. With an appropriate definition of integrands and their duals, we find that the intersection matrices are simpler than expected: For a filtration-compatible basis, the entries of the intersection matrix are Laurent polynomials in the dimensional regularisation parameter $\varepsilon$. For an $\varepsilon$-factorised basis, the entries are instead integers, up to an overall power of $\varepsilon$, if the boundary values for the auxiliary functions of the rotation are chosen appropriately. This has practical consequences: We can systematically eliminate certain auxiliary transcendental functions, introduced in going from a filtration-compatible basis to an $\varepsilon$-factorised basis. We provide an algorithm that performs this elimination while minimising the number of required calculations.
Published: 2026-08-04 13:29:32
Authors: Angus Mingare, Isabelle Heuzé, Peter V. Coveney
Categories: physics.chem-ph, quant-ph
Abstract:
The quality of multi-scale modelling techniques in molecular electronic structure calculations, such as embedding and subspace methods, relies upon the chosen active space. The automation of active space selection is vital for ensuring the accuracy, reproducibility, and scalability in such calculations. In this work, we introduce Tensor Network Active Space Selection using the Entanglement Feature. Through the isolation of strongly correlated electrons, this method provides a scalable foundation for embedding methods in multi-scale modelling. By representing the purities of all possible orbital partitions as a Matrix Product State, our method isolates regions of strong electron correlation without requiring manual preselection of target atoms or the calculation of expensive high-order density matrices. The results demonstrate that this approach leads to lower ground state energies and more accurate dipole moments than other fully automated selection schemes such as those based solely on single-orbital entropy or the selection of spatial orbitals around the HOMO/LUMO gap.
Published: 2026-08-04 13:29:00
Authors: Maksymilian Wolski, Nicholas Hoernle, Johannes Forkel, Jakob Foerster
Categories: cs.AI, cs.MA
Abstract:
AI agents deployed in real-world settings must be capable of coordinating with humans and other AI agents they have not encountered before. Zero-shot coordination (ZSC) algorithms aim to achieve this by specifying high-level learning rules such that independently engineered agents can coordinate with each other at test time. Rigorous evaluation of ZSC algorithms remains difficult: ideally, multiple independent implementations of each proposed algorithm must be used, reflecting the variation that arises when independent parties interpret and implement the same specification. In practice, however, ZSC algorithms have almost exclusively been evaluated using a single implementation trained across different random seeds, with only a handful of works additionally varying the neural network architecture. This leaves open questions about robustness to specification ambiguities and implementation details. In this work, we provide the first systematic evaluation of this robustness. We introduce a new evaluation scheme, cross-implementation cross-play, varying implementation details that prior work has shown to affect the performance of multi-agent reinforcement learning (MARL) algorithms, and we evaluate Other-Play, a popular ZSC algorithm, with this scheme. Our findings are encouraging and suggest that, for Other-Play, the standard ZSC evaluation is, in fact, a reasonable proxy for this more thorough cross-implementation evaluation.
Published: 2026-08-04 13:28:12
Authors: Adam D. W. Clay, Tibor Jordán
Categories: math.CO
Abstract:
T. Hasunuma (J. Graph Theory, 2023) conjectured that if $G$ is a $k$-connected (resp. $k$-edge-connected) graph with minimum degree $δ(G) \ge k + m - 1$, and $T$ is a tree of order $m$, then $G$ contains a removable copy of $T$, that is, a subtree $T'$ isomorphic to $T$ such that $G - E(T')$ is $k$-connected (resp. $k$-edge-connected). We prove (a strengthening of) this conjecture. We also consider removable matchings in graphs with high minimum degree. We show, among others, that if $G$ is a $k$-edge-connected graph on at least $2m$ vertices with minimum degree $δ(G) \ge k + m$, then there exists a matching $M$ of size $m$ in $G$ for which $G-M$ is $k$-edge-connected.
Published: 2026-08-04 13:27:57
Authors: Mingyue Yang, David Lie, Nicolas Papernot
Categories: cs.CR
Abstract:
As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to malware classifiers. Although evasion and poisoning attacks against machine learning-based malware classifiers can cause misclassification of malware samples, it is not clear how these attacks work against data drift detectors and malware classifiers in combination. This work investigates the effect of evasion and poisoning attacks on the data drift detector along with the malware classifier. We demonstrate how unique characteristics of data drift detectors cause attacks against malware classifiers to work differently against them.
Published: 2026-08-04 13:26:12
Authors: Diego Cifelli, Adolfo Anta
Categories: eess.SY
Abstract:
The increasing penetration of converter-interfaced resources is making power-system stability assessment more challenging, particularly in heterogeneous grids containing both grid-forming and grid-following converters. Existing decentralized mixed small-gain and small-phase criteria provide scalable stability certificates, but they require all converters to satisfy the same type of condition at a given frequency. As a result, they cannot simultaneously exploit the low gain of grid-following converters and the favorable phase properties of grid-forming converters, leading to unnecessary conservatism. This paper proposes a partitioned mixed gain-phase decentralized stability criterion that allows distinct subsets of converters to satisfy different local requirements at the same frequency. Specifically, one subset can be certified through small-gain bounds, while the complementary subset is certified through small-phase bounds. The admissible trade-off between gain and phase margins is determined by a network-dependent quadratic constraint, yielding a technology-aware stability certificate that remains local at the converter level. The paper characterizes the admissible set of gain and phase bounds, establishes useful convexity and boundedness properties, and develops a practical procedure for selecting these bounds. The proposed method is demonstrated on heterogeneous systems containing grid-forming and grid-following converters, including a two-converter system and the IEEE 39-bus system, outperforming the standard decentralized small-gain and small-phase conditions.
Published: 2026-08-04 13:19:51
Authors: Haoze Lv, Ning Lu, Shengcai Liu, Shaofeng Zhang, Ke Tang
Categories: cs.NE, cs.AI
Abstract:
Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, existing methods primarily optimize a single heuristic, whereas practical optimization frameworks often rely on multiple interacting components. Directly extending single-heuristic methods is challenging because early component selection can overlook components with late potential, while independent evolution ignores inter-component dependencies. We propose MuEvo, an LLM-driven framework for evolving heuristic ensembles under ensemble-level feedback. MuEvo combines Dynamic Component Management, which uses short-budget probing and a reversible lifecycle to revise component priorities throughout the search, with LLM-Driven Co-Evolution, which coordinates component populations through Multi-Ensemble Evaluation, Cross-Component Information Sharing, Relation-Guided Pair Evolution, and Adaptive Budget Allocation. We evaluate MuEvo on selection hyper-heuristics and componentized ant colony optimization across four combinatorial optimization domains. Results show that MuEvo consistently improves human-designed frameworks and outperforms representative multi-component extensions of state-of-the-art LLM-AHD methods, demonstrating its effectiveness across both controller-mediated heuristic pools and functionally differentiated algorithmic components.
Published: 2026-08-04 13:17:30
Authors: Yinuo Jiang, Yongjie Ye, Zhou Tao, Xiang Zhuang, Qiang Zhang, Huajun Chen, Tiankai Li
Categories: cs.AI
Abstract:
On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable. However, the assumptions overlook a fundamental failure mode of language models: their token-level judgments can be driven by input-agnostic language priors, formatting conventions, or stereotyped reasoning templates rather than task-specific evidence. We refer to such optimization-relevant but weakly input-grounded supervision as spurious signals in OPD, which may produce large gradients while contributing little task-improving direction. To mitigate this issue, we propose SA-OPD, a Spurious-Signal-Aware On-Policy Distillation framework that identifies and filters misleading token-level supervision based on input-groundedness and optimization impact. SA-OPD introduces a lightweight input-groundedness proxy estimating whether a token-level distillation signal truly depends on the input. It then filters only tokens that simultaneously exhibit low input-groundedness and extreme distillation divergence, thereby removing high-impact spurious updates and achieving fine-grained OPD optimization. Extensive experiments on both large language model (LLM) and vision-language model (VLM) settings demonstrate that SA-OPD consistently outperforms Vanilla OPD and competitive selective methods. These results establish input-groundedness as a key dimension for OPD supervision selection and offer a simple, effective strategy for mitigating spurious updates.
Published: 2026-08-04 13:13:12
Authors: Alessandro Linzi
Categories: math.RA
Abstract:
We study Krasner quotient hyperfields arising from finite fields, $F_q/G_r$, where $G_r\le F_q^\times$ has index $r$. Building on the structure theorem of Baker--Jin, we determine the characteristic and C-characteristic of all sufficiently large such quotients: they depend only on the parity of $r$ and, when $r$ is even, on the residue class of $q$ modulo $2r$. In particular, the two Baker--Jin stable classes for even $r$ are separated by characteristic $2$ versus $3$, while the C-characteristic is always $1$.
We complement this structural result with a computational laboratory: sharp Weil thresholds for Baker--Jin large-$q$ isomorphism, empirical minimal stabilization bounds $N_r^{\mathrm{emp}}$, complete finite-field quotient atlases for hyperfield orders $n\le 7$, and comparisons with the enumerations of Ameri--Eyvazi--Hošková-Mayerová (orders $\le 6$) and Massouros--Massouros (order $7$). Among other findings, exactly $15$ isomorphism types of order $7$ arise as finite-field quotients, out of $277$ hyperfields of that order -- a concrete data point toward the Baker--Jin rarity conjecture for quotients. All algorithms and tables are available in an open-source package suitable for independent verification and arXiv ancillary material.
Published: 2026-08-04 13:04:31
Authors: Mounir Nisse
Categories: math.AG
Abstract:
We establish new Newton-polygon bounds for the singularities of amoeba contours of smooth plane curves. Our cusp estimate refines the degree-four bound of Lang--Shapiro--Shustin, reducing its leading coefficient from $8$ to $4$ while retaining the normalized area, boundary lattice points, and directional widths of the Newton polygon. We also obtain a new multiplicity-sensitive bound for transverse $s$-nodes, with the natural decay factor $1/\binom{s}{2}$. The proofs combine logarithmic Gauss maps, normalized fiber products, ramification theory, and saturated off-diagonal intersection schemes, revealing the distinct geometric mechanisms governing cusps and multiple nodes.
Published: 2026-08-04 13:02:48
Authors: Chunlei Meng, Jacqueline J. Pang, Pengbin Feng, Zhenyu Yu, Chun Ouyang, Zhongxue Gan
Categories: cs.AI, cs.MM
Abstract:
Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete. Existing methods for incomplete-observation MSA mainly follow two paradigms. Reconstruction-based methods recover missing information from observed modalities, while joint-representation methods learn directly from incomplete inputs. Although effective, these methods usually treat modality reliability only implicitly within representation learning or fusion design rather than modeling it explicitly. We argue that modality reliability is a central variable in incomplete-observation settings. Failure to model it explicitly gives rise to two related issues. The first is reliability mismatch, in which the affective evidence retained by each modality varies across samples and missing rates. The second is reliability propagation bias, in which messages from degraded modalities may adversely affect cross-modal interaction and predictive performance. To address these issues, we propose MRCF, a Modality Reliability-Calibrated Framework for MSA with incomplete observations. MRCF contains a Reliability-Aware Branch that estimates sample-specific modality reliability from intramodal quality cues and cross-modal semantic consistency, a Reliability-Guided Interaction Branch that uses the estimated scores to modulate cross-modal information flow, and a Reliability-Calibrated Fusion Module that integrates reliability and semantic cues for final prediction. Experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS show that MRCF achieves strong performance under standard incomplete-observation protocols. Further analyses provide evidence that explicit reliability modeling helps mitigate reliability mismatch and reliability propagation bias during interaction and fusion.
Published: 2026-08-04 13:02:47
Authors: Yuan Xie, Jiaqi Song, Xianliang Wang, Ming Lei, Jie Gao, Jie Wu
Categories: cs.CL, cs.SD, eess.AS
Abstract:
Modern LLM-based ASR systems have established multilingual capability as a standard feature, leveraging large-scale multilingual corpora and LLMs' cross-lingual knowledge to achieve competitive performance across multilingual benchmarks. However, joint modeling of languages with heterogeneous acoustic, phonological, and lexical characteristics inevitably introduces optimization conflicts, undermining language-wise specialization. To address this challenge, we propose Language-Specialized Multi-Teacher On-Policy Distillation (LS-MOPD), which decouples language-specific knowledge acquisition from multilingual capability integration: language-specialized teachers are independently optimized via reinforcement learning (RL), after which their expertise is integrated into a generalist multilingual student through language routing and token-level multi-teacher distillation, thereby reducing direct cross-lingual optimization conflicts. We further explore two acoustic-prefix configurations, static and dynamic, to examine how teacher--student prefix consistency influences the efficacy of on-policy distillation. Experiments on benchmarks covering Mandarin, Mandarin subdialects, Cantonese, and English demonstrate that LS-MOPD substantially outperforms RL baselines and consistently surpasses the empirical performance envelope defined by best-performing RL teachers, revealing its potential to generalize beyond all teachers in multilingual ASR.
Published: 2026-08-04 12:50:43
Authors: Cunming Zhang, Yu Pei, Michail Papadakis
Categories: cs.SE
Abstract:
Reproducing web GUI bugs from natural-language bug reports is critical for software maintenance, but remains difficult because reports often lack prerequisites such as dependencies and input files. Existing bug reproduction techniques mainly target code units or mobile applications and lack end-to-end visual execution and validation for web GUIs. We present ReBug, a context-aware agent system that reconstructs, executes, and validates browser-level reproduction procedures from web GUI bug reports by driving a real browser. ReBug separates reproduction into two stages. In the preparation stage, ReBug reconstructs missing prerequisites from the report and available artifacts, and it produces a high-level reproduction plan. In the execution stage, it performs tool-mediated interactions in the browser, maintains structured summaries of page state and action history, and validates the final state against expectations derived from the report. We evaluate ReBug on 667 real-world bug reports from four open-source web applications. On controlled current deployments, ReBug outperforms both baselines, achieving an average RSR of 49.96%, a mean task completion rate of 74.96%, and a mean action execution success rate of 86.54%. Our results show that explicit context reconstruction and state-aware browser execution effectively support report-derived browser reproduction, while historical replay shows that successful procedures often expose the original bug-present behavior on restored buggy versions.
Published: 2026-08-04 12:48:05
Authors: Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk, Vahid Abolghasemi, Naser Hossein Motlagh, Sadasivan Puthusserypady, Daniel Teichmann, Abdolrahman Peimankar
Categories: cs.AI, cs.LG
Abstract:
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality. Recent ECG foundation models offer transferable representations for automated AF detection. However, their relative effectiveness remains unclear because existing studies use different datasets, preprocessing procedures, classifiers, and validation protocols. This study presents FOUND-AF, a unified, leakage-controlled, and deployment-oriented benchmarking framework that evaluates the quality of pretrained ECG representations under identical experimental conditions. Nine publicly available foundation models from five families, including HuBERT-ECG, CLEF, ST-MEM, ECG-JEPA, and ECGFounder, were evaluated across four heterogeneous ECG datasets, namely AFDB, CinC2017, CPSC2021, and LTAFDB. All models were used as frozen feature extractors with standardized preprocessing, model-native resampling, a fixed XGBoost classifier, and recording-level grouped cross-validation. The evaluation included classification metrics, receiver operating characteristic analysis, paired recording-level bootstrap comparisons with Holm correction, embedding-space visualization, and computational efficiency profiling. The ECGFounder model consistently achieved the strongest overall performance across datasets while offering a favorable trade-off between accuracy, model size, inference time, and memory usage. FOUND-AF therefore provides a reproducible framework for selecting ECG foundation models and demonstrates that compact, clinically pretrained encoders can support robust and computationally efficient AF detection across heterogeneous acquisition settings.
Published: 2026-08-04 12:44:47
Authors: Alonso S. Castellanos, Guilherme Tizziotti, Wilson Olaya-León
Categories: cs.IT, math.AG
Abstract:
In this work, we investigate permutation decoding for algebraic geometry (AG) codes arising from algebraic curves defined by separated polynomials. Using automorphisms of the underlying curves, we construct permutation automorphisms of the associated algebraic geometry codes and exploit the resulting orbit structure to determine information and check positions. We introduce a class of curves, called SAP curves (Separated Additive Polynomial curves), and investigate one-point AG codes defined on them. For these codes, we obtain permutation decoding sets that correct burst errors supported on coordinates associated with rational points sharing a common coordinate. We further identify a subclass of special SAP curves, including Hermitian curves, generalized Hermitian curves, and certain maximal curves, for which additional automorphisms yield more powerful decoding sets.
Published: 2026-08-04 12:34:46
Authors: Yaozhi Wen, Jialong Guo, Zhenliang Ni, Han Shu, Xinghao Chen
Categories: cs.CV
Abstract:
While Vision-Language Models (VLMs) have demonstrated remarkable performance in processing and understanding both text and images, their large parameter sizes lead to significant computational overhead, limiting their deployment on resource-constrained devices. While pruning has been effective for compressing Large Language Models (LLMs), directly applying it to VLMs leads to significant performance drops, largely due to redundant visual tokens interfering with importance estimation. To this end, we propose SlimVLM, a structured pruning framework designed to compress VLMs while preserving their task performance. We introduce an adaptive visual token selection strategy for VLMs that leverages average text-to-visual attention scores to assess the importance of visual tokens, removing redundant ones during pruning based on a set threshold, thereby optimizing the importance calculation. Recognizing the varying tolerance to sparsity across different modules, we also propose a Sensitivity-aware dynamic pruning mechanism that determines the appropriate pruning ratio for each module by calculating the linear reconstruction error between the outputs of the pruned and unpruned modules, ensuring overall performance stability. Experimental results show that SlimVLM outperforms existing methods across multiple multimodal benchmarks, achieving state-of-the-art performance.
Published: 2026-08-04 12:32:26
Authors: Aparna Gupta, Avijit Pal, Bhaskar Paul
Categories: math.FA, math.CV
Abstract:
This paper studies the domain $\mathbfΘ_n$ from the perspective of operator theory. We obtain several characterizations of $\mathbfΘ_n$-contractions (respectively, $\mathbfΘ_n$-unitaries and $\mathbfΘ_n$-isometries) and establish their relationships with $Γ_n$-contractions (respectively, $Γ_n$-unitaries and $Γ_n$-isometries), tetrablock contractions (respectively, tetrablock unitaries and tetrablock isometries), and $\mathbfΘ_{n+1}$-contractions (respectively, $\mathbfΘ_{n+1}$-unitaries and $\mathbfΘ_{n+1}$-isometries). We prove that every $\mathbfΘ_n$-contraction admits a canonical decomposition into the direct sum of a $\mathbfΘ_n$-unitary and a completely non-unitary $\mathbfΘ_n$-contraction. We further develop a dilation theory for $\mathbfΘ_n$-contractions by obtaining necessary and sufficient conditions for the existence of minimal $\mathbfΘ_n$-isometric dilations. As an application, we show that the minimal $Γ_n$-isometric dilation arises as a special case of the minimal $\mathbfΘ_n$-isometric dilation. Finally, we identify a class of $\mathbfΘ_2$-contractions that always admit $\mathbfΘ_2$-isometric extensions.
Published: 2026-08-04 12:26:59
Authors: Haowen Yang, Yun Peng, Zishuo Ding
Categories: cs.SE
Abstract:
Large software systems often suffer from time inefficiencies that cause excessive execution time despite functional correctness. Localizing their fix locations is difficult because, unlike functional bugs, they produce neither test failures nor stack-trace clues, making traditional and recent LLM-based fault localization methods unsuitable. Runtime profiling provides alternative evidence but faces three challenges in repository-level settings: single-run profiling cannot reliably distinguish inefficiency hotspots from execution noise; existing profilers struggle to extract relevant execution paths from extensive background execution; and a semantic gap remains between observed hotspots and actual fix locations. We propose EffiHolmes, an LLM-based framework for repository-level time inefficiency fix localization. EffiHolmes uses differential profiling under default and scaled workloads to identify inefficiency hotspots, extracts compact execution paths connecting these hotspots to the reported inefficient function, and employs domain-guided LLM reasoning to locate the underlying inefficiency logic. We also introduce RepoEffi-Bench, the first benchmark for repository-level inefficiency localization, containing 140 high-quality issues collected from popular Python repositories. Experiments show that EffiHolmes consistently outperforms state-of-the-art retrieval-, agent-, and profiling-based baselines, improving file-level Acc@3 by 4.29 percentage points with GPT-5.1 and function-level Acc@5 by 15.00 percentage points with qwen3-4b. It also remains robust across model capacities.
Published: 2026-08-04 12:25:45
Authors: Hyungkyu Ham, Junhyeong Bae, Seungheon Lee, Myeongjae Jeon, Gwangsun Kim
Categories: cs.AR
Abstract:
This paper presents a heterogeneous decode-phase serving system that relocates the KV cache out of GPU memory, motivated by the retrieval-based sparse attention that recent frontier LLMs adopt to serve million-token contexts. It partitions a decode step by operation type: GPU nodes hold the model weights and execute the projections and MoE layers, while processing-near-memory (PNM) nodes hold the KV cache and index keys and execute every operation that reads them. We first show that the assumptions behind prior PIM and PNM designs no longer hold for these operations, and derive four design requirements for such a node. From these requirements, we propose KARAT (KV-cache-resident Accelerator for Retrieval-based ATtention), a general-purpose PNM design that is the design point meeting all four. A KARAT device combines large LPDDR capacity with general-purpose compute sized for the retrieval indexer, serving an operational intensity beyond what PIM/PNM designs built for low-intensity GEMV target while accommodating diverse sparse attention algorithms that fixed-function units cannot support as they evolve. To reduce pipeline bubbles as the two device types alternate between micro-batches, we further propose opportunistic, fine-grained micro-batch scheduling (OFMS), which hides expert all-to-all behind the other micro-batch's GEMMs, and context-length-aware micro-batch rebalancing (CMR), which equalizes their token counts despite the variance in context length. Across three state-of-the-art models and real agentic traces, our proposed system improves throughput per TDP under a service-level objective by 2.09-6.13x over a GPU-only baseline and runs training-free sparse attention methods with 1.36-3.21x improvements.
Published: 2026-08-04 12:23:23
Authors: Rodrigo Jaeschke-Ubiergo, Xanthe H. Verbeek, Colin Lange, Sergio Rodriguez, Atasi Chakraborty, Alexander Mook, Jairo Sinov
Categories: cond-mat.str-el, cond-mat.mtrl-sci
Abstract:
We demonstrate the recent concept of emergent surface altermagnetism through its unique signatures in \KVSO. We show that for bulk antiferromagnetically ordered \KVSO, the (001) surface exhibits $d$-wave altermagnetism. Our results fully explain the recent seemingly contradicgting experimental evidence, independently showing both an antiferromagnetically ordered bulk from neutron diffraction, and $d$-wave spin splitting from photoemission spectroscopy. To fully verify this conecept, we predict, as a key experimental signature, a large nonlinear Edelstein response, which is localized at the surface, and follows the $d$-wave altermagnetic symmetry. These results are not only relevant for the metallic and room-temperature magnet \KVSO, but also for several other Lieb lattice systems. Our work expands the pool of techniques that can be used to detect altermagnetism emerging at the surfaces of antiferromagnets.
Published: 2026-08-04 12:16:06
Authors: Xiaoyan Feng, Zheng Gao, Tong Guan, Rui Bao, Bokang Zeng, Xiaoyu Li, Jiaojiao Jiang
Categories: cs.CV
Abstract:
Most in-generation diffusion watermarks embed patterns independent of the image that carries them, and attackers transplant the marks onto images the generator did not produce, resulting in forgery. Binding the mark to visual semantics prevents such transplantation, yet existing bindings anchor to a proxy image rather than the image they mark. Realizing visual-semantic binding inside generation faces two challenges. The mark derives from the image itself yet enters the sampling trajectory before that image exists, and may itself shift the semantics it binds. The binding also meets opposite sensitivity demands, breaking under semantic change while holding through common processing. We present IRIS, a training-free watermarking scheme that embeds an Intrinsic Ring Identifier from Semantics. IRIS reads a content code from the non-watermarked generated image, derives a one-time ring from the code and a secret key, returns to the final low-noise steps of the same trajectory and blends the ring in, after the semantics it binds are settled. To meet the opposite sensitivity demands, the code is read through a canonicalization shared between embedding and detection, holding through common distortions and mild regeneration while flipping under semantic change. Detection recomputes the ring from the query image and the key alone, and the mark therefore fails on a foreign or spliced image, with acceptance tracking semantic displacement. On three prompt datasets IRIS detects reliably and stays close to its same-seed non-watermarked counterpart, a fidelity prior in-generation marks do not reach. While forgeries transfer fixed-pattern marks and regeneration strips post-hoc marks, IRIS alone among the compared marks withstands both.
Published: 2026-08-04 12:15:17
Authors: Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi, Pekka Abrahamsson
Categories: cs.SE
Abstract:
Large Language Models are increasingly evaluated for code generation using test-based benchmarks. The validity of such evaluations depends on the reliability of their references and tests, while test-based correctness captures only part of the observable properties of generated code. We present CodeAssay, a taxonomy-first benchmark of 185 Python tasks across ten software-engineering categories. It combines audited ground truth, public tests for generation and repair, hidden tests for grading, mutation-based test-suite validation, and selected code-property measures. Regrading fixed model outputs after the audit changed 170 of 1,890 correctness labels (9.0%) and increased the measured best-to-worst model spread from 11.9 to 23.7 percentage points, although aggregate correctness remained nearly unchanged. The complete and hidden test suites achieved mutation scores of 82.6% and 74.8%, respectively. Across seven proprietary LLMs, standard-prompt correctness ranged from 77.3% to 98.9%, with significant differences in 12 of 21 model pairs. On the 120 tasks solved by all 14 model-prompt configurations, no model performed best across all selected code properties. A security-focused prompt produced no significant change in correctness or consistent reduction in the selected static-analysis findings, while increasing program length and cyclomatic complexity across all models. These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures. CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
Published: 2026-08-04 12:14:19
Authors: Pavithren V S Pakianathan, Rania Islambouli, Magenta Jade Shipsey, Laura Maaß, Jan Smeddinck
Categories: cs.HC
Abstract:
Dynamic consent can promise end users with greater control, but little is known about how older adults with chronic conditions navigate the tradeoff between control and burden in granular consent mechanisms in health data life-cycles. Using a two-stage design process we evaluated this tradeoff. An expert workshop (n=5) informed the design requirements for granular dynamic consent prototype. We evaluated single step vs multi-step granularity in dynamic consent using prototypes with cardiac patients (n=7) using a mixed-methods study. Quantitative measures showed no significant differences between low- and high-granularity consent screens in usability, workload, perceived information control or willingness to share data. However, qualitative findings revealed a control-burden paradox and trust-dependent engagement with granularity. Participants sought greater transparency and control over AI-mediated data processing. We contribute implications for designing granular consent in health data life-cycles.
Published: 2026-08-04 12:13:25
Authors: Gupta Lovi Raj, kaur Kamalpreet, Dama Sriram, Parali Prajithaa
Categories: cs.AI, cs.CY
Abstract:
Institutions increasingly rely on browser lockdown, webcam monitoring, and behavioral analytics to secure high-stakes digital assessments, yet these mechanisms are commonly designed and evaluated independently and often overlook learner accessibility. This paper introduces Behaviorally-Adaptive Visual Diversion (BAVD), a theoretical framework in which a synthetic, non-semantic visual field is composited with assessment content and adaptively modulated according to observed candidate behavior. The underlying assessment content is never altered; only its visual presentation is modified to reduce the usefulness of unauthorized screen capture or screen sharing while remaining minimally intrusive for legitimate candidates. The framework further incorporates an accessibility-aware attenuation mechanism that reduces or suppresses diversion intensity for candidates with approved visual-processing accommodations. We formulate the model using a coupled dynamical-systems representation comprising a Diversion Field Generator, Rendering Tensor, Behavior Tensor, Composite Integrity Functional, and Multi-dimensional Entropy Model, and establish theoretical properties for content fidelity, rendering stability, entropy boundedness, integrity tracking, and closed-loop adaptation stability. The framework explicitly states its threat model, identifies deployment assumptions and limitations, and discusses the trade-off between accessibility and capture resistance. This work provides a mathematically grounded foundation for behaviorally adaptive and accessibility-aware assessment delivery and offers a basis for future empirical validation in trusted digital assessment platforms.
Published: 2026-08-04 12:12:09
Authors: Shengyuan Zhang, Xinpeng Liu, Muchun Niu, Yulong Chen, Lizhuang Ma, Yue Gao, Cewu Lu, Yong-Lu Li
Categories: cs.RO
Abstract:
Replicating human behavior with physics simulation has been a long-expected goal in character animation. Existing efforts have achieved impressive performance in imitating a wide span of general motions. However, most existing efforts could still suffer from unnatural movements due to the lack of biomechanical and physiological priors. Given this, we project our sights to advances in behavioral energetics, which demonstrate how energy use shapes human movements. In contrast, current character controllers typically assume the character is equipped with infinite energy over time. Inspired by these, we propose to adopt fatigue as a proxy of the finite energy limit, inject it into general character animation, and thoroughly investigate how fatigue introduces new characteristics to physics-based character control. Leveraging the Three-Compartment Controller (3CC) model, we managed to obtain a policy for general motion imitation under different fatigue statuses. Furthermore, extensive analyses are conducted to demonstrate how fatigue could influence the naturalness, scalability, and robustness of character animation. Our code will be made public.
Published: 2026-08-03 15:50:33
Authors: Victor Ojewale, Ro Encarnación, Suresh Venkatasubramanian, Danaé Metaxa
Categories: cs.AI, cs.CY
Abstract:
Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms record satisfaction without task purpose. Together, they leave a critical gap in LLM evaluation: no existing infrastructure routinely links interaction trajectories to user-defined outcomes. We introduce MonitrLLM, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata.
To demonstrate the value of this approach, we conducted a two-week feasibility pilot with 26 college students using ChatGPT, collecting 206 evaluation reports with full conversation transcripts. The findings from our pilot demonstrate the value of connecting conversation trajectories with user-reported outcomes. For instance, despite reporting high average satisfaction (4.19/5) with their LLM interactions, participants also experience a substantial 23.1% failure rate on their goal tasks. We also find that multi-turn conversations are reported as failing at 2.5 times the rate of single-turn exchanges, a pattern that reframes extended interaction as a signal of difficulty rather than engagement. We conclude by discussing the value of incorporating direct user feedback with observational data for robust LLM evaluations, and the possibilities for infrastructure that enables this goal.