Published: 2026-04-07 10:25:23
Authors: Inge S. Helland
Categories: quant-ph
Abstract:
In several articles, this author has advocated an alternative approach towards quantum foundation based upon a set of postulates, and based upon the notions of theoretical variables and of accessible theoretical variables. It is shown in this article that this basis can be considerably simplified. In particular, the assumption that there exists an inaccessible variable $φ$ such that all the accessible ones can be seen as functions of $φ$, can be dropped. This assumption has been difficult to motivate in the previous articles. From this, I get a simple basis for the main Theorems.The essential assumption is that there in the given context exist two different maximal accessible variables, what Niels Bohr would have called two complementary variables. From this, the whole Hilbert space formalism may be derived. It is also discussed in some detail how this Hilbert space should be chosen. The resulting theory is a purely mathematical theory, but it leads to qunantum mechanics by letting the variables be physical variables. Other applications of the main theory are also considered. The mathematical proofs are mostly deferred to the Appendix.
Published: 2026-04-07 09:56:03
Authors: Wuyang Luan, Junhui Li, Weiguang Zhao, Wenjian Zhang, Tieru Wu, Rui Ma
Categories: cs.CV, cs.AI
Abstract:
Vision-Language-Action (VLA) models based on flow matching -- such as pi0, pi0.5, and SmolVLA -- achieve state-of-the-art generalist robotic manipulation, yet their iterative denoising, typically 10 ODE steps, introduces substantial latency: on a modern GPU, denoising alone accounts for 80% of end-to-end inference time. Naively reducing the step count is unreliable, degrading success on most tasks due to the velocity field being uncalibrated for single-step jumps. We present SnapFlow, a plug-and-play self-distillation method that compresses multi-step denoising into a single forward pass (1-NFE) for flow-matching VLAs. SnapFlow mixes standard flow-matching samples with consistency samples whose targets are two-step Euler shortcut velocities computed from the model's own marginal velocity predictions, avoiding the trajectory drift caused by conditional velocities, as we analyze theoretically. A zero-initialized target-time embedding lets the network switch between local velocity estimation and global one-step generation within a single architecture. SnapFlow requires no external teacher, no architecture changes, and trains in ~12h on a single GPU. We validate on two VLA architectures spanning a 6x parameter range, with identical hyperparameters: on pi0.5 (3B) across four LIBERO suites (40 tasks, 400 episodes), SnapFlow achieves 98.75% average success -- matching the 10-step teacher at 97.75% and slightly exceeding it -- with 9.6x denoising speedup and end-to-end latency reduced from 274ms to 83ms; on SmolVLA (500M), it reduces MSE by 8.3% with 3.56x end-to-end acceleration. An action-step sweep on long-horizon tasks reveals that SnapFlow maintains its advantage across execution horizons, achieving 93% at n_act=5 where the baseline reaches only 90%. SnapFlow is orthogonal to layer-distillation and token-pruning approaches, enabling compositional speedups.
Published: 2026-04-07 09:41:11
Authors: I-han Lai, Stefan Wager
Categories: stat.ME
Abstract:
We develop methods for estimating how infinitesimal policy changes affect long-term outcomes in dynamic systems. We show that dynamic marginal policy effects (MPEs) can be identified via tractable reduced-form expressions, and can be estimated under a general sequential unconfoundedness assumption. We also propose a doubly robust estimator for dynamic MPEs. Our approach does not require observing full dynamic state information (as is typically assumed for off-policy evaluation in Markov decision processes), and does not incur an exponential curse of horizon (as is typical in non-Markovian off-policy evaluation). We demonstrate practicality and robustness of our approach in a number of simulations, including one motivated by a dynamic pricing application where people use past prices to form a reference level for current prices.
Published: 2026-04-07 09:36:24
Authors: Manish Kumar, Anton Frederik Thielmann, Christoph Weisser, Benjamin Säfken
Categories: cs.LG
Abstract:
Numerical preprocessing remains an important component of tabular deep learning, where the representation of continuous features can strongly affect downstream performance. Although its importance is well established for classical statistical and machine learning models, the role of explicit numerical preprocessing in tabular deep learning remains less well understood. In this work, we study this question with a focus on spline-based numerical encodings. We investigate three spline families for encoding numerical features, namely B-splines, M-splines, and integrated splines (I-splines), under uniform, quantile-based, target-aware, and learnable-knot placement. For the learnable-knot variants, we use a differentiable knot parameterization that enables stable end-to-end optimization of knot locations jointly with the backbone. We evaluate these encodings on a diverse collection of public regression and classification datasets using MLP, ResNet, and FT-Transformer backbones, and compare them against common numerical preprocessing baselines. Our results show that the effect of numerical encodings depends strongly on the task, output size, and backbone. For classification, piecewise-linear encoding (PLE) is the most robust choice overall, while spline-based encodings remain competitive. For regression, no single encoding dominates uniformly. Instead, performance depends on the spline family, knot-placement strategy, and output size, with larger gains typically observed for MLP and ResNet than for FT-Transformer. We further find that learnable-knot variants can be optimized stably under the proposed parameterization, but may substantially increase training cost, especially for M-spline and I-spline expansions. Overall, the results show that numerical encodings should be assessed not only in terms of predictive performance, but also in terms of computational overhead.
Published: 2026-04-07 09:35:03
Authors: Amir Konigsberg
Categories: cs.AI, cs.HC
Abstract:
In 1950, Alan Turing proposed replacing the question "Can machines think?" with a behavioral test: if a machine's outputs are indistinguishable from those of a thinking being, the question of whether it truly thinks can be set aside. This paper argues that Turing's move was not only a pragmatic simplification but also an epistemological commitment, a decision about what kind of evidence counts as relevant to intelligence attribution, and that this commitment has quietly constrained AI research for seven decades. We trace how Turing's behavioral epistemology became embedded in the field's evaluative infrastructure, rendering unaskable a class of questions about process, mechanism, and internal organization that cognitive psychology, neuroscience, and related disciplines learned to ask. We draw a structural parallel to the behaviorist-to-cognitivist transition in psychology: just as psychology's commitment to studying only observable behavior prevented it from asking productive questions about internal mental processes until that commitment was abandoned, AI's commitment to behavioral evaluation prevents it from distinguishing between systems that achieve identical outputs through fundamentally different computational processes, a distinction on which intelligence attribution depends. We argue that the field requires an epistemological transition comparable to the cognitive revolution: not an abandonment of behavioral evidence, but a recognition that behavioral evidence alone is insufficient for the construct claims the field wishes to make. We articulate what a post-behaviorist epistemology for AI would involve and identify the specific questions it would make askable that the field currently has no way to ask.
Published: 2026-04-07 09:27:01
Authors: Xinran Wang, Yuxuan Zhang, Xiao Zhang, Haolong Yan, Muxi Diao, Songyu Xu, Zhonghao Yan, Hongbing Li, Kongming Liang, Zhanyu Ma
Categories: cs.CV, cs.CL, cs.MM
Abstract:
Accurately detecting and localizing hallucinations is a critical task for ensuring high reliability of image captions. In the era of Multimodal Large Language Models (MLLMs), captions have evolved from brief sentences into comprehensive narratives, often spanning hundreds of words. This shift exponentially increases the challenge: models must now pinpoint specific erroneous spans or words within extensive contexts, rather than merely flag response-level inconsistencies. However, existing benchmarks lack the fine granularity and domain diversity required to evaluate this capability. To bridge this gap, we introduce DetailVerifyBench, a rigorous benchmark comprising 1,000 high-quality images across five distinct domains. With an average caption length of over 200 words and dense, token-level annotations of multiple hallucination types, it stands as the most challenging benchmark for precise hallucination localization in the field of long image captioning to date. Our benchmark is available at https://zyx-hhnkh.github.io/DetailVerifyBench/.
Published: 2026-04-07 09:20:55
Authors: Chenyu Xue, Yiran Liu, Mian Zhou, Jionglong Su, Zhixiang Lu
Categories: cs.CV, cs.AI
Abstract:
Medical image segmentation driven by free-text clinical instructions is a critical frontier in computer-aided diagnosis. However, existing multimodal and foundation models struggle with the semantic ambiguity of clinical reports and fail to disambiguate complex anatomical overlaps in low-contrast scans. Furthermore, fully fine-tuning these massive architectures on limited medical datasets invariably leads to severe overfitting. To address these challenges, we propose a novel Semantic-Topological Graph Reasoning (STGR) framework for language-guided pulmonary screening. Our approach elegantly synergizes the reasoning capabilities of large language models (LLaMA-3-V) with the zero-shot delineation of vision foundation models (MedSAM). Specifically, we introduce a Text-to-Vision Intent Distillation (TVID) module to extract precise diagnostic guidance. To resolve anatomical ambiguity, we formulate mask selection as a dynamic graph reasoning problem, where candidate lesions are modeled as nodes and edges capture spatial and semantic affinities. To ensure deployment feasibility, we introduce a Selective Asymmetric Fine-Tuning (SAFT) strategy that updates less than 1% of the parameters. Rigorous 5-fold cross-validation on the LIDC-IDRI and LNDb datasets demonstrates that our framework establishes a new state-of-the-art. Notably, it achieves an 81.5% Dice Similarity Coefficient (DSC) on LIDC-IDRI, outperforming leading LLM-based tools like LISA by over 5%. Crucially, our SAFT strategy acts as a powerful regularizer, yielding exceptional cross-fold stability (0.6% DSC variance) and paving the way for robust, context-aware clinical deployment.
Published: 2026-04-07 09:07:07
Authors: Spiros Cotsakis
Categories: gr-qc, math.DS
Abstract:
We develop a bifurcation-theoretic description of Friedmann--Robertson--Walker cosmologies with a scalar field $φ$, a barotropic fluid of index $γ$, and spatial curvature. For the strict exponential potential $V(φ)=V_{0}e^{λφ}$, with $a=\sqrt{3/2}\,λ$, the local phase portrait is organised by five loci in the $(γ,a)$-plane: $|a|=3$, $a^{2}=3$, $a^{2}=9γ/2$, $γ=2/3$, and $γ=2$. Near these loci we compute translated jets, centre(-like) reductions, and normal forms governing persistence and transitions. For the quadratic potential $V(φ)=(1/2)m^{2}φ^{2}$, the effective slope $λ$ is dynamical. Using the bounded variable $ζ=\arctanλ$, we obtain a regular autonomous $4$-dimensional system in $(X,Y,Ω_{k},ζ)$, where $Ω_{k}$ is the curvature variable. This reveals invariant gates, robust equilibrium continua, and vertical $γ$-thresholds for loss and recovery of normal hyperbolicity. We then construct an explicit stratification for the exponential class and a pull-back stratification for the massive case, together with the corresponding physical path maps into unfolding space. The resulting framework also organises slow-roll, ultra slow-roll, and oscillatory regimes.
Published: 2026-04-07 08:56:53
Authors: Nikolaos D. Tantaroudas, Andrew J. McCracken, Ilias Karachalios, Evangelos Papatheou
Categories: cs.CE, cs.AI, cs.CL, cs.CV, cs.ET
Abstract:
Video conferencing has become central to professional collaboration, yet most platforms offer limited support for deaf, hard-of-hearing, and multilingual users. The World Health Organisation estimates that over 430 million people worldwide require rehabilitation for disabling hearing loss, a figure projected to exceed 700 million by 2050. Conventional accessibility measures remain constrained by high costs, limited availability, and logistical barriers, while Extended Reality (XR) technologies open new possibilities for immersive and inclusive communication. This paper presents INTERACT (Inclusive Networking for Translation and Embodied Real-Time Augmented Communication Tool), an AI-driven XR platform that integrates real-time speech-to-text conversion, International Sign Language (ISL) rendering through 3D avatars, multilingual translation, and emotion recognition within an immersive virtual environment. Built on the CORTEX2 framework and deployed on Meta Quest 3 headsets, INTERACT combines Whisper for speech recognition, NLLB for multilingual translation, RoBERTa for emotion classification, and Google MediaPipe for gesture extraction. Pilot evaluations were conducted in two phases, first with technical experts from academia and industry, and subsequently with members of the deaf community. The trials reported 92% user satisfaction, transcription accuracy above 85%, and 90% emotion-detection precision, with a mean overall experience rating of 4.6 out of 5.0 and 90% of participants willing to take part in further testing. The results highlight strong potential for advancing accessibility across educational, cultural, and professional settings. An extended version of this work, including full pilot data and implementation details, has been published as an Open Research Europe article [Tantaroudas et al., 2026a].
Published: 2026-04-07 08:52:41
Authors: Changlong Zhu, Claudiu Genes, Birgit Stiller
Categories: quant-ph
Abstract:
Quantum memories for optical states are essential resources for quantum communication and information processing. We propose a quantum memory protocol based on coherent photon-phonon transduction in a Brillouin-active optical waveguide supporting traveling acoustic modes. A pulsed pump drives an effective beam-splitter interaction between optical and acoustic fields, enabling the mapping of a propagating optical quantum state onto a traveling phononic excitation and its subsequent retrieval on demand. Using a continuum optoacoustic model, we show that the protocol enables broadband quantum state storage in a distributed medium without relying on discrete cavity modes. Analytical and numerical results demonstrate high-fidelity storage and retrieval of squeezed and entangled states under experimentally realistic parameters. The memory bandwidth is set by the Brillouin interaction and can reach hundreds of MHz. Our results identify continuum Brillouin optomechanical systems as a scalable platform for broadband quantum memories and multimode quantum signal processing.
Published: 2026-04-07 08:44:45
Authors: Deepa Madivalar, Vishwanath Kadaba Puttanna, A Kandasamy
Categories: physics.flu-dyn
Abstract:
This study numerically investigates heat transfer enhancement in laminar, incompressible viscoplastic nanofluid flow through the entrance region of a circular cylinder with a uniformly heated wall, including the effects of both, non-aggregation and aggregation of nanoparticles. Nanofluid properties are modeled using Brinkman and Maxwell models in the case of non-aggregation, and Krieger-Dougherty, Maxwell-Bruggeman models in the case of aggregation, while the viscoplastic behavior is described by the Bingham-Papanastasiou model. The governing boundary layer equations are solved using a finite-difference method. The effects of yield stress and nanoparticle volume fraction (up to 5%) on friction, pressure drop, and Nusselt number are analyzed, and performance evaluation criteria are evaluated to identify the optimal volume fraction for maximum efficiency.
Published: 2026-04-07 08:43:34
Authors: Yujie Yao, Yuhaohang He, Junjie Huang, Zhou Liu, Jiangzhao Li, Yan Qiao, Wen Xiao, Yunsen Liang, Xiaofan Li
Categories: cs.CV
Abstract:
Annotation-free skin lesion segmentation is attractive for low-resource dermoscopic deployment. However, its performance remains constrained by three coupled challenges: noisy pseudo-label supervision, unstable transfer under limited target-domain data, and boundary probability under-confidence. Most existing annotation-free methods primarily focus on pseudo-label denoising. In contrast, the effect of compressed boundary probabilities on final mask quality has received less explicit attention, although it directly affects contour completeness and cannot be adequately corrected by global threshold adjustment alone. To address this issue, we propose BPC-Net, a boundary probability calibration framework for annotation-free skin lesion segmentation. The core of the framework is Gaussian Probability Smoothing (GPS), which performs localized probability-space calibration before thresholding to recover under-confident lesion boundaries without inducing indiscriminate foreground expansion. To support this calibration under noisy pseudo-supervision and cross-domain transfer, we further incorporate two auxiliary designs: a feature-decoupled decoder that separately handles context suppression, detail recovery, and boundary refinement, and an interaction-branch adaptation strategy that updates only the pseudo-label interaction branch while preserving the deployed image-only segmentation path. Under a strictly annotation-free protocol, no manual masks are used during training or target-domain adaptation, and validation labels, when available, are used only for final operating-point selection. Experiments on ISIC-2017, ISIC-2018, and PH2 show that the proposed framework achieves state-of-the-art performance among published unsupervised methods, reaching a macro-average Dice coefficient and Jaccard index of 85.80\% and 76.97\%, respectively, while approaching supervised reference performance on PH2.
Published: 2026-04-07 08:43:30
Authors: Xin Sun, Di Wu, Sijing Qin, Isao Echizen, Abdallah El Ali, Saku Sugawara
Categories: cs.AI, cs.CL
Abstract:
Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge). This work challenges its reliability by showing that trust judgments by LLMs are biased by disclosed source labels. Using a counterfactual design, we find that both humans and LLM judges assign higher trust to information labeled as human-authored than to the same content labeled as AI-generated. Eye-tracking data reveal that humans rely heavily on source labels as heuristic cues for judgments. We analyze LLM internal states during judgment. Across label conditions, models allocate denser attention to the label region than the content region, and this label dominance is stronger under Human labels than AI labels, consistent with the human gaze patterns. Besides, decision uncertainty measured by logits is higher under AI labels than Human labels. These results indicate that the source label is a salient heuristic cue for both humans and LLMs. It raises validity concerns for label-sensitive LLM-as-a-Judge evaluation, and we cautiously raise that aligning models with human preferences may propagate human heuristic reliance into models, motivating debiased evaluation and alignment.
Published: 2026-04-07 08:41:00
Authors: Aditya Jha, Masoodah Gunny, Joshua D Mcgraw, Yacine Amarouchene, Thomas Salez
Categories: cond-mat.soft, cond-mat.stat-mech, physics.class-ph, physics.flu-dyn
Abstract:
Diffusion of a solute along a channel is enhanced by hydrodynamic flow, a phenomenon known as Taylor dispersion. In microfluidic applications, the compliance of the channel boundaries modifies the hydrodynamic flow and thus solutal transport. Here, we develop the theory of solutal dispersion in a soft, axisymmetric channel where the channel walls respond to the hydrodynamic pressure through a Winkler response. By deriving the modified macro-transport equation for the solutal concentration dynamics based on multiple-time-scale analysis, we explore the influence of softness on solutal transport for steady and pulsatile configurations. Our main finding is that softness enhances the effective advection velocity and dispersion coefficient, which might have practical implication in biology and microfluidic technology.
Published: 2026-04-07 08:29:41
Authors: Zhe Zhao, Haibin Wen, Jiaming Ma, Jiachang Zhan, Tianyi Xu, Ye Wei, Qingfu Zhang
Categories: cs.AI, math.OC
Abstract:
An important recurring pattern in scientific breakthroughs is a two-stage process: an initial phase of undirected experimentation that yields an unexpected finding, followed by a retrospective phase that explains why the finding works and situates it within existing theory. We present ResearchEVO, an end-to-end framework that computationally instantiates this discover-then-explain paradigm. The Evolution Phase employs LLM-guided bi-dimensional co-evolution -- simultaneously optimizing both algorithmic logic and overall architecture -- to search the space of code implementations purely by fitness, without requiring any understanding of the solutions it produces. The Writing Phase then takes the best-performing algorithm and autonomously generates a complete, publication-ready research paper through sentence-level retrieval-augmented generation with explicit anti-hallucination verification and automated experiment design. To our knowledge, ResearchEVO is the first system to cover this full pipeline end to end: no prior work jointly performs principled algorithm evolution and literature-grounded scientific documentation. We validate the framework on two cross-disciplinary scientific problems -- Quantum Error Correction using real Google quantum hardware data, and Physics-Informed Neural Networks -- where the Evolution Phase discovered human-interpretable algorithmic mechanisms that had not been previously proposed in the respective domain literatures. In both cases, the Writing Phase autonomously produced compilable LaTeX manuscripts that correctly grounded these blind discoveries in existing theory via RAG, with zero fabricated citations.
Published: 2026-04-07 08:17:12
Authors: K. Abe, S. Abe, J. Abhir, A. Abhishek, V. A. Acciari, F. Acero, A. Aguasca-Cabot, I. Agudo, C. Alispach, D. Ambrosino, F. Ambrosino, T. Aniello, S. Ansoldi, L. A. Antonelli, C. Aramo, A. Arbet-Engels, C. Arcaro, T. T. H. Arnesen, P. Aubert, A. Babić, C. Bakshi, A. Baktash, M. Balbo, A. Bamba, A. Baquero Larriva, U. Barres de Almeida, J. A. Barrio, L. Barrios Jiménez, I. Batkovic, J. Baxter, J. Becerra González, W. Bednarek, E. Bernardini, J. Bernete, A. Berti, C. Bigongiari, A. Biland, E. Bissaldi, O. Blanch, G. Bonnoli, P. Bordas, Ž. Bošnjak, A. Briscioli, E. Bronzini, G. Brunelli, J. Buces, A. Bulgarelli, I. Burelli, L. Burmistrov, A. Campoy-Ordaz, M. Cardillo, S. Caroff, A. Carosi, R. Carosi, R. Carraro, M. Carretero-Castrillo, F. Cassol, A. J. Castro-Tirado, D. Cerasole, G. Ceribella, A. Cerviño Cortínez, Y. Chai, G. Chon, L. Chytka, G. M. Cicciari, A. Cifuentes Santos, J. L. Contreras, J. Cortina, S. Covino, H. Costantini, M. Croisonnier, M. Dalchenko, G. D'Amico, P. Da Vela, F. Dazzi, A. De Angelis, M. de Bony de Lavergne, R. Del Burgo, M. Delfino, C. Delgado, J. Delgado Mengual, D. della Volpe, B. De Lotto, L. Del Peral, R. de Menezes, G. De Palma, V. de Souza, C. Díaz, L. Di Bella, A. Di Piano, F. Di Pierro, R. Di Tria, L. Di Venere, A. Dinesh, D. Dominis Prester, A. Donini, D. Dorner, M. Doro, L. Eisenberger, D. Elsässer, G. Emery, L. Feligioni, J. Escudero, L. Fariña, F. Ferrarotto, A. Fiasson, L. Foffano, L. Font, F. Frías García-Lago, S. Fröse, Y. Fukazawa, S. Gallozzi, R. Garcia López, S. Garcia Soto, C. Gasbarra, D. Gasparrini, S. Gasparyan, M. Gaug, J. Giesbrecht Paiva, N. Giglietto, F. Giordano, P. Gliwny, N. Godinovic, T. Gradetzke, R. Grau, J. Green, G. Grolleron, S. Gunji, P. Günther, J. Hackfeld, D. Hadasch, A. Hahn, G. Harutyunyan, M. Hashizume, T. Hassan, K. Hayashi, L. Heckmann, M. Heller, J. Herrera Llorente, N. Hiroshima, D. Hoffmann, D. Horns, J. Houles, D. Hrupec, R. Imazawa, T. Inada, S. Inoue, K. Ioka, M. Iori, D. Israyelyan, T. Itokawa, A. Iuliano, J. Jahanvi, I. Jimenez Martinez, J. Jimenez Quiles, I. Jorge Rodrigo, J. Jormanainen, J. Jurysek, M. Kagaya, S. Kankkunen, V. Karas, H. Katagiri, T. Kayanoki, D. Kerszberg, T. Kiyomoto, G. W. Kluge, Y. Kobayashi, K. Kohri, J. Konrad, P. Kornecki, P. M. Kouch, G. Koziol, H. Kubo, J. Kushida, B. Lacave, M. Lainez, A. Lamastra, L. Lemoigne, E. Lindfors, M. Linhoff, S. Lombardi, F. Longo, R. López-Coto, M. López-Moya, A. López-Oramas, S. Loporchio, J. Lozano Bahilo, F. Lucarelli, H. Luciani, L. Lulić, P. L. Luque-Escamilla, E. Lyard, P. Majumdar, M. Makariev, M. Mallamaci, D. Mandat, G. Maneva, M. Manganaro, S. Mangano, K. Mannheim, S. Marchesi, F. Marini, M. Mariotti, P. Marquez, G. Marsella, J. Martí, D. Martin, O. Martinez, G. Martínez, M. Martínez, M. Massa, P. Maruševec, D. Mazin, S. Menchiari, J. Méndez-Gallego, S. Menon, E. Mestre Guillen, D. Miceli, T. Miener, J. M. Miranda, R. Mirzoyan, M. Molero Gonzalez, E. Molina, H. A. Mondal, T. Montaruli, A. Moralejo, A. Morselli, V. Moya, A. L. Müller, H. Muraishi, S. Nagataki, T. Nakamori, C. Nanci, A. Negro, A. Neronov, V. Neustroev, D. Nieto Castaño, M. Nievas Rosillo, C. Nigro, L. Nikolic, K. Noda, V. Novotny, S. Nozaki, M. Ohishi, A. Okumura, R. Orito, L. Orsini, J. Otero-Santos, P. Ottanelli, S. Paiano, M. Palatiello, G. Panebianco, D. Paneque, R. Paoletti, J. M. Paredes, M. Pech, M. Pecimotika, M. Peresano, F. Perrotta, M. Persic, F. Pfeifle, M. Pihet, G. Pirola, C. Plard, F. Podobnik, M. Polo, C. Pozo-Gonzaléz, P. G. Prada Moroni, E. Prandini, S. Rainò, R. Rando, W. Rhode, M. Ribó, J. Rico, G. Rodriguez Fer dez, M. D. Rodríguez Frías, A. Roy, A. Ruina, E. Ruiz-Velasco, N. Sahakyan, T. Saito, S. Sakurai, D. A. Sanchez, H. Sano, E. Santos Moura, T. Šarić, Y. Sato, F. G. Saturni, V. Savchenko, F. Schiavone, K. Schmitz, F. Schmuckermaier, F. Schussler, T. Schweizer, M. Seglar Arroyo, A. Sciaccaluga, G. Silvestri, A. Simongini, J. Sitarek, V. Sliusar, I. Sofia, D. Sobczynska, A. Stamerra, J. Strišković, D. Strom, M. Strzys, Y. Suda, A. Sunny, H. Tajima, M. Takahashi, R. Takeishi, S. J. Tanaka, D. Tateishi, T. Tavernier, P. Temnikov, Y. Terada, K. Terauchi, T. Terzic, M. Teshima, M. Tluczykont, T. Tomura, D. F. Torres, F. Tramonti, P. Travnicek, G. Tripodo, A. Tutone, S. Ubach, M. Vacula, M. Vázquez Acosta, S. Ventura, G. Verna, I. Viale, A. Viana, A. Vigliano, C. F. Vigorito, E. Visentin, V. Vitale, G. Voutsinas, I. Vovk, T. Vuillaume, R. Walter, C. Walther, F. Wersig, M. Will, T. Yamamoto, R. Yamazaki, Y. Yao, P. K. H. Yeung, T. Yoshida, T. Yoshikoshi, W. Zhang, N. Zywucka, F. D'Ammando, D. Linder, F. Wierda
Categories: astro-ph.HE
Abstract:
Extremely high-peaked BL Lac objects - also named extreme blazars - are among the most energetic and persistent extragalactic accelerators in the Universe, defined by a synchrotron emission peaking above $10^{17}$ Hz in X-rays. Such emission is then reprocessed and produces radiation extending deeply into very-high-energy (VHE, energy E>100 GeV) gamma rays. Observations in this energy band - optimally investigated by the Imaging Air-Shower Cherenkov telescopes - are crucial for probing the physical processes that drive their extreme behavior. This study extends our investigation of extreme blazars in the VHE gamma-ray range, providing a second new mini-catalog of sources observed by the MAGIC telescopes. We report on the monitoring of seven targets between 2017 and 2025, including four newly observed sources and three that have been part of long-term observation campaigns, for a total of approximately 338 hours of observations. The analysis of MAGIC data reveals two new VHE detections of extreme blazars, along with three additional sources showing hints of VHE emission. Joint observations of MAGIC and the first Large-Sized Telescope (LST-1) also confirmed a new VHE extreme blazar. Our results are complemented by simultaneous multiwavelength observations in other energy bands, including optical-UV, X-rays, and high-energy gamma rays (100 MeV
Published: 2026-04-07 08:05:12
Authors: Luc Pommeret, Thibault Wagret, Jules Deret
Categories: cs.CL
Abstract:
We describe THIVLVC, a two-stage system for the EvaLatin 2026 Dependency Parsing task. Given a Latin sentence, we retrieve structurally similar entries from the CIRCSE treebank using sentence length and POS n-gram similarity, then prompt a large language model to refine the baseline parse from UDPipe using the retrieved examples and UD annotation guidelines. We submit two configurations: one without retrieval and one with retrieval (RAG). On poetry (Seneca), THIVLVC improves CLAS by +17 points over the UDPipe baseline; on prose (Thomas Aquinas), the gain is +1.5 CLAS. A double-blind error analysis of 300 divergences between our system and the gold standard reveals that, among unanimous annotator decisions, 53.3% favour THIVLVC, showing annotation inconsistencies both within and across treebanks.
Published: 2026-04-07 07:58:55
Authors: Xuan Dong, Huanyang Zheng, Tianhao Niu, Zhe Han, Pengzhan Li, Bofei Liu, Zhengyang Liu, Guancheng Li, Qingfu Zhu, Wanxiang Che
Categories: cs.CL
Abstract:
Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible conclusions. This joint capability is not systematically assessed in existing benchmarks, which largely under-evaluate proactive search, multi-evidence integration and sustained evidence use over time. In this work, we introduce EpiBench, an episodic multi-turn multimodal benchmark that instantiates short research workflows. Given a research task, agents must navigate across papers over multiple turns, align evidence from figures and tables, and use the accumulated evidence in the memory to answer objective questions that require cross paper comparisons and multi-figure integration. EpiBench introduces a process-level evaluation framework for fine-grained testing and diagnosis of research agents. Our experiments show that even the leading model achieves an accuracy of only 29.23% on the hard split, indicating substantial room for improvement in multi-turn, multi-evidence research workflows, providing an evaluation platform for verifiable and reproducible research agents.
Published: 2026-04-07 07:42:42
Authors: Zhiyu Li, Xinwen Yue, Shenghui Zhao, Jing Wang
Categories: eess.AS
Abstract:
We propose a multimodal deep learning model for VR auralization that generates spatial room impulse responses (SRIRs) in real time to reconstruct scene-specific auditory perception. Employing SRIRs as the output reduces computational complexity and facilitates integration with personalized head-related transfer functions. The model takes two modalities as input: scene information and waveforms, where the waveform corresponds to the low-order reflections (LoR). LoR can be efficiently computed using geometrical acoustics (GA) but remains difficult for deep learning models to predict accurately. Scene geometry, acoustic properties, source coordinates, and listener coordinates are first used to compute LoR in real time via GA, and both LoR and these features are subsequently provided as inputs to the model. A new dataset was constructed, consisting of multiple scenes and their corresponding SRIRs. The dataset exhibits greater diversity. Experimental results demonstrate the superior performance of the proposed model.
Published: 2026-04-07 07:19:42
Authors: Hongcheng Liu, Yuhao Wang, Zhe Chen, Pingjie Wang, Zhiyuan Zhu, Yixuan Hou, Yanfeng Wang, Yu Wang
Categories: cs.CL
Abstract:
Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this aspect has been largely overlooked. To bridge this gap, we formalize the challenge as a cross-modal coreference problem, where a model must localize a referent in a source modality and re-identify it in a target modality. Building on this paradigm, we introduce CrossOmni, a dataset comprising nine tasks equipped with human-designed reasoning rationales to evaluate and enhance this capability. Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference, which we attribute to the absence of coreference-aware thinking patterns. To address this, we enhance cross-modal alignment via two strategies: a training-free In-Context Learning method and a training-based SFT+GRPO framework designed to induce such thinking patterns. Both approaches yield substantial performance gains and generalize effectively to collaborative reasoning tasks. Overall, our findings highlight cross-modal coreference as a crucial missing piece for advancing robust omni-modal reasoning.
Published: 2026-04-07 07:18:58
Authors: Seiki Saito, Keisuke Takeuchi, Hiroaki Nakamura, Yasuhiro Oda, Kazuo Hoshino, Yuki Homma, Shohei Yamoto, Yuki Uchida
Categories: physics.plasm-ph, cond-mat.mtrl-sci
Abstract:
Understanding the long-term transport of hydrogen isotopes in plasma-facing materials, such as tungsten, is critical for the steady-state operation of magnetic confinement fusion reactors. However, dynamically updating the transition parameters for kinetic Monte Carlo (kMC) simulations as the atomic structure evolves under continuous plasma irradiation remains a severe computational bottleneck. Conventionally, calculating these migration barriers requires the iterative and computationally expensive Nudged Elastic Band (NEB) method. To overcome this limitation, this article presents a highly efficient surrogate model for predicting migration barriers using a three-dimensional Convolutional Neural Network (3D-CNN), establishing the final component necessary to realize on-the-fly molecular dynamics (MD) and kMC hybrid simulations. The proposed deep learning model takes a two-channel volumetric input, the local three-dimensional potential energy distribution and the voxelized spatial coordinates of the initial and final trapping sites, to directly output the migration barrier as a scalar value. Trained on a comprehensive dataset of tungsten-hydrogen configurations evaluated using the Embedded Atom Method (EAM) potential, the model demonstrated robust predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.124 eV and a high coefficient of determination of 0.890. Furthermore, utilizing GPU acceleration, the inference time is reduced to approximately 2.7 milliseconds per barrier, achieving a speed-up ratio of over 23,000 compared to conventional NEB calculations. This extraordinary acceleration effectively resolves the computational barrier of transition rate evaluations, paving the way for large-scale, dynamic modeling of plasma-wall interactions.
Published: 2026-04-07 07:10:24
Authors: Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibang Jiang, Yao Zhu
Categories: cs.AI
Abstract:
The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow range of task formulations and language support, constraining their applicability to diverse diagram types. In this work, we propose OmniDiagram, a unified framework that incorporates diverse diagram code languages and task definitions. To address the challenge of aligning code logic with visual fidelity in Reinforcement Learning (RL), we introduce a novel visual feedback strategy named Visual Interrogation Verifies All (\textsc{Viva}). Unlike brittle syntax-based rules or pixel-level matching, \textsc{Viva} rewards the visual structure of rendered diagrams through a generative approach. Specifically, \textsc{Viva} actively generates targeted visual inquiries to scrutinize diagram visual fidelity and provides fine-grained feedback for optimization. This mechanism facilitates a self-evolving training process, effectively obviating the need for manually annotated ground truth code. Furthermore, we construct M3$^2$Diagram, the first large-scale diagram code generation dataset, containing over 196k high-quality instances. Experimental results confirm that the combination of SFT and our \textsc{Viva}-based RL allows OmniDiagram to establish a new state-of-the-art (SOTA) across diagram code generation benchmarks.
Published: 2026-04-07 07:10:13
Authors: Violaine Courrier, Christophe Biernacki
Categories: stat.ME
Abstract:
Clustering is viewed as an unsupervised technique, but in practice it requires guidance to uncover meaningful structures. We formalize this with guided clustering, a paradigm that uses a guiding variable to steer the discovery process, and introduce the Guided Clustering Variational Autoencoder (GCVAE) as its deep generative realization. GCVAE learns a latent space structured as a Gaussian Mixture Model by optimizing a variational objective that forces the representation to be maximally informative about the guiding variable. This framework allows the resulting clustering to be reoriented by changing the guiding variable, yielding clusters that are meaningful for the specified context. Experiments on public (MNIST-SVHN) and proprietary connected health devices data demonstrate GCVAE's ability to discover coherent and task-relevant clusters in complex settings.
Published: 2026-04-07 07:02:36
Authors: Isabella Masina, Mariano Quiros
Categories: hep-ph, hep-th
Abstract:
An interesting framework to achieve gauge coupling unification consists in adding to the Standard Model Lagrangian non-renormalizable operators of $d \geq 5$, which affect the kinetic term of gauge fields. We first review the phenomenology related to this framework in the context of $SU(5)$, identifying which are the most interesting representations for the sake of achieving coupling unification. Secondly, we point out that in the case of a dynamical breaking pattern, it is possible to relate gauge coupling unification with the doublet-triplet splitting problem. We show that condensates of fermions in the $5$ representation do not lead to viable models because of proton decay constraints. At difference, we point out that successful models can be obtained by considering condensates of fermions in the $10$, as well as in the $24$ representations.
Published: 2026-04-07 06:59:39
Authors: Yunmei Zhang, Jun Zhan, Ping Wu, Yun-Peng Huang, Qixiao Yuan, Hongyu Li, Zhuying Wang, Wanru Ma, Shuikang Yu, Kunming Zhang, Wanlin Cheng, Deshu Chen, Minrui Chen, Tao Wu, Ziji Xiang, Xianxin Wu, Zhenyu Wang, Xianhui Chen
Categories: cond-mat.supr-con
Abstract:
Kagome superconductor AV$_3$Sb$_5$ (A stands for K, Rb, and Cs) hosts a wealth of intertwined electronic orders driven by geometric frustration and electron correlations. Among them, the breaking of rotational and/or time-reversal symmetry, observed within the triple-$Q$ charge density wave (CDW) phase yet exhibiting a more complex temperature dependence, remains a central puzzle. Here, by using scanning tunneling microscopy to study the electronic structures of CsV$_3$Sb$_5$ as a function of temperature and Ti doping, we disentangle the interrelation between two distinct nematic order parameters, one associated with the CDW and the other manifested as $C_2$ distortion of the V-$d_{x^{2}-y^{2}}$ Fermi pockets without breaking transition symmetry. The latter persists to high doping levels and high temperatures where the long-range CDW is fully suppressed. Moreover, its nematic director is oriented in a lattice direction distinct from that of the CDW-induced nematicity at intermediate doping, and eventually aligns with the strong nematic CDW order in the pristine compound where the quasiparticles of vanadium orbitals become coherent below a lower characteristic temperature. These observations, combined with Ginzburg-Landau analysis, reveal a rich interplay between two nematic orders that can be assigned to distinct kagome-lattice orbitals. Our results shed new light on the enigmatic intertwined orders in this family and establish a rare material platform in which dual nematic orders coexist and couple to give rise to unusual correlated phenomena.
Published: 2026-04-07 06:27:08
Authors: Ting Wu, Jin-Zhong Liu, Senyu Qi, Zhi-Xiang Zhang, Hubiao Niu, Ali Esamdin, Wei-Min Gu
Categories: astro-ph.SR
Abstract:
Contact binaries (CBs) serve as fundamental laboratories for studying complex stellar interactions, including mass transfer, tidal effects, and angular momentum loss. In this work, we search for CB with high-precision light curves from the Transiting Exoplanet Survey Satellite (TESS) and large radial-velocity variation from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). We derive a sample of 1,281 CB candidates, among which 266 are newly reported. Our sample with both high-precision photometry and medium-resolution spectra may provide new constraints on the physical scales, luminosity calibration, and population distribution of CBs, offering valuable insights into their evolutionary role within the stellar population.
Published: 2026-04-07 06:24:41
Authors: Li Kang, Yutao Fan, Rui Li, Heng Zhou, Yiran Qin, Zhemeng Zhang, Songtao Huang, Xiufeng Song, Zaibin Zhang, Bruno N. Y. Chen, Zhenfei Yin, Dongzhan Zhou, Wangmeng Zuo, Lei Bai
Categories: cs.RO, cs.CV
Abstract:
Multi-agent embodied systems hold promise for complex collaborative manipulation, yet face critical challenges in spatial coordination, temporal reasoning, and shared workspace awareness. Inspired by human collaboration where cognitive planning occurs separately from physical execution, we introduce the concept of compositional environment -- a synergistic integration of real-world and simulation components that enables multiple robotic agents to perceive intentions and operate within a unified decision-making space. Building on this concept, we present CoEnv, a framework that leverages simulation for safe strategy exploration while ensuring reliable real-world deployment. CoEnv operates through three stages: real-to-sim scene reconstruction that digitizes physical workspaces, VLM-driven action synthesis supporting both real-time planning with high-level interfaces and iterative planning with code-based trajectory generation, and validated sim-to-real transfer with collision detection for safe deployment. Extensive experiments on challenging multi-arm manipulation benchmarks demonstrate CoEnv's effectiveness in achieving high task success rates and execution efficiency, establishing a new paradigm for multi-agent embodied AI.
Published: 2026-04-07 06:21:41
Authors: Hanxi Li, Jianan Zhou, Jiale Lao, Yibo Wang, Zhengmao Ye, Yang Cao, Junfen Wang, Mingjie Tang
Categories: cs.CR, cs.DB
Abstract:
Vector databases serve as the retrieval backbone of modern AI applications, yet their security remains largely unexplored. We propose the Black-Hole Attack, a poisoning attack that injects a small number of malicious vectors near the geometric center of the stored vectors. These injected vectors attract queries like a black hole and frequently appear in the top-k retrieval results for most queries. This attack is enabled by a phenomenon we term centrality-driven hubness: in high-dimensional embedding spaces, vectors near the centroid become nearest neighbors of a disproportionately large number of other vectors, while this centroid region is nearly empty in practice. The attack shows that vectors in a vector database cannot be blindly trusted: geometric defects in high-dimensional embeddings make retrieval inherently vulnerable. Our experiments show that malicious vectors appear in up to 99.85% of top-10 results. Additionally, we evaluate existing hubness mitigation methods as potential defenses against the Black-Hole Attack. The results show that these methods either significantly reduce retrieval accuracy or provide limited protection, which indicates the need for more robust defenses against the Black-Hole Attack.
Published: 2026-04-07 06:06:49
Authors: Giulio Valentino Dalla Riva
Categories: stat.ME, cs.LG, stat.ML
Abstract:
We study language models as evolving model organisms and ask when autoregressive next-token learning selects for world-tracking representations. For any encoding of latent world states, the Bayes-optimal next-token cross-entropy decomposes into the irreducible conditional entropy plus a Jensen--Shannon excess term. That excess vanishes if and only if the encoding preserves the training ecology's equivalence classes. This yields a precise notion of ecological veridicality for language models and identifies the minimum-complexity zero-excess solution as the quotient partition by training equivalence. We then determine when this fixed-encoding analysis applies to transformer families: frozen dense and frozen Mixture-of-Experts transformers satisfy it, in-context learning does not enlarge the model's separation set, and per-task adaptation breaks the premise. The framework predicts two characteristic failure modes: simplicity pressure preferentially removes low-gain distinctions, and training-optimal models can still incur positive excess on deployment ecologies that refine the training ecology. A conditional dynamic extension shows how inter-model selection and post-training can recover such gap distinctions under explicit heredity, variation, and selection assumptions. Exact finite-ecology checks and controlled microgpt experiments validate the static decomposition, split-merge threshold, off-ecology failure pattern, and two-ecology rescue mechanism in a regime where the relevant quantities are directly observable. The goal is not to model frontier systems at scale, but to use small language models as laboratory organisms for theory about representational selection.
Published: 2026-04-07 05:57:56
Authors: Toshifumi Futamase, Reiki Kojima, Masanori Tomonaga
Categories: astro-ph.CO
Abstract:
The Hubble parameters measured by the DESI DR2 BAO observations show a significant discrepancy from the prediction of the standard cosmological model.
This discrepancy, together with the long-discussed Hubble tension, may originate from large-scale inhomogeneities in the matter distribution.
This interpretation is motivated by infrared galaxy surveys, which suggest that our galaxy resides within the $\sim300$ Mpc under-dense region known as the KBC void.
In this study, we apply a linear order relation -- relating the horizon-scale Hubble parameter inferred from CMB observations and the local-scale Hubble parameter -- to the Pantheon+ Type Ia supernovae and the DESI DR2 BAO data.
We show that a simple inhomogeneous cosmological model consisting of eight top-hat shells can consistently explain the Hubble parameters inferred from both observations.
Based on the matter-density distribution, we also briefly discuss its possible impact on cosmological observables, including the magnitude--redshift relation, the kinematic Sunyaev--Zel'dovich effect, and the integrated Sachs--Wolfe effect.
Published: 2026-04-07 05:56:09
Authors: Dongsoo Han
Categories: cs.CY
Abstract:
This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.
Published: 2026-04-07 05:40:00
Authors: Yao Wang
Categories: cond-mat.str-el, physics.optics
Abstract:
Novel nonequilibrium states of magnet induced by light attract considerable attention both in nature of physics and apply. In this work, we systematically explore the electronic and magnetic states of a double-exchange model on a triangular lattice under the irradiation of circularly polarized continuous wave field, by means of molecular dynamics calculation. Several exotic nonequilibrium magnetic states are discovered, including a vortex state, long-range magnetic orders at the $Γ$ and $\textbf{K}/2$, as well as quasi(dynamical)-long-range magnetic order at the $\textbf{K}$ and $\textbf{M}$, respectively. Correspondingly, the evolution of electron bands and fillings are also uncovered. These results offer a promising candidate approach for the optical control of exotic magnetic and electronic states.
Published: 2026-04-07 05:39:21
Authors: Akoramurthy B, Surendiran. B
Categories: quant-ph
Abstract:
Quantum phase estimation~(QPE) is central to numerous quantum algorithms, yet its standard implementation demands an $\calO(m^{2})$-gate quantum Fourier transform~(QFT) on $m$ control qubits-a prohibitive overhead on near-term noisy intermediate-scale quantum (NISQ) devices. We introduce the \emph{Phase-Fidelity-Aware Truncated QFT} (PFA-TQFT), a family of approximate QFT circuits parameterised by a truncation depth~$d$ that omits controlled-phase rotations below a hardware-calibrated fidelity threshold~$\eps$. Our central result establishes $\TV(P_{\varphi},P_{\varphi}^{d})\leqπ(m{-}d)/2^{d}$, showing that for $d=\calO(\log m)$ circuit size collapses from $\calO(m^{2})$ to $\calO(m\log m)$ while estimation error grows by at most $\calO(2^{-d})$. We characterise $\dstar=\Floor{\log_{2}(2π/\eps_{2q})}$ directly from native gate fidelities, demonstrating 31.3 -43.7\% at m = 30, gate-count reduction on IBM Eagle/Heron and IonQ~Aria with negligible accuracy loss. Numerical experiments on the transverse-field Ising model confirm all theoretical predictions and reveal a \emph{noise-truncation synergy}: PFA-TQFT outperforms full QFT under NISQ noise $\eps_{2q}\gtrsim 2\times10^{-3}$.
Published: 2026-04-07 05:37:31
Authors: Yu-Ting Kao, Yeong-Jar Chang
Categories: quant-ph
Abstract:
In the pursuit of quantum advantage for financial engineering, researchers face a critical dilemma: analog rotation gates suffer from inherent 'sine-to-square' biases and error magnification, while digital arithmetic circuits (e.g., WeightedAdder) incur prohibitive quadratic complexity that exceeds NISQ capabilities. This study introduces Digital Spreading (DS), a fully digital quantum computing framework designed to resolve this trade-off. DS overcomes these limitations by utilizing a pruned Cuccaro ripple-carry architecture that avoids costly multiplication and eliminates rotation gates entirely. The proposed circuit employs integer comparison operations on superposed quantum states, mapping multi-qubit outcomes onto the probability of a single target qubit. Experiments based on a random walk model for option pricing demonstrate that DS achieves floating-point precision with a relative error as low as 0.0001%, outperforming JP Morgan's rotation-based method (1.43%), as well as ITRI's analog calibration (1.43%) and digital calibration approaches (19.14%). Overall, DS provides a compact, robust, and accurate framework for quantum weighted-average computation.
Published: 2026-04-07 05:23:39
Authors: Ruixue Li, Wenjing Yang, Zhaorong Zhang, Xun Li, Juanjuan Xu
Categories: math.OC
Abstract:
In this paper, we investigate the distributed optimal control problem for a kind of nonlinear multi-agent systems. In particular,both the state and the system dynamic structures of each agent are private and can only be shared among communicating agents.This type of information structure is inevitable in fields such as collaborative control for industrial confidentiality, and renders traditional distributed control methods using all systems' dynamic structures ineffective. The primary contribution is the proposal of a distributed algorithm for the global optimal controller under such practical information structure via distributed approximation of the Hamilton-Jacobi-Bellman equation. Practical numerical simulation demonstrates the effectiveness of the proposed algorithm.
Published: 2026-04-07 04:59:50
Authors: Yi-Jen Tsai, Yen-Yu Lin, Chien-Yao Wang
Categories: cs.CV
Abstract:
Few-Shot Semantic Segmentation (FSS) focuses on segmenting novel object categories from only a handful of annotated examples. Most existing approaches rely on extensive episodic training to learn transferable representations, which is both computationally demanding and sensitive to distribution shifts. In this work, we revisit FSS from the perspective of modern vision foundation models and explore the potential of Segment Anything Model 3 (SAM3) as a training-free solution. By repurposing its Promptable Concept Segmentation (PCS) capability, we adopt a simple spatial concatenation strategy that places support and query images into a shared canvas, allowing a fully frozen SAM3 to perform segmentation without any fine-tuning or architectural changes. Experiments on PASCAL-$5^i$ and COCO-$20^i$ show that this minimal design already achieves state-of-the-art performance, outperforming many heavily engineered methods. Beyond empirical gains, we uncover that negative prompts can be counterproductive in few-shot settings, where they often weaken target representations and lead to prediction collapse despite their intended role in suppressing distractors. These findings suggest that strong cross-image reasoning can emerge from simple spatial formulations, while also highlighting limitations in how current foundation models handle conflicting prompt signals. Code at: https://github.com/WongKinYiu/FSS-SAM3
Published: 2026-04-07 04:58:01
Authors: Wuyang Zhang, Shichao Pei
Categories: cs.CR, cs.AI
Abstract:
Tool-use large language model (LLM) agents are increasingly deployed to support sensitive workflows, relying on tool calls for retrieval, external API access, and session memory management. While prior research has examined various threats, the risk of systematic data exfiltration by backdoored agents remains underexplored. In this work, we present Back-Reveal, a data exfiltration attack that embeds semantic triggers into fine-tuned LLM agents. When triggered, the backdoored agent invokes memory-access tool calls to retrieve stored user context and exfiltrates it via disguised retrieval tool calls. We further demonstrate that multi-turn interaction amplifies the impact of data exfiltration, as attacker-controlled retrieval responses can subtly steer subsequent agent behavior and user interactions, enabling sustained and cumulative information leakage over time. Our experimental results expose a critical vulnerability in LLM agents with tool access and highlight the need for defenses against exfiltration-oriented backdoors.
Published: 2026-04-07 04:26:59
Authors: Honghao Fu, Miao Xu, Yiwei Wang, Dailing Zhang, Liu Jun, Yujun Cai
Categories: cs.CV, cs.AI
Abstract:
Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organizing query-relevant visual evidence into a compact context, most existing methods (i) flatten videos into independent segments, breaking their inherent spatio-temporal structure, and (ii) depend on explicit semantic matching, which can miss cues that are implicitly relevant to the query's intent. To overcome these limitations, we propose VideoStir, a structured and intent-aware long-video RAG framework. It firstly structures a video as a spatio-temporal graph at clip level, and then performs multi-hop retrieval to aggregate evidence across distant yet contextually related events. Furthermore, it introduces an MLLM-backed intent-relevance scorer that retrieves frames based on their alignment with the query's reasoning intent. To support this capability, we curate IR-600K, a large-scale dataset tailored for learning frame-query intent alignment. Experiments show that VideoStir is competitive with state-of-the-art baselines without relying on auxiliary information, highlighting the promise of shifting long-video RAG from flattened semantic matching to structured, intent-aware reasoning. Codes and checkpoints are available at Github.
Published: 2026-04-07 04:25:26
Authors: Taehyeon Kim, Hojung Jung, Se-Young Yun
Categories: cs.CL
Abstract:
Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller model to draft future tokens, which are then verified by the target LLM. This preserves generation quality by accepting only aligned tokens. However, individual drafters, often trained for specific tasks or domains, exhibit limited effectiveness across diverse applications. To address this, we introduce \textsc{MetaSD}, a unified framework that integrates multiple drafters into the SD process. MetaSD dynamically allocates computational resources to heterogeneous drafters by leveraging alignment feedback and framing drafter selection as a multi-armed bandit problem. Extensive experiments show MetaSD consistently outperforms single-drafter approaches.
Published: 2026-04-07 04:19:39
Authors: Shijie Wang, Zijian Wang, Yadan Luo, Scott Chapman, Xin Yu, Zi Huang
Categories: cs.CV
Abstract:
Wheat disease segmentation is fundamental to precision agriculture but faces severe challenges from significant intra-class temporal variations across growth stages. Such substantial appearance shifts make collecting a representative dataset for training from scratch both labor-intensive and impractical. To address this, we propose SGPer, a Semantic-Geometric Prior Synergization framework that treats wheat disease segmentation under limited data as a coupled task of disease-specific semantic perception and disease boundary localization. Our core insight is that pretrained DINOv2 provides robust category-aware semantic priors to handle appearance shifts, which can be converted into coarse spatial prompts to guide SAM for the precise localization of disease boundaries. Specifically, SGPer designs disease-sensitive adapters with multiple disease-friendly filters and inserts them into both DINOv2 and SAM to align their pretrained representations with disease-specific characteristics. To operationalize this synergy, SGPer transforms DINOv2-derived features into dense, category-specific point prompts to ensure comprehensive spatial coverage of all disease regions. To subsequently eliminate prompt redundancy and ensure highly accurate mask generation, it dynamically filters these dense candidates by cross-referencing SAM's iterative mask confidence with the category-specific semantic consistency derived from DINOv2. Ultimately, SGPer distills a highly informative set of prompts to activate SAM's geometric priors, achieving precise and robust segmentation that remains strictly invariant to temporal appearance changes. Extensive evaluations demonstrate that SGPer consistently achieves state-of-the-art performance on wheat disease and organ segmentation benchmarks, especially in data-constrained scenarios.
Published: 2026-04-07 03:30:38
Authors: Ka I Chan, Hongbo Lan, Jun Fang, Yuntao Wang, Yuanchun Shi
Categories: cs.HC
Abstract:
Conflicts are common in text-based communication, particularly in intimate relationships, where misunderstandings can easily escalate into verbal aggression. To address this, we present SpeakSoftly, a system that applies Nonviolent Communication (NVC) principles to scaffold couples' conflict communication through LLM-powered just-in-time interventions. Informed by formative interviews with couples and NVC principles, we designed two core features: NVC-Prompt, which detects verbal aggression and suggests revisions to prevent escalation, and NVC-Guide, which analyzes dialogues to uncover users' feelings and needs, fostering self-awareness and perspective-taking. These features were implemented across three progressive intervention modes, each varying in intervention depth and tone: Basic Reminder, Neutral Guide, and Empathetic Guide. We conducted a mixed-methods user study with 18 couples across simulated and real-life conflict settings to evaluate the effectiveness of each mode. Results showed that Empathetic Guide significantly facilitated both behavioral and cognitive changes, while Neutral Guide was effective only for behavioral changes in simulated conflicts. In real-life conflicts, Neutral Guide showed distinct advantages due to lower cognitive load demands. We discuss the mechanisms behind these findings and propose design implications for in-situ interventions in high-stakes communication contexts.
Published: 2026-04-07 03:26:35
Authors: Srivathsan Poyyapakkam Sundar, Prince Frederick Kwao, Alexey Galda, Ayush Asthana
Categories: quant-ph, physics.chem-ph
Abstract:
Problems in quantum chemical simulations, especially achieving accurate excited-state potential energy surfaces, are among the primary applications to achieve quantum utility. On near-term quantum hardware, variants of the variational quantum eigensolver (VQE) algorithms are the primary choice for chemistry simulation. In this study, a combination of leading ground and excited state quantum algorithms for general excited states, namely, ADAPT-VQE/LUCJ and q-sc-EOM, are utilized to calculate accurate excited state potential energy surfaces in challenging bond-breaking scenarios and compared with the classical scalable EOM-CCSD method. This work investigates avenues toward quantum utility in excited-state quantum chemistry using the q-sc-EOM approach. We assess its accuracy while mitigating major scaling bottlenecks through the Davidson algorithm and basis rotation grouping, reducing the measurement scaling from O(N$^{12}$) to O(N$^{5}$), and implementing the method on quantum hardware with various error mitigation strategies to reduce gate and measurement errors in excited states. The hardware implementation of the q-sc-EOM algorithm, augmented by mitigation of M3 readout error and symmetry projection, produces reasonably accurate excited-state energies with gate noise identified as the predominant source of error. This paves the way for accurate and scalable, generally applicable quantum excited-state methods with potential for quantum utility while identifying critical problems that require advancements.
Published: 2026-04-07 03:24:56
Authors: Xing Tang, Jingyang Bin, Ziqiang Cui, Xiaokun Zhang, Fuyuan Lyu, Jingyan Jiang, Dugang Liu, Chen Ma, Xiuqiang He
Categories: cs.IR, cs.LG
Abstract:
The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often struggle to adapt to real-time preference shifts during inference due to challenges posed by distributional divergence and parameterized constraints. Existing approaches to address this issue include test-time training, test-time augmentation, and retrieval-augmented fine-tuning. However, these methods either introduce significant computational overhead, rely on random augmentation strategies, or require a carefully designed two-stage training paradigm. In this paper, we argue that the key to effective test-time adaptation lies in achieving both effective augmentation and efficient adaptation. To this end, we propose Retrieve-then-Adapt (ReAd), a novel framework that dynamically adapts a deployed SR model to the test distribution through retrieved user preference signals. Specifically, given a trained SR model, ReAd first retrieves collaboratively similar items for a test user from a constructed collaborative memory database. A lightweight retrieval learning module then integrates these items into an informative augmentation embedding that captures both collaborative signals and prediction-refinement cues. Finally, the initial SR prediction is refined via a fusion mechanism that incorporates this embedding. Extensive experiments across five benchmark datasets demonstrate that ReAd consistently outperforms existing SR methods.
Published: 2026-04-07 03:21:17
Authors: Qi Guo, Zheming Yang, Yunqing Hu, Chang Zhao, Wen Ji
Categories: cs.MM
Abstract:
Multimodal large language models (MLLMs) have shown strong capability in semantic understanding and visual reasoning, yet their use on continuous video streams in bandwidth-constrained edge-cloud systems incurs prohibitive computation and communication overhead and hinders low-latency alerting and effective visual evidence delivery. To address this challenge, we propose DAT to achieve high-quality semantic generation, low-latency event alerting, and effective visual evidence supplementation. To reduce unnecessary deep reasoning costs, we propose a collaborative small-large model cascade. A lightweight edge-side small model acts as a gating module to filter non-target-event frames and perform object detection, triggering MLLM inference only for suspicious frames. Building on this, we introduce an efficient fine-tuning strategy with visual guidance and semantic prompting, which improves structured event understanding, object detection, and output consistency. To ensure low-latency semantic alerting and effective visual evidence supplementation under bandwidth constraints, we further devise a semantics and bandwidth-aware multi-stream adaptive transmission optimization method. Experimental results show that DAT achieves 98.83% recognition accuracy and 100% output consistency. Under severe congestion, it reduces weighted semantic alert delay by up to 77.5% and delivers 98.33% of visual evidence within 0.5 s, demonstrating the effectiveness of jointly optimizing cascade inference and elastic transmission.
Published: 2026-04-07 03:14:49
Authors: Raul E. Curto, Abderrazzak Ech-charyfy, Hamza El Azhar, El Hassan Zerouali
Categories: math.FA
Abstract:
This paper is devoted to the study of propagation phenomena for $2$--hyponormal, quadratically hyponormal, and cubically hyponormal operator-valued weighted shifts. \ First, we show that every {\it quadratically} hyponormal matrix-valued weighted shift with two equal weights ({\it excluding the initial weight}) is flat. \ Second, we show that a {\it cubically} hyponormal operator-valued weighted shift with two equal weights ({\it possibly including the initial weight}) is flat. \ Next, we introduce a {\it local flatness} notion for matrix-valued weighted shifts. \ We prove that $2$--hyponormal (in particular, subnormal) matrix-valued weighted shifts satisfy this stronger propagation phenomenon. \ As a result, we prove a {\it structural decomposition theorem} for $2$--hyponormal matrix-valued weighted shifts.
Published: 2026-04-07 03:01:51
Authors: Rixiang Ni, Boyang Li, Jun Chen, Yonghao Li, Feiyu Ren, Yuji Wang, Haoyang Yuan, Wujiao He, Wei An
Categories: cs.CV
Abstract:
Infrared small target detection (IRSTD) aims to separate small targets from clutter backgrounds. Extensive research is dedicated to the pixel-level supervision-guided "encoder-decoder" segmentation paradigm. Although having achieved promising performance, they neglect the fact that small targets only occupy a few pixels and are usually accompanied with blurred boundary caused by clutter backgrounds. Based on this observation, we argue that the first principle of IRSTD should be target localization instead of separating all target region accompanied with indistinguishable background noise. In this paper, we reformulate IRSTD as a centroid regression task and propose a novel Single-Point Supervision guided Infrared Probabilistic Response Encoding method (namely, SPIRE), which is indeed challenging due to the mismatch between reduced supervision network and equivalent output. Specifically, we first design a Point-Response Prior Supervision (PRPS), which transforms single-point annotations into probabilistic response map consistent with infrared point-target response characteristics, with a High-Resolution Probabilistic Encoder (HRPE) that enables encoder-only, end-to-end regression without decoder reconstruction. By preserving high-resolution features and increasing effective supervision density, SPIRE alleviates optimization instability under sparse target distributions. Finally, extensive experiments on various IRSTD benchmarks, including SIRST-UAVB and SIRST4 demonstrate that SPIRE achieves competitive target-level detection performance with consistently low false alarm rate (Fa) and significantly reduced computational cost. Code is publicly available at: https://github.com/NIRIXIANG/SPIRE-IRSTD.
Published: 2026-04-07 03:01:11
Authors: Simran Arora, Devabrat Mahanta
Categories: hep-ph
Abstract:
Leptogenesis is an elegant way to explain the baryon asymmetry of the Universe in connection to the neutrino mass and mixing. Although leptogenesis from the decay of a heavy Majorana neutrino has been the minimal set up, it is also motivating to look for leptogenesis from the decay of triplet fermion as it can have detectable signatures in the experiments. However, due to strong gauge annihilations and constraints from neutrino sector, the triplet fermions have to be as heavy as $10^{10}$ GeV or more to generate the observed baryon asymmetry. While this prediction is based on the standard radiation dominated history of the early Universe, it is also possible to have a non-standard expansion history of the Universe prior to the big-bang nucleosynthesis. In this work we study triplet leptogenesis in two non-standard cosmological scenarios, where the Universe expands faster than radiation and a scalar tensor theory of gravity. We show that it is possible to have successful leptogenesis with a few TeV triplet fermion for fast expanding Universe and a few hundered TeV for a scalar tensor gravity theory.
Published: 2026-04-07 03:01:01
Authors: Dakang Cen, Caixia Ou, Seakweng Vong
Categories: math.NA
Abstract:
In this work, our aim is to introduce a symmetric fractional-order reduction (SFOR) method to develop numerical algorithms on nonuniform temporal meshes for fractional wave equations under lower regularity assumptions. The $L$-type methods--including $L1$ and $L2$-$1_σ$ schemes--are specifically designed for diffusion-wave equations, and we propose novel optimal parameter selections tailored to nonuniform meshes. Finally, several numerical experiments are conducted to validate the efficiency and accuracy of the algorithms.
Published: 2026-04-07 02:55:32
Authors: Zhe Yu, Wenpeng Xing, Meng Han
Categories: cs.AI, cs.LG
Abstract:
Retrieval-augmented generation (RAG) mitigates hallucination but does not eliminate it: a deployed system must still decide, at inference time, whether its answer is actually supported by the retrieved evidence. We introduce LatentAudit, a white-box auditor that pools mid-to-late residual-stream activations from an open-weight generator and measures their Mahalanobis distance to the evidence representation. The resulting quadratic rule requires no auxiliary judge model, runs at generation time, and is simple enough to calibrate on a small held-out set. We show that residual-stream geometry carries a usable faithfulness signal, that this signal survives architecture changes and realistic retrieval failures, and that the same rule remains amenable to public verification. On PubMedQA with Llama-3-8B, LatentAudit reaches 0.942 AUROC with 0.77,ms overhead. Across three QA benchmarks and five model families (Llama-2/3, Qwen-2.5/3, Mistral), the monitor remains stable; under a four-way stress test with contradictions, retrieval misses, and partial-support noise, it reaches 0.9566--0.9815 AUROC on PubMedQA and 0.9142--0.9315 on HotpotQA. At 16-bit fixed-point precision, the audit rule preserves 99.8% of the FP16 AUROC, enabling Groth16-based public verification without revealing model weights or activations. Together, these results position residual-stream geometry as a practical basis for real-time RAG faithfulness monitoring and optional verifiable deployment.
Published: 2026-04-07 02:54:59
Authors: Miguel Tierz
Categories: cond-mat.stat-mech, math-ph, quant-ph
Abstract:
We derive closed-form asymptotic formulas for the Rényi entanglement entropies of the open XX spin-$1/2$ chain by mapping the underlying determinant of the boundary correlation matrix (which has Toeplitz-plus-Hankel structure) to a Hankel determinant with a positive weight whose large-size asymptotics follow from known Riemann--Hilbert results. An explicit evaluation of the Szegő function yields the leading $2k_F$ oscillatory amplitude and phase. A single variable $s = 2\ell \sin(k_F/2)$ organizes the hard-edge crossover as the Fermi momentum approaches the band edge: the oscillation envelope obeys $s^{\pm1/α}$ power laws and $\ln s$ is the natural leading logarithm for a clean data collapse. For detached blocks the oscillatory amplitude is numerically consistent with a factorization through the conformal cross-ratio. The same framework recovers the open-boundary-condition (OBC) equipartition offset $-\tfrac{1}{2}\log\log\ell$ for symmetry-resolved entropies, together with the known halving of the Gaussian width relative to the periodic chain.