Published: 2026-03-02 18:59:29
Authors: Valentin Lacombe, Valentin Quesnel, Damien Sileo
Categories: cs.CL
Abstract:
Training on verifiable symbolic data is a promising way to expand the reasoning frontier of language models beyond what standard pre-training corpora provide. Yet existing procedural generators often rely on fixed puzzles or templates and do not deliver the distributional breadth needed at scale. We introduce Reasoning Core, a scalable suite that procedurally generates verifiable symbolic reasoning data across core formal domains: PDDL planning over randomized domains, first-order logic with equality, context-free grammar parsing and generation, causal reasoning over random Bayesian networks, and systems of equations. Each task is paired with an external solver for rigorous verification and admits continuous difficulty control for curriculum design. Examples can optionally include solver-derived reasoning traces, enabling supervised training from the earliest pre-training stages, and the same interface provides verifiable reward functions for reinforcement learning. Our experiments show that mixing Reasoning Core data into pre-training improves downstream reasoning while preserving, or slightly improving, language modeling quality. Zero-shot evaluations confirm these tasks challenge frontier models such as GPT-5. The code and data are publicly available under the MIT license.
Published: 2026-03-02 18:58:39
Authors: Siminfar Samakoush Galougah, Pranav Pulijala, Ramani Duraiswami
Categories: cs.SD
Abstract:
A primary challenge in developing synthetic spatial hearing systems, particularly underwater, is accurately modeling sound scattering. Biological organisms achieve 3D spatial hearing by exploiting sound scattering off their bodies to generate location-dependent interaural level and time differences (ITD/ILD). While Head-Related Transfer Function (HRTF) models based on rigid scattering suffice for terrestrial humans, they fail in underwater environments due to the near-impedance match between water and soft tissue. Motivated by the acoustic anatomy of underwater animals, we introduce a novel, analytically derived, closed-form forward model for scattering from a semi-transparent sphere containing two rigid spherical scatterers. This model accurately maps source direction, frequency, and material properties to the pressure field, capturing the complex physics of layered, penetrable structures. Critically, our model is implemented in a fully differentiable setting, enabling its integration with a machine learning algorithm to optimize a cost function for active localization. We demonstrate enhanced convergence for localization under noise using a physics-informed frequency weighting scheme, and present accurate moving-source tracking via an Extended Kalman Filter (EKF) with analytically computed Jacobians. Our work suggests that differentiable models of scattering from layered rigid and transparent geometries offer a promising new foundation for microphone arrays that leverage scattering-based spatial cues over conventional beamforming, applicable to both terrestrial and underwater applications. Our model will be made open source.
Published: 2026-03-02 18:58:22
Authors: Amir Asiaee, Kavey Aryan, James P. Long
Categories: cs.LG, stat.ML
Abstract:
Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be learned from data. Our contributions are: (i) a contamination-robust conformal coverage theorem that quantifies how misclassification of "unaffected" calibration examples degrades coverage via an explicit function $g(δ,n)$ of the contamination fraction and calibration set size, providing a finite-sample lower bound that holds for arbitrary contaminating distributions; (ii) a task-driven partial causal learning formulation that estimates only the binary descendant indicators $Z_{a,i}=\mathbf{1}\{i\in\mathrm{desc}(a)\}$ needed for selective calibration, rather than the full causal graph; and (iii) algorithms for descendant discovery via perturbation intersection patterns (differentially affected variable set intersections across interventions), and for approximate distance-to-intervention estimation via local invariant causal prediction. We provide recovery conditions under which contamination is controlled. Experiments on synthetic linear structural equation models (SEMs) validate the bound: under controlled contamination up to $δ=0.30$, the corrected procedure maintains $\ge 0.95$ coverage while uncorrected selective CP degrades to $0.867$. A proof-of-concept on Replogle K562 CRISPR interference (CRISPRi) perturbation data demonstrates applicability to real genomic screens.
Published: 2026-03-02 18:57:52
Authors: Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang, Yasaman Samadzadeh, Volker Tresp, Serena Yeung-Levy
Categories: cs.AI, cs.CL
Abstract:
Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for self-evolving large reasoning models (LRMs), enabling online adaptation on unlabeled test inputs via self-induced rewards through majority voting. However, a spurious yet high-frequency unverified consensus can become a biased and reinforced reward signal, leading to incorrect mode collapse. We address this failure mode with T^3RL (Tool-Verification for Test-Time Reinforcement Learning), which introduces test-time tool verification into reward estimation. Concretely, a verifier uses an external tool as evidence (e.g., from code execution) to upweight verified rollouts in a verification-aware voting, producing more reliable pseudo-labels for training. Across various math difficulties (MATH-500, AMC, and AIME 2024) and diverse backbone types, T^3RL significantly improves over TTRL, with larger gains on harder problems. More broadly, T^3RL can be viewed as verified online data synthesis, highlighting test-time tool verification as a key mechanism for stabilizing self-evolution.
Published: 2026-03-02 18:40:23
Authors: Andrew Urilyon, Romain Vasseur, Sarang Gopalakrishnan, Jacopo De Nardis
Categories: cond-mat.str-el
Abstract:
We introduce a multi-species generalization of the hard-rod gas in which each species has a distinct effective length, and the repulsive scattering shift is set by the smaller of the two colliding rods. We argue that this model shares key quasiparticle and scattering features with the XXZ spin chain. We show that fixing only the functional decay of bare velocities with rod length is sufficient to reproduce the XXZ spin-transport phase diagram: diffusion (with anomalous fluctuations) in the anisotropic regime and superdiffusion at the isotropic point. We then demonstrate that the statistics of charge transfer differs qualitatively from that of particle trajectories. For long rods, trajectories are Gaussian in the diffusive regime and appear to exhibit KPZ statistics at the isotropic point, providing a direct microscopic signature of KPZ physics in integrable quasiparticle motion. In contrast, charge-transfer fluctuations are anomalous in the anisotropic regime, while they cross over to Gaussian statistics at late times at the isotropic point, reconciling non-Gaussian trajectory fluctuations with Gaussian charge-transfer statistics. Our results establish classical hard-rod dynamics as a minimal yet quantitatively faithful framework for anomalous spin and charge transport in integrable systems, and offer new insight into the origin of KPZ fluctuations in isotropic integrable models.
Published: 2026-03-02 18:39:49
Authors: Jintao Zhang, Marco Chen, Haoxu Wang, Kai Jiang, Ion Stoica, Joseph E. Gonzalez, Jianfei Chen, Jun Zhu
Categories: cs.LG, cs.AI
Abstract:
Low-bit attention, such as SageAttention, has emerged as an effective approach for accelerating model inference, but its applicability to training remains poorly understood. In prior work, we introduced SageBwd, a trainable INT8 attention that quantizes six of seven attention matrix multiplications while preserving fine-tuning performance. However, SageBwd exhibited a persistent performance gap to full-precision attention (FPA) during pre-training. In this work, we investigate why this gap occurs and demonstrate that SageBwd matches full-precision attention during pretraining. Through experiments and theoretical analysis, we reach a few important insights and conclusions: (i) QK-norm is necessary for stable training at large tokens per step, (ii) quantization errors primarily arise from the backward-pass score gradient dS, (iii) reducing tokens per step enables SageBwd to match FPA performance in pre-training, and (iv) K-smoothing remains essential for training stability, while Q-smoothing provides limited benefit during pre-training.
Published: 2026-03-02 18:27:47
Authors: Gonzalo A. Benavides, Ricardo H. Nochetto, Mansur Shakipov
Categories: math.AP, math.DG, math.FA
Abstract:
This paper and its follow-up arXiv:2508.11109 are concerned with the well-posedness and $\mathrm{L}^p$-based Sobolev regularity for appropriate weak formulations of a family of prototypical PDEs posed on manifolds of minimal regularity. In particular, the domains are assumed to be compact, connected $d$-dimensional manifolds without boundary of class $C^k$ and $C^{k-1,1}$ ($k \geq 1$) embedded in $\mathrm{R}^{d+1}$. The focus of this program is on the $\mathrm{L}^p$-based theory that is sharp with respect to the regularity of the source terms and the manifold. In the present paper, we focus our attention on the case of general scalar elliptic problems. We first establish $\mathrm{L}^p$-based well-posedness and higher regularity for the purely diffusive problems with variable coefficients by localizing and rewriting these equations in flat domains to employ the Calderón--Zygmund theory, combined with duality arguments. We then invoke the Fredholm alternative to derive analogous results for general scalar elliptic problems, underscoring the subtle differences that the geometric setting entails compared to the theory in flat domains.
Published: 2026-03-02 18:26:25
Authors: Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur, Sara P. Oliveira, Angelos Chatzimparmpas, Sanne Abeln, Wilson Silva
Categories: cs.CV
Abstract:
While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influence predictions. To address this, we introduce DIMAFx, an explainable multimodal framework for cancer survival prediction that produces disentangled, interpretable modality-specific and modality-shared representations from histopathology whole-slide images and transcriptomics data. Across multiple cancer cohorts, DIMAFx achieves state-of-the-art performance and improved representation disentanglement. Leveraging its interpretable design and SHapley Additive exPlanations, DIMAFx systematically reveals key multimodal interactions and the biological information encoded in the disentangled representations. In breast cancer survival prediction, the most predictive features contain modality-shared information, including one capturing solid tumor morphology contextualized primarily by late estrogen response, where higher-grade morphology aligned with pathway upregulation and increased risk, consistent with known breast cancer biology. Key modality-specific features capture microenvironmental signals from interacting adipose and stromal morphologies. These results show that multimodal models can overcome the traditional trade-off between performance and explainability, supporting their application in precision medicine.
Published: 2026-03-02 18:24:16
Authors: Sarah Organ, Toby Kenney, Hong Gu
Categories: stat.ME
Abstract:
Controlling the false discovery rate (FDR) in variable selection becomes challenging when predictors are correlated, as existing methods often exclude all members of correlated groups and consequently perform poorly for prediction. We introduce a new setwise variable-selection framework that identifies clusters of potential predictors rather than forcing selection of a single variable. By allowing any member of a selected set to serve as a surrogate predictor, our approach supports strong predictive performance while maintaining rigorous FDR control. We construct sets via hierarchical clustering of predictors based on correlation, then test whether each set contains any non-null effects. Similar clustering and setwise selection have been applied in the familywise error rate (FWER) control regime, but previous research has been unable to overcome the inherent challenges of extending this to the FDR control framework. To control the FDR, we develop substantial generalizations of linear step-up procedures, extending the Benjamini-Hochberg and Benjamini-Yekutieli methods to accommodate the logical dependencies among these composite hypotheses. We prove that these procedures control the FDR at the nominal level and highlight their broader applicability. Simulation studies and real-data analyses show that our methods achieve higher power than existing approaches while preserving FDR control, yielding more informative variable selections and improved predictive models.
Published: 2026-03-02 18:15:09
Authors: Luigi Medrano, Arush Verma, Mukul Chhabra
Categories: cs.IR, cs.AI, cs.CL
Abstract:
Retrieval-Augmented Generation (RAG) systems commonly adopt retrieval fusion techniques such as multi-query retrieval and reciprocal rank fusion (RRF) to increase document recall, under the assumption that higher recall leads to better answer quality. While these methods show consistent gains in isolated retrieval benchmarks, their effectiveness under realistic production constraints remains underexplored. In this work, we evaluate retrieval fusion in a production-style RAG pipeline operating over an enterprise knowledge base, with fixed retrieval depth, re-ranking budgets, and latency constraints.
Across multiple fusion configurations, we find that retrieval fusion does increase raw recall, but these gains are largely neutralized after re-ranking and truncation. In our setting, fusion variants fail to outperform single-query baselines on KB-level Top-$k$ accuracy, with Hit@10 decreasing from $0.51$ to $0.48$ in several configurations. Moreover, fusion introduces additional latency overhead due to query rewriting and larger candidate sets, without corresponding improvements in downstream effectiveness.
Our analysis suggests that recall-oriented fusion techniques exhibit diminishing returns once realistic re-ranking limits and context budgets are applied. We conclude that retrieval-level improvements do not reliably translate into end-to-end gains in production RAG systems, and argue for evaluation frameworks that jointly consider retrieval quality, system efficiency, and downstream impact.
Published: 2026-03-02 17:56:52
Authors: Fausto Barbero, Fan Yang
Categories: math.LO
Abstract:
We study whether a logic based on team semantics can be enriched with a conditional satisfying minimal requirements--namely, preservation of the closure property of the logic, Modus Ponens, and the Deduction Theorem. We show that such well-behaved conditionals exist for downward or upward closed logics, but do not typically exist for union closed, convex or intersection closed logics. We also briefly investigate conditionals satisfying weaker requirements.
Published: 2026-03-02 17:41:46
Authors: Fernando M. Fernandes, Fouad El Haj Hassan, Sophie Hermans, Benoît Hackens
Categories: cond-mat.mtrl-sci, cond-mat.mes-hall
Abstract:
In this work we provide an in-depth analysis of the sensing mechanisms of $NO_{2}$ by lead-sulfide nanocrystals (PbS-NCs). A detailed model for the sorption mechanism is proposed, and the correlation is established between experimental sensing characteristics and the surface composition, based on both experimental characterization and ab initio (DFT) simulations. We demonstrated how the sensitivity and the sensing dynamic response can be tuned by a post-deposition multistep dry-thermal process at mild temperature, that alternates vacuum-assisted annealing and heating in open-air. Sensors with different surface compositions were fabricated, and their dynamic response was characterized at low concentration of $NO_{2}$ (0.5 ppm) in air, at ambient temperature. DFT simulations indicate that both surface stoichiometry and oxidation critically govern $NO_{2}$ interaction on PbS, with sulfur-rich terminations favoring weaker binding and faster desorption, while intermediate oxidation enhances interaction and overoxidation leads to surface passivation, in agreement with the measured experimental sensing dynamics. By linking surface composition, adsorption chemistry, and resistance transduction within a single framework, this work provides clear indications to design room-temperature, low-ppm $NO_{2}$ microsensors fabricated through a simple and scalable processes.
Published: 2026-03-02 17:40:54
Authors: Justin Waugh
Categories: cs.AI, cs.GT, cs.LG
Abstract:
We introduce Pencil Puzzle Bench, a framework for evaluating large language model reasoning through pencil puzzles, a family of constraint-satisfaction problems closely related to NP-complete problems, with deterministic, step-level verification. From a database of 62,231 puzzles across 94 varieties with verified unique solutions, we select a benchmark of 300 puzzles spanning 20 varieties and evaluate 51 models from 11 providers in two modes: direct ask (single-shot) and agentic (multi-turn with iterative verification). A key differentiator of our benchmark is that every intermediate board state can be checked against variety-specific constraints, localizing errors to the exact rule violated, providing the infrastructure for dense, per-move reward signals for process supervision and reinforcement learning.
Our evaluation reveals two distinct axes of capability: (1) reasoning effort scaling, where GPT-5.2 improves 81x from no reasoning to maximum effort; and (2) agentic iteration, where Claude Opus 4.6 rises from 0.3% to 30.0% through iterative checking, while GPT-5.2@xhigh improves from 20.2% to 56.0%. Agentic attempts span a median of 29 turns over 17 minutes, with the longest exceeding 1,221 turns and 14.3 hours - a demanding test of long-context utilization, not just reasoning.
Published: 2026-03-02 17:38:58
Authors: Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang
Categories: cs.RO, cs.AI, cs.LG
Abstract:
General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effective for expert demonstrations, this paradigm scales poorly to large-scale robotics datasets where failed and suboptimal trajectories are abundant and assigning dense progress labels is ambiguous. We introduce Robometer, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision. Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints across trajectories of the same task, enabling effective learning from both real and augmented failed trajectories. To support this formulation at scale, we curate RBM-1M, a reward-learning dataset comprising over one million trajectories spanning diverse robot embodiments and tasks, including substantial suboptimal and failure data. Across benchmarks and real-world evaluations, Robometer learns more generalizable reward functions than prior methods and improves robot learning performance across a diverse set of downstream applications. Code, model weights, and videos at https://robometer.github.io/.
Published: 2026-03-02 17:37:10
Authors: Chenxiao Yang, Nathan Srebro, Zhiyuan Li
Categories: cs.LG, cs.CL
Abstract:
Modern language models reason within bounded context, an inherent constraint that poses a fundamental barrier to long-horizon reasoning. We identify recursion as a core principle for overcoming this barrier, and propose recursive models as a minimal realization, where the model can recursively invoke itself to solve subtasks in isolated contexts. We prove that any computable problem admits a recursive decomposition in which each subtask requires only exponentially smaller active context than standard autoregressive models; this strictly surpasses any context management approach confined to a single sequence, such as summarization. We further generalize our framework to modern agentic systems with arbitrary context processing and control flows, and prove that recursive models can achieve optimal power within this broader class. Experimentally, we train a 3B model to reason recursively and evaluate on Boolean satisfiability, a task requiring long-horizon combinatorial search, where it significantly outperforms frontier LLMs.
Published: 2026-03-02 17:28:02
Authors: Thang Pham, Andrea Pinamonti, Dung The Tran, Boqing Xue
Categories: math.CO, math.CA, math.GR, math.NT
Abstract:
We study Kakeya maximal operators associated with horizontal lines in finite Heisenberg groups $\mathbb H_n(\mathbb F_q)$. For the operator parameterized only by projective horizontal directions, we show that projection to $\mathbb F_q^{2n}$ reduces the problem to the affine finite field Kakeya maximal operator, and we determine the exact $\ell^u \to \ell^v$ growth exponent for all $n$ and all $1 \le u,v \le \infty$. We then introduce a refined-direction operator that also records the central slope of a horizontal line. In $\mathbb H_1(\mathbb F_q)$, we prove the sharp $\ell^2 \to \ell^2$ estimate \[ \|M_{\mathbb H_1}^{\mathrm{rd}}F\|_{\ell^2(D_1)} \lesssim q^{1/2}\|F\|_{\ell^2(\mathbb H_1(\mathbb F_q))}, \] deduce the exact mixed-norm exponent formula, and obtain lower bounds for horizontal Heisenberg Kakeya sets with prescribed refined directions. The argument is purely Fourier-analytic and does not use the polynomial method. An outlook toward a new approach to the affine Kakeya problem in $\mathbb{F}_q^3$ will be discussed in this paper.
Published: 2026-03-02 17:26:06
Authors: Yun Fang
Categories: astro-ph.CO, astro-ph.HE
Abstract:
Recent PTA analyses reporting evidence for a nanohertz common-spectrum process motivate targeted tests of whether any anisotropic component of the stochastic gravitational-wave background (SGWB) is correlated with the nearby large-scale structure (LSS), as anticipated for an astrophysical background dominated by supermassive black hole binaries. We present the first Bayesian PTA likelihood analysis that embeds an externally observed, full-sky galaxy-survey LSS template directly as an overlap-reduction-function (ORF) component. Using the 2MASS Photometric Redshift (2MPZ) galaxy catalog, we construct low-multipole LSS--correlated ORF templates in two redshift slices ($0
Summary
(gpt-4o-mini — added
2026-03-05 05:03 UTC)
Published: 2026-03-02 17:10:35
Authors: Francesco Lin, Michael Lipnowski
Categories: math.GT, math.DG, math.NT, math.SP
Abstract:
We discuss a construction of families of hyperbolic rational homology spheres with coexact $1$-form spectral gap uniformly bounded below which is well-suited for explicit computations. Using this, we provide several disjoint intervals containing a limit point of such spectral gaps, the rightmost of which is $[0.8196,0.8277]$. Furthermore, we also exhibit a family of arithmetic examples, answering a question of Abdurrahman-Adve-Giri-Lowe-Zung.
Published: 2026-03-02 17:01:56
Authors: Ming-Hao Liu, Christophe De Beule, Alina Mreńca-Kolasińska, Hsin-You Wu, Aitor Garcia-Ruiz, Denis Kochan, Klaus Richter
Categories: cond-mat.mes-hall
Abstract:
We generalize the scalable tight-binding model for graphene, which allows for efficient quantum transport simulations in the Dirac regime, to account for elastic strain. We show that the original scalable model with scaling factor $s$ is readily applicable to strained graphene, provided that the displacement fields corresponding to the deformed graphene lattice are properly scaled. In particular, we show that the long-wavelength theory remains invariant when the strain tensor is scaled by $s$. This is achieved in practice by scaling the in-plane displacement fields by $s$ while the out-of-plane displacements have to be scaled by $\sqrt{s}$. We confirm these scaling laws by extensive numerical simulations, starting with the pseudomagnetic field and the local density of states for different scaled lattices. The latter allows us to study pseudo-Landau levels as well as hybrid Landau levels in the presence of an external magnetic field. Finally, we consider quantum transport simulations motivated by a recent experiment, where a uniaxial strain barrier is engineered in monolayer graphene by vertically misaligned gates. Our work generalizes the scalable tight-binding model to allow for efficient modeling of quantum transport in large-scale strained graphene devices.
Published: 2026-03-02 17:00:22
Authors: Ding Pan, Zhuangzhuang Zhou, Long Qian, Binhang Yuan
Categories: cs.DC
Abstract:
The rapid adoption of large language models and multimodal foundation models has made multimodal data preparation pipelines critical AI infrastructure. These pipelines interleave CPU-heavy preprocessing with accelerator-backed (GPU/NPU/TPU) inference and produce massive intermediate artifacts. Achieving high throughput is difficult because workloads are highly non-stationary: regime shifts, input-dependent inference, and transient memory spikes cause rapid performance fluctuations and out-of-memory (OOM) failures. Existing schedulers typically rely on threshold-based autoscaling or assume synchronous, homogeneous operators, leading to poor efficiency. We present Trident, an adaptive scheduling framework for heterogeneous multimodal pipelines on fixed-resource clusters. Trident closes the loop across three coupled layers: (i) an observation layer that estimates per-operator sustainable throughput for asynchronous operators via Gaussian Process regression with anomaly filtering; (ii) an adaptation layer that detects workload shifts online and performs memory-constrained Bayesian optimization to recommend OOM-safe configurations; and (iii) a scheduling layer that solves a mixed-integer linear program to jointly optimize operator parallelism, placement, and configuration transitions under heterogeneous compute and bandwidth constraints, accounting for cold-start overhead via rolling updates. Decisions trigger sample invalidation and model refresh to keep estimates consistent with the active configuration. Implemented on Ray Data, Trident improves end-to-end throughput by up to 2.01x on a document curation (PDF) pipeline and 1.88x on a video curation pipeline over a static baseline, with low overhead suitable for online re-optimization.
Published: 2026-03-02 16:55:48
Authors: Alexander Zuyev, Emmanuel Trélat
Categories: math.OC
Abstract:
We establish a polynomial turnpike estimate for an optimal control problem consisting of a system of infinitely many controlled oscillators, considered as an abstract differential equation in a Hilbert space, with a quadratic cost. Our proof relies on spectral considerations and on the construction of a Riesz basis. A concrete example is given, which involves a rotating bodybeam system. To our knowledge, this is the first example of a pointwise turnpike estimate around a steady-state that is polynomial but not exponential.
Published: 2026-03-02 16:53:52
Authors: Lilian Luo, Paola Pinilla, Camila Pulgarés, Laura M. Pérez, Miguel Vioque, Nicolás T. Kurtovic, Anibal Sierra, Carolina Agurto-Gangas, Rossella Anania, John Carpenter, Lucas A. Cieza, Dingshan Deng, James Miley, Ilaria Pascucci, Giovanni P. Rosotti, Benoît Tabone, Ke Zhang
Categories: astro-ph.EP
Abstract:
How substructures and disk properties affect dust evolution and the delivery of solids and volatiles into planet-forming regions remains an open question. We present results from tailored dust evolution modeling of the AGE-PRO ALMA large program, a sample of 30 protoplanetary disks spanning different evolutionary stages. Visibility fitting of the AGE-PRO ALMA data (at 1.3\,mm) reveals that approximately half of the disks exhibit radial substructures. Combined with stellar properties, disk inclinations, and gas mass estimates from CO isotopologues and N$_2$H$^+$, this well-characterized set of disks provides an ideal testbed to constrain dust evolution models across different ages and disk morphologies. Using the dust evolution code \texttt{DustPy}, we simulate dust evolution in each disk under four model configurations, varying two key free parameters: the turbulent viscosity ($α= 10^{-4}, 10^{-3}$) and fragmentation velocity ($v_{\rm{frag}} = 1 \mathrm{m\,s^{-1}}, 10 \mathrm{m\,s^{-1}}$). Pressure traps are incorporated by perturbing the gas surface density based on the continuum intensity profiles, and synthetic observations generated with \texttt{RADMC-3D} are compared to these profiles. While no single model fits all disks, nearly half are best reproduced by the configuration with low turbulence and low fragmentation velocity ($α= 10^{-4}, v_{\rm{frag}} = 1\,\mathrm{m\,s^{-1}}$). Models of smooth disks underpredict dust mass, possibly indicating unresolved substructures. Pebble fluxes into inner disk regions correlate more strongly with disk age than with the presence of substructures, highlighting time-dependent dust transport as a key factor in shaping inner disk composition. Our results also provide a comparative baseline for interpreting multiwavelength and JWST water vapor observations.
Published: 2026-03-02 16:51:24
Authors: Konstantinos Vrettos, Galini Papadaki, Emmanouil Brilakis, Matthaios Triantafyllou, Dimitrios Leventis, Despina Staraki, Maria Mavroforou, Eleftherios Tzanis, Konstantina Giouroukou, Michail E. Klontzas
Categories: cs.AI
Abstract:
The rapid developments in artificial intelligence (AI) research in radiology have produced numerous models that are scattered across various platforms and sources, limiting discoverability, reproducibility and clinical translation. Herein, OpenRad was created, a curated, standardized, open-access repository that aggregates radiology AI models and providing details such as the availability of pretrained weights and interactive applications. Retrospective analysis of peer reviewed literature and preprints indexed in PubMed, arXiv and Scopus was performed until Dec 2025 (n = 5239 records). Model records were generated using a locally hosted LLM (gpt-oss:120b), based on the RSNA AI Roadmap JSON schema, and manually verified by ten expert reviewers. Stability of LLM outputs was assessed on 225 randomly selected papers using text similarity metrics. A total of 1694 articles were included after review. Included models span all imaging modalities (CT, MRI, X-ray, US) and radiology subspecialties. Automated extraction demonstrated high stability for structured fields (Levenshtein ratio > 90%), with 78.5% of record edits being characterized as minor during expert review. Statistical analysis of the repository revealed CNN and transformer architectures as dominant, while MRI was the most commonly used modality (in 621 neuroradiology AI models). Research output was mostly concentrated in China and the United States. The OpenRad web interface enables model discovery via keyword search and filters for modality, subspecialty, intended use, verification status and demo availability, alongside live statistics. The community can contribute new models through a dedicated portal. OpenRad contains approx. 1700 open access, curated radiology AI models with standardized metadata, supplemented with analysis of code repositories, thereby creating a comprehensive, searchable resource for the radiology community.
Published: 2026-03-02 16:30:03
Authors: Yihan Liu, Yu Zhang, C. -Y. Ng, Zijian Qiu, Sujie Lin, Lili Yang
Categories: astro-ph.HE
Abstract:
The evolution of pulsar Wind Nebulae (PWNe) influences how high energy particles in the vicinity are generated and transport. The Vela PWN (only $\sim300$\,pc away), provides a rather rare case between young and well-evolved systems. We therefore performed new 6 and 16\,cm high-resolution observations of the Vela X Cocoon region with the Australia Telescope Compact Array (ATCA). The observations reveal a complex region with a $\sim0.5^\circ$ major curved filament extending to far south from the pulsar, as well as other intersecting filaments and wisps. Our spectral analysis hints its connection with the PWN. Our results also found strongly linearly polarized emission, ordered and tangential $B$-field to the filaments. We find the rotation measure (RM) and polarization fraction (PF) along the filament are anti-correlated with the total intensity. We develop a simple 3D model of a spiral filament to explain these, while the PF distribution requires external interpretations such as interaction with the reverse shock. Comparison with archival data suggests that large scale features like the major filament are generally stable and large motions near the X-ray filament, all these confirm the distinction between radio and X-ray features.
Published: 2026-03-02 16:28:39
Authors: Andrew Szot, Michael Kirchhof, Omar Attia, Alexander Toshev
Categories: cs.LG
Abstract:
Reinforcement learning (RL) has demonstrated notable success in post-training large language models (LLMs) as agents for tasks such as computer use, tool calling, and coding. However, exploration remains a central challenge in RL for LLM agents, especially as they operate in language-action spaces with complex observations and sparse outcome rewards. In this work, we address exploration for LLM agents by leveraging the ability of LLMs to plan and reason in language about the environment to shift exploration from low-level actions to higher-level language strategies. We thus propose Strategy-Guided Exploration (SGE), which first generates a concise natural-language strategy that describes what to do to make progress toward the goal, and then generates environment actions conditioned on that strategy. By exploring in the space of strategies rather than the space of actions, SGE induces structured and diverse exploration that targets different environment outcomes. To increase strategy diversity during RL, SGE introduces mixed-temperature sampling, which explores diverse strategies in parallel, along with a strategy reflection process that grounds strategy generation on the outcomes of previous strategies in the environment. Across UI interaction, tool-calling, coding, and embodied agent environments, SGE consistently outperforms exploration-focused RL baselines, improving both learning efficiency and final performance. We show that SGE enables the agent to learn to solve tasks too difficult for the base model.
Published: 2026-03-02 16:18:42
Authors: Melis Yardımcı, Samet Ok, Belinda Kalomeni
Categories: astro-ph.SR
Abstract:
We present a multi-band study of three symbiotic binaries using combined ground- and space-based monitoring that spans up to 14 years. These datasets enable a systematic investigation of variability on intermediate timescales (tens of days) and the detection of shorter-period signals. All systems display coherent photometric modulations that are distinct from the orbital cycles. In AX Per, a dominant 75-day signal and its 37-day harmonic are identified, which we interpret as pulsations of the cool giant. CI Cyg exhibits a stable modulation between 70 and 74 days, which likely arises from a combination of pulsation and circumstellar or disk-related variability. For Z And, we confirm a persistent modulation between 55 and 60 days, consistent with semiregular pulsations of the cool component. Additionally, space-based data reveal further short-period variability, including coherent signals at 26.7 and 66.6 minutes in Z And and CI Cyg, respectively, and a quasi-periodic modulation near 0.95 days in AX Per. These detections suggest the presence of rapid activity driven by accretion or rotation, superposed on the intermediate timescale behavior. Our results show that the observed variability in these symbiotic binaries reflects the combined effects of cool giant pulsation, circumstellar or disk activity, and possible rotation of the hot component. The multi-timescale behavior revealed here offers new constraints on mass transfer and activity cycles in interacting binaries.
Published: 2026-03-02 16:11:37
Authors: Javier Lafuente-López
Categories: math.DG, gr-qc
Abstract:
A viable spacetime is one that admits a complete timelike geodesic. It is shown that a causal diffeomorphism preserving the Ricci tensor between two spacetimes is necessarily a homothety, if one of them is viable.
Published: 2026-03-02 16:07:24
Authors: Yue Niu, Zhaokai Sun, Jiayi Yang, Xiaofeng Cao, Rui Fan, Xin Sun, Hanli Wang, Wei Ye
Categories: cs.LG, cs.AI
Abstract:
Despite their success in various domains, the growing dependence on GNNs raises a critical concern about the nature of the combinatorial reasoning underlying their predictions, which is often hidden within their black-box architectures. Addressing this challenge requires understanding how GNNs translate topological patterns into logical rules. However, current works only uncover the hard logical rules over graph concepts, which cannot quantify the contribution of each concept to prediction. Moreover, they are post-hoc interpretable methods that generate explanations after model training and may not accurately reflect the true combinatorial reasoning of GNNs, since they approximate it with a surrogate. In this work, we develop a graph concept bottleneck layer that can be integrated into any GNN architectures to guide them to predict the selected discriminative global graph concepts. The predicted concept scores are further projected to class labels by a sparse linear layer. It enforces the combinatorial reasoning of GNNs' predictions to fit the soft logical rule over graph concepts and thus can quantify the contribution of each concept. To further improve the quality of the concept bottleneck, we treat concepts as "graph words" and graphs as "graph sentences", and leverage language models to learn graph concept embeddings. Extensive experiments on multiple datasets show that our method GCBMs achieve state-of-the-art performance both in classification and interpretability.
Published: 2026-03-02 16:05:02
Authors: He Li, Feichen Song, Boyi Zeng, Shixiang Song, Zhiqin John Xu, Ziwei He, Zhouhan Lin
Categories: cs.CL
Abstract:
Test-time scaling has shown that allocating more additional computation at inference can improve generation quality, motivating a natural follow-up question: where should this computation be spent? Building on this insight, we introduce PonderLM-3, a pretraining framework for token-wise adaptive pondering that learns to selectively allocate additional computation under purely self-supervised objectives, built on top of the PonderLM-2 backbone. This makes additional inference computation an allocatable per-token resource, so tokens receive more computation only when it is beneficial, rather than paying a uniform extra cost. To make this allocation learnable while maintaining train-inference consistency, PonderLM-3 injects a differentiable attention mask during pretraining and pairs it with a matching hard pruning rule at inference. PonderLM-3 defines a stronger Pareto frontier: compared with existing recursive or adaptive baselines, it achieves lower pretraining perplexity at equal inference FLOPs. On downstream benchmarks, PonderLM-3 attains comparable performance to fixed-step PonderLM-2 under the same maximum number of additional computation steps, while using fewer inference FLOPs in practice. Overall, PonderLM-3 provides an end-to-end differentiable and train-inference consistent framework for token-wise adaptive computation, enabling additional inference compute to be allocated where it is most useful rather than paid uniformly by every token.
Published: 2026-03-02 15:54:01
Authors: Yohan Bard, Emilie Presles, Marc Buyse, Silvy Laporte, Paul Zufferey, Frederikus A. Klok, Olivier Sanchez, Francis Couturaud, Edouard Ollier
Categories: stat.ME
Abstract:
Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC)remains limited. This work investigates the performance of several GPC-based estimators in the presence of clustering, temporal trends, and varying correlation structures typical of SW-CRTs. We conducted an extensive simulation study covering a range of intraclass correlations (ICC), cluster autocorrelation coefficients (CAC), time effects, and treatment effect sizes. Eight analytical approaches were compared, including unadjusted estimators, cluster-stratified win odds, mixed-effects models applied to cluster-period win odds, and probabilistic index models (PIMs). Type I error control was strongly compromised for methods ignoring time or clustering, whereas only two approaches consistently maintained nominal error rates: a hierarchical mixed-effects model with sequence and cluster-level random slopes (b4) and a cluster-restricted PIM (c2). These two methods were further evaluated in terms of statistical power, where c2 generally showed higher efficiency, particularly under strong clustering, low CAC, or the presence of temporal trends, while both converged to similar performance for large treatment effects. Overall, our findings identify b4 and c2 as the most reliable GPC-based strategies for SW-CRT analysis and provide practical guidance for their application, including for ongoing trials such as ETHER.
Published: 2026-03-02 15:49:21
Authors: Ferhat Kaya, Birgul Koc, Atakan Aygun, Onur Ata, Ali Karakus
Categories: physics.flu-dyn
Abstract:
Reduced-order models (ROMs) have become an essential tool for reducing the computational cost of fluid flow simulations. While standard ROMs can efficiently approximate laminar flows, their accuracy often suffers in convection-dominated regimes due to the truncation of dynamically important modes. To account for the influence of unresolved scales, ROM closure models are commonly introduced. Classical closure strategies are typically based on phenomenological arguments or analogies with large eddy simulation (LES), often formulated within a variational multiscale (VMS) framework, in which the resolved and unresolved scales are explicitly separated and their interactions are systematically modeled. More recently, advances in data-driven modeling and machine learning have opened new opportunities to construct ROM closures that are both more accurate and more consistent with the underlying physics. In this work, we develop a new ROM closure that combines machine learning with physics-based modeling principles. The closure term is derived within a VMS framework, where the reduced solution space is decomposed into resolved and unresolved components. This VMS-derived closure term is then modeled using PhysicsInformed Neural Networks (PINNs) and incorporated into a newly constructed C-PINN-ROM. The resulting closure leverages high-fidelity data while enforcing physical constraints imposed by the reduced-order equations, thereby ensuring consistency with the underlying dynamics and enhanced robustness in convection-dominated regimes. Through this PINN-based framework, we demonstrate how physics-informed machine learning can substantially improve the accuracy and robustness of ROMs, effectively bridging classical multiscale closure modeling with state-of-the-art data-driven methodologies.
Published: 2026-03-02 15:43:22
Authors: ATLAS Collaboration
Categories: hep-ex
Abstract:
A search is presented for massive long-lived particles in events featuring at least one displaced vertex and at least one displaced muon, using proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider from 2022 to 2024 at a centre-of-mass energy of 13.6 TeV. The data sample corresponds to an integrated luminosity of 164 fb$^{-1}$. The analysis targets scenarios in which long-lived particles decay inside the ATLAS inner detector, resulting in a topology of at least one massive, displaced vertex (DV) with multiple associated tracks, and at least one muon with a large transverse impact parameter relative to the primary interaction point. The muon is not required to be associated with the DV. Two signal regions are defined by the transverse distance of the reconstructed DV from the interaction point. Background contributions are estimated by using fully data-driven techniques. No significant excess above the expected background is observed. Upper limits at 95% confidence level are set on the visible cross-section and on the production cross-sections of several benchmark models of $R$-parity-violating supersymmetry.
Published: 2026-03-02 15:42:04
Authors: David Gentile, Joshua Huang, James M. Murphy
Categories: math.ST
Abstract:
Change point detection for time series analysis is a difficult and important problem in applied statistics, for which a variety of approaches have been developed in the past several decades. Here, the Wasserstein metric is employed as a tool for change-point identification in multi-dimensional time series data in order to identify clusters in time series in an unsupervised way. We leverage the simplicity of the optimal transport cost in the 1-dimensional setting to quickly identify both a segmentation (family of change points for a trajectory) and a clustering for the data when the number of segments is much smaller than the number of data points, making no parametric assumptions about the particular distributions involved. Our change point detection method scales linearly in the size of the data and in the dimension of the samples. We test our approach on idealized synthetic data trajectories, as well as real world trajectories coming from the domain of molecular dynamics simulations and underwater acoustics. We find that segmenting these time series via change points obtained by estimating the Wasserstein metric derivative and then clustering the identified segments as measures with similarity measured by the Wasserstein metric, successfully identifies metastable states in the law of the processes.
Published: 2026-03-02 15:40:25
Authors: Alexander Ulanowski, Johannes Früh, Fabian Salamon, Adrian Holzäpfel, Andreas Reiserer
Categories: quant-ph
Abstract:
Their exceptional coherence makes nuclear spins in solids a prime candidate for quantum memories in quantum networks and repeaters. Still, the direct all-optical initialization, coherent control, and readout of individual nuclear spin qubits have been an outstanding challenge. Here, this is achieved by embedding 167-Er dopants in yttrium orthosilicate in a cryogenic Fabry-Perot cavity, whose linewidth of 65 MHz is much smaller than the 0.9 GHz separation of neighboring hyperfine levels. Frequency-selective emission enhancement thus enables a single-shot readout fidelity of 91(2)%. Furthermore, a large magnetic field freezes paramagnetic impurities, leading to coherence times exceeding 0.2 s. The combination of nuclear-spin qubits with frequency-multiplexed addressing and lifetime-limited photon emission in the minimal-loss telecommunications C-band establishes 167-Er as a leading platform for long-range, fiber-based quantum networks.
Published: 2026-03-02 15:34:49
Authors: P. Cataldi, V. Cristiani, F. Rodriguez, A. Taverna, M. C. Artale, B. Levine, the LSST Dark Energy Science Collaboration
Categories: astro-ph.CO, astro-ph.GA
Abstract:
Upcoming imaging surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will enable high signal-to-noise measurements of galaxy clustering. The halo occupation distribution (HOD) is a widely used framework to describe the connection between galaxies and dark matter haloes, playing a key role in evaluating models of galaxy formation and constraining cosmological parameters. Consequently, developing robust methods for estimating this statistic is crucial to fully exploit data from current and future galaxy surveys. The main goal of this project is to extend a background subtraction method to estimate the HOD with more photometry-based information in preparation for the clustering analysis of the upcoming LSST data and to enable the study of the HOD with significantly improved statistical power. We evaluate the performance of the method using a mock galaxy redshift survey constructed from the cosmoDC2 catalogue. We implement an extension of the background subtraction technique to utilize information from photometric galaxy surveys. To identify the centres of galaxy groups, we implement an iterative centroiding approach (Central Galaxy Finder). We evaluate the impact of each step in our pipeline, including group size estimation from luminosity and purity, and completeness on group identification, along with the influence of observational systematics such as the use of photometric redshifts and halo mass uncertainties. We demonstrate the validity of the proposed method using a mock galaxy catalogue, recovering the HOD from cosmoDC2 over the absolute magnitude range $M_r = -20.0$ to $-17.0$ and halo masses up to $10^{15}\, \mathrm{M_\odot}$. We present key performance metrics to quantify the precision and reliability of the group finder and the resulting HOD measurements.
Published: 2026-03-02 15:13:44
Authors: Wei Ren, Xi Zhang, Shiyu Guo, Jeongsoo Park, Jack Tavakley, Daochen Long, Kenji Watanabe, Takashi Taniguchi, Ke Wang
Categories: cond-mat.mes-hall
Abstract:
We fabricate twisted double bilayer graphene devices with zero twist angle and a set of local top and bottom gates aligned perpendicularly to each other. A 1D PN junction can be electrostatically defined when the gate voltages applied to the top gates are the same but different on the bottom gates. Resistance peaks are observed at finite doping instead of at the charge neutrality points, exhibiting an unconventional broken-cross shape that arises from layer polarization of the P and N region, which can be further enhanced with finite magnetic fields. A 0D point junction (PJ) can be electrostatically defined by applying different gate voltages to the top and bottom gates, such that the P and N sides of the device are connected at a single point in the center of the device. As finite magnetic field B increases, the quantum Hall (QH) states are selectively brought into contact or away from each other depending on their layer polarization, leading to unconventional quantum oscillations which characterize the layer-polarized band-crossing. Our work provides new insights into understanding band-structure evolution and layer polarization in twisted bilayers and paves the way for new device functionality based on manipulating layer-polarized electronic states.
Published: 2026-03-02 15:08:14
Authors: Mehran Shakerinava, Behnoush Khavari, Siamak Ravanbakhsh, Sarath Chandar
Categories: cs.LG
Abstract:
State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling tasks while remaining efficient and highly-parallelizable. However, the theoretical understanding of their expressive power remains limited. In this work, we study the expressivity of input-Dependent Complex-valued Diagonal (DCD) SSMs on sequential state-tracking tasks. We show that single-layer DCD SSMs cannot express state-tracking of any non-Abelian group at finite precision. More generally, we show that $k$-layer DCD SSMs can express state-tracking of a group if and only if that group has a subnormal series of length $k$, with Abelian factors. That is, we identify the precise expressivity range of $k$-layer DCD SSMs within the solvable groups. Empirically, we find that multi-layer models often fail to learn state-tracking for non-Abelian groups, highlighting a gap between expressivity and learnability.
Published: 2026-03-02 14:57:24
Authors: Stefan Inerle, Markus Pauly, Moritz Berger
Categories: stat.ME
Abstract:
Ordinal measurements are common outcomes in studies within psychology, as well as in the social and behavioral sciences. Choosing an appropriate regression model for analysing such data poses a difficult task. This paper aims to facilitate modeling decisions for quantitative researchers by presenting the results of an extensive simulation study on the inferential properties of common ordinal regression models: the proportional odds model, the category-specific odds model, the location-shift model, the location-scale model, and the linear model, which incorrectly treats ordinal outcomes as metric. The simulations were conducted under different data generating processes based on each of the ordinal models and varying parameter configurations within each model class. We examined the bias of parameter estimates as well as type I error rates ($α$-errors) and the power of statistical parameter testing procedures corresponding to the respective models. Our findings reveal several highlights. For parameter estimates, we observed that cumulative ordinal regression models exhibited large biases in cases of large parameter values and high skewness of the outcome distribution in the true data generation process. Regarding statistical hypothesis testing, the proportional odds model and the linear model showed the most reliable results. Due to its better fit and interpretability for ordinal outcomes, we recommend the use of the proportional odds model unless there are relevant contraindications.
Published: 2026-03-02 14:52:52
Authors: Chao Chen, Yanhui Chen, Shanshan Lin, Dongsheng Hong, Shu Wu, Xiangwen Liao, Chuanyi Liu
Categories: cs.LG, cs.AI
Abstract:
Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs using human-provided explanations or supervision on model attributions. These approaches improve interpretability but typically assume benign inputs and incur heavy annotation costs. In contrast, both predictions and saliency maps of DNNs could dramatically alter facing imperceptible perturbations or unseen patterns. Adversarial training (AT) can substantially improve robustness, but it does not guarantee that model decisions rely on semantically meaningful features. In response, we propose Explanation-Guided Adversarial Training (EGAT), a unified framework that integrates the strength of AT and EGL to simultaneously improve prediction performance, robustness, and explanation quality. EGAT generates adversarial examples on the fly while imposing explanation-based constraints on the model. By jointly optimizing classification performance, adversarial robustness, and attributional stability, EGAT is not only more resistant to unexpected cases, including adversarial attacks and out-of-distribution (OOD) scenarios, but also offer human-interpretable justifications for the decisions. We further formalize EGAT within the Probably Approximately Correct learning framework, demonstrating theoretically that it yields more stable predictions under unexpected situations compared to standard AT. Empirical evaluations on OOD benchmark datasets show that EGAT consistently outperforms competitive baselines in both clean accuracy and adversarial accuracy +37% while producing more semantically meaningful explanations, and requiring only a limited increase +16% in training time.
Published: 2026-03-02 14:48:13
Authors: Junbo Huang, Max Weinig, Ulrich Fritsche, Ricardo Usbeck
Categories: cs.CL, cs.AI, cs.LG
Abstract:
Narratives in news discourse play a critical role in shaping public understanding of economic events, such as inflation. Annotating and evaluating these narratives in a structured manner remains a key challenge for Natural Language Processing (NLP). In this work, we introduce a narrative graph annotation framework that integrates principles from qualitative content analysis (QCA) to prioritize annotation quality by reducing annotation errors. We present a dataset of inflation narratives annotated as directed acyclic graphs (DAGs), where nodes represent events and edges encode causal relations. To evaluate annotation quality, we employed a $6\times3$ factorial experimental design to examine the effects of narrative representation (six levels) and distance metric type (three levels) on inter-annotator agreement (Krippendorrf's $α$), capturing the presence of human label variation (HLV) in narrative interpretations. Our analysis shows that (1) lenient metrics (overlap-based distance) overestimate reliability, and (2) locally-constrained representations (e.g., one-hop neighbors) reduce annotation variability. Our annotation and implementation of graph-based Krippendorrf's $α$ are open-sourced. The annotation framework and evaluation results provide practical guidance for NLP research on graph-based narrative annotation under HLV.
Published: 2026-03-02 14:45:11
Authors: Lev Gordeev, Edward Hermann Haeusler
Categories: cs.CC
Abstract:
In our previous papers we sketched proofs of the equality NP = coNP = PSPACE. These results have been obtained by proof theoretic tree-to-dag compressing techniques adapted to Prawitz's Natural Deduction (ND) for implicational minimal logic with references to Hudelmaier's cutfree sequent calculus. In this note we comment on Jeřábek's approach that claimed to refute our results by providing exponential lower bounds on the implicational minimal logic. This claim is wrong and misleading, which is briefly demonstrated by Basis example below.
Published: 2026-03-02 14:27:55
Authors: Muyu Liu, Chenhe Du, Xuanyu Tian, Qing Wu, Xiao Wang, Haonan Zhang, Hongjiang Wei, Yuyao Zhang
Categories: cs.CV
Abstract:
Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast distortion due to field-dependent relaxation dynamics. Reconstructing high-field (HF) quality images from LF data is a blind inverse problem, severely challenged by the scarcity of paired training data and the unknown, non-linear contrast transformation operator. Existing zero-shot methods, which assume simplified linear degradation, often fail to recover authentic tissue contrast. In this paper, we propose DACT(Diffusion-Based Adaptive Contrast Transport), a novel zero-shot framework that restores HF-quality images without paired supervision. DACT synergizes a pre-trained HF diffusion prior to ensure anatomical fidelity with a physically-informed adaptive forward model. Specifically, we introduce a differentiable Sinkhorn optimal transport module that explicitly models and corrects the intensity distribution shift between LF and HF domains during the reverse diffusion process. This allows the framework to dynamically learn the intractable contrast mapping while preserving topological consistency. Extensive experiments on simulated and real clinical LF datasets demonstrate that DACT achieves state-of-the-art performance, yielding reconstructions with superior structural detail and correct tissue contrast.
Published: 2026-03-02 14:25:32
Authors: Xu-Yang Hou, Xin Wang, Hao Guo
Categories: quant-ph
Abstract:
Geometric phases play a fundamental role in understanding quantum topology, yet extending the Uhlmann phase to non-Hermitian systems poses significant challenges due to parameter-dependent inner product structures. In this work, we develop a comprehensive theory of the Uhlmann phase for quasi-Hermitian systems, where the physical Hilbert space metric varies with external parameters. By constructing a generalized purification that respects the quasi-Hermitian inner product, we derive the corresponding parallel transport condition and Uhlmann connection. Our analysis reveals that the dynamic metric induces emergent geometric features absent in the standard Hermitian theory. Applying this formalism to solvable two-level models, we uncover rich finite-temperature topological phase diagrams, including multiple transitions between trivial and nontrivial phases driven by thermal fluctuations. Crucially, the quasi-Hermitian parameters are shown to profoundly influence the stability of topological regimes against temperature, enabling nontrivial phases to persist within finite-temperature windows. Furthermore, by extending established interferometric protocols originally developed for Hermitian systems, the geometric amplitude can be recast as a measurable Loschmidt fidelity between purified states, providing a practical and experimentally accessible pathway to investigate quasi-Hermitian mixed-state geometric phases and their finite-temperature transitions. This work establishes a unified framework for understanding mixed-state geometric phases in non-Hermitian quantum systems and opens a practical avenue for their experimental investigation.
Published: 2026-03-02 14:21:30
Authors: Hannah Selder, Charlotte Beylier, Nico Scherf, Arthur Fleig
Categories: cs.HC
Abstract:
Reinforcement learning (RL) enables simulations of HCI tasks, yet their validity is questionable when performance is driven by visual rendering artifacts distinct from interaction design. We provide the first systematic analysis of how luminance and contrast affect behavior by training 247 \RV{simulated users using RL} on pointing and tracking tasks. We vary the luminance of task-relevant objects, distractors, and background under no distractor, static distractor, and moving distractor conditions, and evaluate task performance and robustness to unseen luminances. Results show luminance becomes critical with static distractors, substantially degrading performance and robustness, whereas motion cues mitigate this issue. Furthermore, robustness depends on preserving relational ordering between luminances rather than matching absolute values. Extreme luminances, especially black, often yield high performance but poor robustness. Overall, seemingly minor luminance changes can strongly shape learned behavior, revealing critical insights into what RL-driven simulated users actually learn.
Published: 2026-03-02 14:01:09
Authors: J. A. Hernández-Guajardo, L. F. Barrientos, S. López, E. J. Johnston, C. Ledoux, N. Tejos, A. Afruni, M. Solimano, E. Jullo, H. Cortés-Muñoz, P. Noterdaeme, J. González-López, A. Ormazábal, F. Muñoz-Olivares, T. A. M. Berg
Categories: astro-ph.GA
Abstract:
We report spatially resolved measurements of cool gas traced by Mg II and Fe II absorption in the circumgalactic medium (CGM) of a star-forming galaxy at $z\sim1$ (G1). The fortuitous alignment of a background gravitational arc at z$\sim$2.4 provides seven closely spaced ($\sim$6 kpc) transverse sightlines along the minor axis of G1, probing its CGM out to $\sim$50 kpc. This geometry allows us to detect a galactic-scale outflow simultaneously in down-the-barrel and transverse directions, where blue-shifted Mg II absorption is detected along both types of sightlines, revealing a large-scale, collimated wind. We measure blue-shifted line-of-sight velocities of $v_{\mathrm{los}}$ $\sim$ 62 - 239 km s$^{-1}$ and line-of-sight velocity dispersions $σ_{\mathrm{los}}$ $\sim$ 53 - 133 km s$^{-1}$, suggesting a structure dominated by bulk motion. De-projection of $v_{\mathrm{los}}$ along the minor axis indicates that the outflow material barely approaches the escape velocity and is likely to be gravitationally bound to G1. We constrain an outflow opening angle $θ_c\sim$ 18$^\circ$ - 25$^\circ$, and a mass outflow rate of $ \dot{M}_{\mathrm{out}}$ $\gtrsim$ 0.06 $M_\odot$ yr$^{-1}$, corresponding to a mass loading factor $η$ $\gtrsim$ 0.004, estimated within $\sim$10 - 50 kpc ($\sim$ 0.05 - 0.3 $R_\text{vir}$) of the galaxy centre. Our measurements, combined with previous arc tomography data along the major axis, indicate that normalizing impact parameters by galaxy B-band luminosity substantially reduces scatter in the established anti-correlation between Mg II equivalent width and impact parameter, while also diminishing possible excess of Mg II equivalent width towards the minor axis.
Published: 2026-03-02 13:58:28
Authors: Yiheng Li, Zichang Tan, Guoqing Xu, Yijun Ye, Yang Yang, Zhen Lei
Categories: cs.CV
Abstract:
With the rapid development of generative AI in medical imaging, synthetic Computed Tomography (CT) images have demonstrated great potential in applications such as data augmentation and clinical diagnosis, but they also introduce serious security risks. Despite the increasing security concerns, existing studies on CT forgery detection are still limited and fail to adequately address real-world challenges. These limitations are mainly reflected in two aspects: the absence of datasets that can effectively evaluate model generalization to reflect the real-world application requirements, and the reliance on detection methods designed for natural images that are insensitive to CT-specific forgery artifacts. In this view, we propose CTForensics, a comprehensive dataset designed to systematically evaluate the generalization capability of CT forgery detection methods, which includes ten diverse CT generative methods. Moreover, we introduce the Enhanced Spatial-Frequency CT Forgery Detector (ESF-CTFD), an efficient CNN-based neural network that captures forgery cues across the wavelet, spatial, and frequency domains. First, it transforms the input CT image into three scales and extracts features at each scale via the Wavelet-Enhanced Central Stem. Then, starting from the largest-scale features, the Spatial Process Block gradually performs feature fusion with the smaller-scale ones. Finally, the Frequency Process Block learns frequency-domain information for predicting the final results. Experiments demonstrate that ESF-CTFD consistently outperforms existing methods and exhibits superior generalization across different CT generative models.
Published: 2026-03-02 13:44:18
Authors: Montijn van den Beukel, Jože Martin Rožanec, Ana-Lucia Varbanescu
Categories: cs.LG, cs.AI
Abstract:
The lack of accessible transactional data significantly hinders machine learning research for Anti-Money Laundering (AML). Privacy and legal concerns prevent the sharing of real financial data, while existing synthetic generators focus on simplistic structural patterns and neglect the temporal dynamics (timing and frequency) that characterise sophisticated laundering schemes.
We present Tide, an open-source synthetic dataset generator that produces graph-based financial networks incorporating money laundering patterns defined by both structural and temporal characteristics. Tide enables reproducible, customisable dataset generation tailored to specific research needs. We release two reference datasets with varying illicit ratios (LI: 0.10\%, HI: 0.19\%), alongside the implementation of state-of-the-art detection models.
Evaluation across these datasets reveals condition-dependent model rankings: LightGBM achieves the highest PR-AUC (78.05) in the low illicit ratio condition, while XGBoost performs best (85.12) at higher fraud prevalence. These divergent rankings demonstrate that the reference datasets can meaningfully differentiate model capabilities across operational conditions.
Tide provides the research community with a configurable benchmark that exposes meaningful performance variation across model architectures, advancing the development of robust AML detection methods.
Published: 2026-03-02 13:41:20
Authors: Edgar Desainte-Maréville, Marion Foare, Paulo Gonçalves, Nelly Pustelnik, Elisa Riccietti
Categories: eess.SP, math.OC
Abstract:
Classical first-order optimization methods for imaging inverse problems scale poorly with image resolution. Wavelet based multilevel strategies can accelerate convergence under strong blur, but their fixed coarse-to-fine schedules lose effectiveness in moderate-blur or noise-dominated regimes. In this work, we propose an adaptive multiresolution block coordinate Forward-Backward algorithm for image restoration. Multiresolution block selection is driven by the local magnitude of the proximal update via a stochastic non-smooth Gauss-Southwell rule applied to the wavelet decomposition of the image. This adaptive selection strategy dynamically balances updates across scales, emphasizing coarse or fine blocks according to the degradation regime. As a result, the proposed method automatically adapts to varying blur and noise levels without relying on a predefined hierarchical update scheme.
Published: 2026-03-02 13:34:52
Authors: D. G. Pires, N. M. Litchinitser
Categories: physics.optics
Abstract:
Topological structure is widely invoked as a route to disorder-resilient photonic states, yet whether it protects locally resolved field structure under realistic disorder has not been established. Optical skyrmions, vectorial light fields characterized by a global skyrmion number $N_{sk}$, provide a stringent test of this question under turbulence. Although $N_{sk}$ is expected to be robust, conservation of a global invariant does not guarantee preservation of the underlying polarization texture. Here we reconstruct the full Stokes field of optical skyrmions transmitted through controlled turbulent channels, combining experiment, phase screen simulations, and analytical modelling to independently track global and local observables. We demonstrate a broad disorder regime in which $N_{sk}$ remains conserved while fine polarization structure rapidly degrades. This pronounced decoupling, strengthened for higher-order skyrmions, exposes a hierarchy of robustness between topological invariants and texture-resolved information, defining intrinsic limits of topological protection in disordered wave systems.
Published: 2026-03-02 13:33:39
Authors: Xufei Lv, Jiahui Yang, Yifu Gao, Linbo Qiao, Houde Liu
Categories: cs.CL
Abstract:
Temporal Knowledge Graph Question Answering (TKGQA) demands multi-hop reasoning under temporal constraints. Prior approaches based on large language models (LLMs) typically rely on rigid, hand-crafted retrieval workflows or costly supervised fine-tuning. We show that simply granting an off-the-shelf LLM autonomy, that is, letting it decide what to do next, already yields substantial gains even in a strict zero-shot setting. Building on this insight, we propose AT2QA, an autonomous, training-free agent for temporal question answering that iteratively interacts with the temporal knowledge graph via a general search tool for dynamic retrieval. Experiments on MultiTQ demonstrate large improvements: AT2QA achieves 88.7% Hits@1 (+10.7% over prior SOTA), including a +20.1% gain on challenging multi-target queries, showing that agentic autonomy can decisively outperform fine-tuning for temporal question answering. Code and the full set of sampled trajectories are available on https://github.com/AT2QA-Official-Code/AT2QA-Official-Code