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01.
arXiv (CS.CL) 2026-06-19

CzechDocs: A Multiway Parallel Dataset of Formatted Documents for Minority Languages in Czechia

作者:
Josef Jon↗Ond\v{r}ej Bojar↗

We present CzechDocs, a multiway parallel dataset of formatted documents (HTML, DOCX, and PDF) covering Czech and minority languages used in Czechia-primarily Ukrainian and English, with smaller portions of Vietnamese, Russian and other languages. The dataset is designed to support the evaluation of machine translation systems that aim to preserve document formatting during translation. We provide a comparison of the most common approaches to format-preserving machine translation on a validation subset of the dataset. This validation split, together with the evaluation toolkit, is publicly released for further research. A held-out test split will be reserved for a future shared task focused on document-level translation with formatting preservation.

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02.
arXiv (CS.LG) 2026-06-19

Influence-Guided Concolic Testing of Transformer Robustness

作者:
Chih-Duo Hong↗Chih-Cheng Yang↗Yu Wang↗Fang Yu↗

arXiv:2509.23806v2 Announce Type: replace-cross Abstract: Concolic testing for neural networks alternates concrete execution with constraint solving to search for inputs that flip model decisions. We present a concolic tester for Transformer classifiers that uses SHAP estimates to rank pending path predicates by their impact on the current prediction. To support self-attention with multiple heads in execution backed by SMT solving, we implement attention semantics in pure Python that are compatible with the solver and make the softmax boundary explicit by concretizing exponentiation arguments. We evaluate our method on CIFAR-10 across three compact Transformer classifiers, ResNet18, and VGG16 under a one-pixel budget and a 900s horizon. Across the 500 model–input pairs in this matched comparison, our method achieves 60% success, compared with 15% for a differential evolution baseline that treats the model as a black box. In the primary two-layer Transformer branch-ordering study, SHAP-based predicate prioritization raises success from 56% to 60% and reduces median attack time by 51%. These results show that influence-guided path exploration can make concolic testing a practical way to find adversarial examples in Transformer models.

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03.
bioRxiv (Bioinfo) 2026-06-11

DLDN-Bench: A Benchmark Framework for Deep Learning de Novo Peptide Sequencing in Proteomics

作者:
Schneider↗Hartwig↗Chadt↗Lehr↗Al-Hasani↗Turewicz↗

De novo peptide sequencing is an essential approach for analyzing mass spectrometry data because it enables the identification of novel peptides without relying on protein sequence databases. Recent advances in deep learning have substantially improved the performance of de novo sequencing methods, but the rapid emergence of new models has led to heterogeneous evaluation practices and limited comparability. To address this, we introduce DLDN-Bench, a benchmark framework including a set of benchmark datasets derived from human muscle biopsy mass spectrometry data retrieved from PRIDE and annotated through consensus across multiple widely used database search engines. Using these datasets, we systematically benchmark recent deep learning-based de novo sequencing tools alongside traditional approaches. Performance is assessed using established metrics, including precision and coverage relative to a pseudo-ground truth defined by cross-engine agreement. To demonstrate the utility of DLDN-Bench, we benchmark four recent deep learning models and make all results publicly available. This benchmark framework provides a standardized basis for comparing state-of-the-art methods and offers an extensible resource for evaluating future tools in de novo peptide sequencing.

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04.
arXiv (CS.CV) 2026-06-24

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases

作者:
Qi Chen↗Wenxuan Li↗Pedro R. A. S. Bassi↗Xinze Zhou↗Jakob Wasserthal↗Ibrahim Ethem Hamamci↗Sezgin Er↗Ashwin Kumar↗Yiwen Ye↗Yuhan Wang↗Yuyin Zhou↗Akshay S. Chaudhari↗…

Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsistently across real-world clinical settings. Such inconsistencies occur when patient demographics and imaging protocols vary, for example, in detecting small tumors, analyzing scans from different contrast phases, or evaluating patients of different ages or sexes. To quantify these inconsistencies, we develop a large-scale, open benchmark of 85,355 CT scans that systematically evaluates 12 tumor-detection AI models across tumor size, location, patient subgroup, and imaging protocol. We leverage large language models (LLMs) to extract and organize subgroup information from clinical data, which makes the analysis both scalable and reproducible. Our benchmark reveals that current state-of-the-art AI models, optimized for average accuracy, perform poorly in rare or underrepresented subgroups, such as young, female African Americans. However, collecting sufficient annotated data for these rare cases is often impractical. The benchmark provides a foundation for building more reliable and robust AI models for tumor detection and highlighting the need for rigorous, subgroup-level evaluation in medical imaging and computer vision. Datasets, code

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05.
arXiv (math.PR) 2026-06-24

Conditioning of incoherent sub-dictionaries sampled from a coherent dictionary

作者:
Karin Schnass↗

arXiv:2606.24323v1 Announce Type: new Abstract: Motivated by the desire to find a realistic and stable random model for $d$-dimensional signals, that are sparse in a transform-based and thus often coherent frame, such as a wavelet or a Gabor frame, we study the conditioning of incoherent sub-dictionaries sampled from a coherent dictionary, such as a unit norm frame. In particular, we show that if the sub-dictionary is selected via a coherence rejective Poisson sampling model, it is well-conditioned with high probability, as long as its expected size scales as $d/\log (K)$, where $K$ is the number of dictionary elements. The result is proved for the more general case of sampling quadratic sub-matrices from a real but not necessarily symmetric $K\times K$ matrix with zero diagonal, where coherence rejective sampling is defined via a symmetric mask, that acts as coherence substitute.

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06.
arXiv (CS.LG) 2026-06-24

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

作者:
Sre\'cko {\DJ}ura\v{s}inovi\'c↗Jean-Bernard Lasserre↗Victor Magron↗

arXiv:2509.22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions. A key challenge, especially in high-dimensional settings, is to determine the mixture order and estimate the mixture parameters. We study the problem of approximating a target measure, available only through finitely many of its moments, by a mixture of distributions from a parametric family (e.g., Gaussian, exponential, Poisson), with approximation quality measured by the 2-Wasserstein or the total variation distance. Unlike many existing approaches, the parameter set is not assumed to be finite; it is modeled as a compact basic semi-algebraic set. We introduce a hierarchy of semidefinite relaxations with asymptotic convergence to the desired optimal value. In addition, when a certain rank condition is satisfied, the convergence is even finite and recovery of an optimal mixing measure is obtained. We also present an application to clustering, where our framework serves either as a stand-alone method or as a preprocessing step that yields both the number of clusters and strong initial parameter estimates, thereby accelerating convergence of standard (local) clustering algorithms.

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07.
arXiv (CS.AI) 2026-06-19

Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow

作者:
Liting Gao↗Yonggang Zhu↗Yaru Chen↗Dongyu Wang↗Shubin Zhang↗Zhenbo Li↗Jean-Yves Guillemaut↗Wenwu Wang↗

arXiv:2606.20101v1 Announce Type: cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remaining acoustic content. Despite the remarkable progress of diffusion models, existing training-based editing methods mainly rely on the local inductive biases and cross-attention interaction in convolutional U-Net backbones, which often hinder long-range semantic alignment and precise understanding and localization of instructions. In contrast, diffusion transformers provide stronger global modeling and multimodal fusion, but existing editing architectures usually adopt a simple stack of MMDiT and DiT blocks. Applying joint attention over concatenated audio and text tokens in all blocks results in quadratic complexity with respect to token length. To balance editing performance and efficiency, we propose a hybrid two-stage diffusion transformer architecture for instruction-guided audio editing based on rectified flow matching. It performs joint attention over audio and text tokens to establish coarse semantic alignment at low-resolution stage, then switches to alternating joint-attention and cross-attention blocks to refine editing details at high-resolution stage. This coarse-to-fine strategy enables efficient and accurate instruction-guided audio editing. Experiments show that the proposed framework achieves notable performance gains on challenging editing tasks involving overlapping audio events and complex instructions, while substantially improving editing efficiency with a compact model.

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08.
arXiv (quant-ph) 2026-06-24

Anomalous weak values in a generalized Mach-Zehnder interferometer extracted directly from intensity measurements

作者:
Ismaele V. Masiello↗Hartmut Lemmel↗Andreas Dvorak↗Stephan Sponar↗Yuji Hasegawa↗

arXiv:2606.24798v1 Announce Type: new Abstract: Weak values provide a powerful framework for characterizing quantum systems. Their experimental extraction conventionally relies on weak conditioned von Neumann measurements, involving weak interactions and meter states that increase experimental complexity and often limit measurement efficiency. Here we introduce a method to fully characterize path weak-values in a generalized Mach-Zehnder interferometer employing neither meter states nor weak interactions. We experimentally demonstrate the technique in matter-wave interferometry. We identify anomalous weak values and, equivalently, negative quasiprobability distributions, which reflect the nonclassical behavior of the quantum system. The approach relies uniquely on intensity measurements at the output ports of the interferometer combined with controlled relative phase shifts between the paths. The absence of meter states enables considerable simplification of the setup and shorter measurement times, while preserving full access to weak values with comparable or increased accuracy. The scheme is directly applicable to a broad class of experiments involving two-level quantum systems.

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09.
arXiv (CS.AI) 2026-06-16

Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models

作者:
Dongbin Na↗Chanwoo Kim↗Soonbin Rho↗Giyun Choi↗Gangbok Lee↗Dooyoung Hong↗

arXiv:2606.16902v1 Announce Type: cross Abstract: This work addresses spatial question answering for service robots traversing long egocentric routes. Given a query such as "where can I find a dry cleaner on the way back home?", the system returns a metric coordinate that downstream navigation components can act on. Prior Spatial Question Answering approaches leverage retrieval-augmented agents built on closed-source models such as GPT-4o for path exploration. However, robots operating in the real world often cannot reliably depend on online closed-source models due to network instability, communication latency, and deployment cost. It creates a need for open-source based Spatial Question Answering approaches that can run onboard the robot, yet prior research in this direction remains limited. This work proposes BinTrack, a simple yet effective, fully open-source spatial-localization agent that leverages the temporal ordering of a robot's trajectory. BinTrack performs a binary search over the trajectory segments between two anchor landmarks identified from a query. It improves overall accuracy by up to 22.8% over other open-source implementations and even matches the reported closed-source model result on the global category of the SpaceLocQA benchmark, the most challenging setting that has so far required strong reasoning agents such as GPT-4o. Furthermore, its optimized inference strategy consistently yields more than a 1.5x inference speedup over previous approaches. Finally, this work releases GangnamLoop, a novel and practical multi-trip outdoor benchmark collected by deploying a real quadruped robot on public streets with the anonymization policy. It revisits the same locations under different outdoor conditions and pairs the robot's low viewpoint with the human owner's. The source codes and datasets are publicly available at https://github.com/ndb796/BinaryTracking

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10.
arXiv (CS.AI) 2026-06-19

Robust $Q$-learning for mean-field control under Wasserstein uncertainty in common noise

作者:
Mathieu Lauri\`ere↗Ariel Neufeld↗Kyunghyun Park↗

arXiv:2606.20356v1 Announce Type: cross Abstract: In this article, we present a robust $Q$-learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law. The algorithm combines a quantization-and-projection scheme with a Wasserstein dual reformulation on the common-noise space. We establish its convergence together with finite-time iteration bounds for both synchronous and asynchronous learning schemes. Numerical experiments on systemic risk and epidemic models compare the asynchronous implementation with an idealized Bellman iteration, illustrate the robustness-performance tradeoff under common-noise misspecification, and report the observed convergence behavior of the asynchronous $Q$-learning algorithm.

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11.
arXiv (CS.AI) 2026-06-16

Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling

作者:
Yujin Park↗Haejun Chung↗Ikbeom Jang↗

arXiv:2606.15623v1 Announce Type: cross Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons ($O(n^2)$). While sorting-based methods have reduced this burden to $O(n\log n)$, they still require expensive human judgment for every single comparison. To further improve annotation efficiency, we propose leveraging a Vision-Language Model (VLM) not as an annotator replacement, but as a question prioritizer to identify which comparisons genuinely require human judgment. The proposed Surprise-Guided MergeSort (SGS) framework achieves this through three integrated components: (1) a bottom-up MergeSort scheduler that structures comparisons and exploits transitivity, (2) a composite Surprise Scorer – combining position-bias-cancelled VLM confidence, Elo gap, and vote entropy – to quantify comparison ambiguity, and (3) an adaptive budget allocator that routes high-surprise pairs to humans while automating low-surprise pairs via transitivity inference. Validation was conducted on six diverse benchmarks spanning text similarity (STS-B, BIOSSES, SICKR-STS) and image quality assessment (KonIQ-10k, TID2013, LIVE Challenge). SGS effectively identified and skipped up to 535 non-informative comparisons per session. Consequently, it achieved Kendall's $\tau{\times}100$ improvements of $+6$ to $+12$ over Active Elo under the same total budget. These results demonstrate that combining VLM-guided surprise metrics with algorithmic sorting provides a generally consistent accuracy-efficiency trade-off across diverse domains.

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12.
Science (Express) 2026-04-23

Structural N- and O-glycans revealed by high-resolution cryo-EM analysis of tubular mastigonemes | Science

作者: 未知作者

The chemical complexity and non-templated biosynthesis of glycans have posed significant challenges for establishing sequence-structure relationships. Here we report cryo-EM structures of tubular mastigonemes from a golden alga species, Ochromonas danica , in which a large number of N- and O-glycans are resolved at 1.8-2.2 Å resolution. Beyond high-mannose and complex N-glycans, we identify a non-canonical N-glycan on the Ala- Asn -Asp (A N D) motif. The surface spikes comprise dense O-glycans coating PSXX tetrapeptide repeats, with two glycans linked on trihydroxylated proline and one on serine per repeat. In addition to various types of sugars and their covalent modifiers, water molecules (>10% of resolved volume) and cations are clearly resolved and mediate the structural assembly. Our study establishes a framework for investigating glycan folding in high-order biological assemblies.

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13.
medRxiv (Medicine) 2026-06-24

Uncovering the fitness of endemically circulating Zika virus strains

作者:
Chen↗Fritz↗Clapham↗H. E↗Lefrancq↗Salje↗BUZZ study team↗

Zika virus (ZIKV) is an arbovirus that usually causes few symptoms and has circulated endemically in Asia for decades. However, a large outbreak in South America in 2015 uncovered the serious risk of congenital Zika syndrome in infants born from ZIKV infected mothers. It is unknown whether a lineage with distinct pre-existing fitness advantage emerged from Asia to cause the South American outbreak, and whether there is ongoing evolution that can result in future globally fit strains. Here we used 107 sequences from a single setting (Thailand) collected over an 18 year period (2006-2023). We used novel analytical tools to identify distinct lineages that have circulated in the population and estimated their relative epidemiological fitness. We found there have been six lineages circulating sequentially in the country, with regular emergence and replacement of lineages showing higher fitness than their predecessors. We identified 15 lineage-defining amino acid changes, including four well-documented fitness-enhancing mutations, and two UTR substitutions. The lineage that emerged in South America was evolutionarily linked to the highest-fitness lineage in Thailand, carrying seven of our lineage-defining substitutions acquired during endemic circulation there, and subsequently accumulating four additional changes. After the global pandemic, endemic ZIKV in Thailand continued to evolve, with newly emerged lineages showing novel mutations and increased fitness. Our findings have key implications for the monitoring of ZIKV and can help identify the pathway to increased transmissibility of this globally important pathogen.

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14.
arXiv (CS.LG) 2026-06-17

X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

作者:
Abdul Karim Gizzini↗Yahia Medjahdi↗

arXiv:2602.22277v2 Announce Type: replace Abstract: AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based eXplainable Artificial Intelligence (XAI) solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons. This enables a reliable optimization that identifies the most reliable model components. Simulation results demonstrate that X-REFINE achieves a superior performance-complexity-interpretability trade-off compared to the external perturbation-based XAI frameworks, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance.

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15.
arXiv (CS.AI) 2026-06-24

InSight: Self-Guided Skill Acquisition via Steerable VLAs

作者:
Maggie Wang↗Lars Osterberg↗Stephen Tian↗Ola Shorinwa↗Jiajun Wu↗Mac Schwager↗

arXiv:2606.24884v1 Announce Type: cross Abstract: Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training data. We present InSight, a framework that unlocks autonomous skill acquisition by rendering VLAs steerable at the primitive-action level (e.g., "move gripper to the bowl", "lift upward", "pour the bottle"). InSight consists of two primary stages: (1) an automated segmentation pipeline that partitions demonstrations into labeled primitives via VLM plan decomposition and end-effector poses to enable VLA primitive steerability, and (2) a VLM-guided data flywheel that identifies missing primitives required to accomplish a novel task, autonomously attempts demonstrations of the missing primitives with VLM-proposed low-level control, and automatically labels, stores, and integrates successful demonstrations into the VLA training set. We evaluate InSight across simulation and real-world manipulation tasks, including block flipping, drawer closing, sweeping, twisting, and pouring, without any human demonstrations of these target skills. Once learned, these primitives can be composed to execute novel, long-horizon tasks without additional human demonstrations. Our findings demonstrate that primitive steerability provides a practical foundation for continual skill acquisition in VLA policies. Project website: https://insight-vla.github.io.

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16.
arXiv (CS.CL) 2026-06-16

On Defining Erasure Harms for NLP

作者:
Yu Lu Liu↗Arnav Goel↗Jackie Chi Kit Cheung↗Alexandra Olteanu↗Ziang Xiao↗Su Lin Blodgett↗

The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one such harm, the harm of erasure. Nevertheless, the field lacks a clear and cohesive conceptual foundation for identifying and measuring erasure. Existing conceptualizations of erasure are often broad – making it difficult to identify what is needed to establish and measure erasure – or else specific to particular settings – facilitating measurement for those settings but potentially challenging to adapt to other settings. To address this gap, we develop and propose a structured definition of erasure that clarifies what components are necessary for establishing whether erasure has occurred, which practitioners need to explicitly articulate and operationalize in order to measure erasure.

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17.
arXiv (quant-ph) 2026-06-12

Cayley's First Hyperdeterminant is an Entanglement Measure

作者:
Isaac Dobes↗Naihuan Jing↗

arXiv:2504.15511v2 Announce Type: replace Abstract: Previously, it was shown that both the concurrence and $n$-tangle on $2n$-qubit pure quantum states can be expressed in terms of Cayley's first hyperdeterminant [dobes2024qubits], indicating that Cayley's first hyperdeterminant, denoted $\mathrm{hdet}$, captures some aspects of a state's $2n$-way entanglement. In this paper, we rigorously prove that on both pure and mixed states, $|\mathrm{hdet}|^{2/d}$ is identically zero on separable states, is an LU invariant, and is non-increasing on average under LOCC, thus demonstrating that $|\mathrm{hdet}|^{d/2}$ is a physically meaningful and legitimate entanglement measure. Moreover, we discuss a few key examples to illustrate the particular type of entanglement Cayley's first hyperdeterminant is detecting: genuine full $d$-level GHZ-type entanglement across all $2n$ parties. Combined, this establishes Cayley's first hyperdeterminant (or $|\mathrm{hdet}|^{2/d}$ to be precise), as a genuine, physically significant generalization of the concurrence and the $n$-tangle to $2n$-qudit states.

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18.
arXiv (quant-ph) 2026-06-11

Q-DICE: Quantum Distributed Interconnect Compiler and Emulator

作者:
Michael Silver↗Zachary Vernec↗Hans-Arno Jacobsen↗

arXiv:2606.11340v1 Announce Type: new Abstract: As distributed quantum computing (DQC) offers a leading path towards scalable quantum computation, the ability to benchmark distributed algorithms under realistic conditions becomes critical for system co-design. However, without access to physical systems, researchers lack tools to evaluate distribution protocols. We introduce Q-DICE (Quantum Distributed Interconnect Compiler and Emulator), a hardware-aware emulation environment for benchmarking distributed quantum circuits on classical simulators and on NISQ-era monolithic hardware. This work provides three core contributions: (1) a programmatic scheme to construct distributed QPU backends, utilizing two novel techniques - QPU slicing and stitching - to facilitate distributed circuit mapping, (2) a methodology for modeling nonlocal link noise using physically motivated Kraus operators and stochastic error channels, and (3) a boundary-aware circuit mapping algorithm enforcing distributed QPU topology constraints during transpilation. Together, these components constitute a distribution-aware compiler and noise-modeling engine that faithfully enforces the physical limitations of distributed quantum hardware within existing execution environments. We validate Q-DICE against a multitude of experimentally demonstrated quantum circuits, including a distributed Grover's search on optically linked trapped-ion hardware, achieving a worst-case fidelity deviation of 4% between simulated and experimental results. These findings demonstrate Q-DICE's capacity to accurately reproduce real distributed quantum system behavior across platforms, streamlining experimentation with distributed quantum algorithms and architectures.

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19.
medRxiv (Medicine) 2026-06-16

Investigating naming error patterns after non-invasive brain stimulation and language treatment in persons with aphasia

作者:
Sydnor↗M. J↗Johnson↗M. A↗Lammers↗Murter↗J. L↗Lindquist↗Sebastian↗

Abstract Background: Transcranial direct current stimulation (tDCS) paired with behavioral language therapy can improve naming in persons with aphasia (PWA), yet naming errors persist. Little is known about how naming error patterns change after non-invasive brain stimulation is combined with language treatment. Aims: To examine whether right cerebellar tDCS plus computerized aphasia therapy changes the types of naming errors in people with chronic aphasia across timepoints, and to determine whether effects differ by cerebellar tDCS polarity (anode vs. cathode). Methods and Procedures: In a randomized, double-blind, sham-controlled, within-subject crossover study, we retrospectively analyzed behavioral data from 24 individuals with post-stroke aphasia. Each participant completed two 15-session intervention periods (3-5 sessions/week) with active cerebellar tDCS + computerized aphasia therapy and sham + computerized aphasia therapy, separated by a two-month washout. General linear models (GLMs) assessed longitudinal changes in six error types (semantic, phonological real word, phonological nonword, no response, mixed, unrelated) on an untrained picture naming task (Philadelphia Naming Test; PNT) and a trained task (Naming 80; N80). Additional GLMs evaluated polarity effects with 2 (Group: anode vs. cathode) x 2 (Treatment) interactions, and treatment-order effects with 2 (Group: tDCS-first vs. sham-first) x 2 (Treatment) interactions. Outcomes and Results: Active cerebellar tDCS did not significantly change error types for trained items (N80). For untrained items (PNT), active tDCS reduced several error types relative to sham, with the clearest and most durable reduction in phonological nonword errors; more moderate reductions occurred for phonological real word and unrelated errors. Mixed errors showed a marginally opposite pattern, tending to increase after tDCS and decrease after sham. Polarity analyses indicated broadly similar effects across anodal and cathodal stimulation overall, but only the anode group showed a reliable treatment effect for phonological nonword errors on the PNT. Treatment-order analyses revealed no significant order effects. Conclusions: Our results indicate a shift in naming error types, particularly after tDCS treatment for the untrained naming task (PNT). These findings may help guide the course of treatment approaches of those with aphasia and what error naming pattern types may show changes post stroke when combining non-invasive brain stimulation and computerized aphasia therapy. Clinical Trial Registration: Cerebellar Transcranial Direct Current Stimulation and Aphasia Treatment [NCT02901574] Keywords: aphasia, naming errors, non-invasive brain stimulation, cerebellar tDCS, computerized aphasia treatment

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20.
arXiv (quant-ph) 2026-06-24

Analysis of the frequency shift in coherent population trapping resonance's dynamic continuous-wave spectroscopy at the phase-jump modulation and its comparison with the conventional approach

作者:
E. D. Chivilis↗E. A. Tsygankov↗

arXiv:2606.23908v1 Announce Type: cross Abstract: We present the research of dynamic continuous-wave spectroscopy of the coherent population trapping resonance at the phase-jump modulation. {\Lambda} system of levels supplemented by a nonabsorbing state and bichromatic optical field, whose spectral components have different intensities, are considered. We demonstrate that the asymmetry leads to an additional nonlinear shift of the error-signal frequency under unisotropic relaxation of the ground-state density-matrix elements. We also investigate the conventional approach where the frequency difference of the optical field components is harmonically modulated to obtain the error signal. Comparison demonstrates that in the high-frequency modulation regime the corresponding frequency shift is more linear than at the phase-jump modulation for nonshort integration times.

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21.
arXiv (CS.AI) 2026-06-16

JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks

作者:
Lanbo Lin↗Jiayao Liu↗Tianyuan Yang↗Li Cai↗Yuanwu Xu↗Lei Wei↗Sicong Xie↗Guannan Zhang↗

arXiv:2602.06486v2 Announce Type: replace Abstract: Evaluating agentic AI on open-ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail to accommodate diverse valid response strategies, while LLM-as-a-judge approaches adapt to individual responses yet suffer from instability and bias. Human experts address this dilemma by combining domain-grounded principles with dynamic, claim-level assessment. Inspired by this process, we propose JADE, a two-layer evaluation framework. Layer 1 encodes expert knowledge as a predefined set of evaluation skills, providing stable evaluation criteria. Layer 2 performs report-specific, claim-level evaluation to flexibly assess diverse reasoning strategies, with evidence-dependency gating to invalidate conclusions built on refuted claims. Experiments on BizBench show that JADE improves evaluation stability and reveals critical agent failure modes missed by holistic LLM-based evaluators. We further demonstrate strong alignment with expert-authored rubrics and effective transfer to HealthBench and DR.BENCH, covering medical and 10-domain professional evaluation settings. Code and data are available at https://github.com/smiling-world/JADE.

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22.
arXiv (CS.CV) 2026-06-11

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization

作者:
Hao Lu↗Yongxin Guo↗Onur Koyun↗Zhengjie Zhu↗Abbas Alili↗Metin N. Gurcan↗

Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify encoder drift as a key driver of this failure: as the encoder moves the latent distribution, sparsely updated code vectors can lag behind, lose assignments, and increase quantization error, creating a feedback loop through the straight-through estimator. We propose NSVQ, a non-stationary-aware VQ training strategy that combines a dense non-stationary embedding loss, codebook replacement, and stage-wise encoder freezing. NSVQ first helps the codebook track encoder drift during early training, then freezes the encoder to consolidate the codebook under a fixed latent geometry, and finally reintroduces adversarial refinement. Experiments on ImageNet-1k show that NSVQ improves reconstruction quality while maintaining full codebook utilization. On ImageNet-1k at 128$\times$128 with 65,536 codes, NSVQ reduces rFID from 2.39 to 2.10 compared with SimVQ, while both methods maintain 100\% utilization. Additional latent diffusion experiments show that NSVQ also improves downstream ImageNet generation FID.

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23.
arXiv (CS.LG) 2026-06-12

Metriplectic Conditional Flow Matching for Dissipative Dynamics

作者:
Ali Baheri↗Lars Lindemann↗

arXiv:2509.19526v2 Announce Type: replace Abstract: Metriplectic conditional flow matching (MCFM) learns dissipative dynamics without violating first principles. Neural surrogates often inject energy and destabilize long-horizon rollouts; MCFM instead builds the conservative-dissipative split into both the vector field and a structure preserving sampler. MCFM trains via conditional flow matching on short transitions, avoiding long rollout adjoints. In inference, a Strang-prox scheme alternates a symplectic update with a proximal metric step, ensuring discrete energy decay; an optional projection enforces strict decay when a trusted energy is available. We provide continuous and discrete time guarantees linking this parameterization and sampler to conservation, monotonic dissipation, and stable rollouts. On a controlled mechanical benchmark, MCFM yields phase portraits closer to ground truth and markedly fewer energy-increase and positive energy rate events than an equally expressive unconstrained neural flow, while matching terminal distributional fit.

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24.
arXiv (quant-ph) 2026-06-12

Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection

作者:
Pauli Taipale↗

arXiv:2606.13244v1 Announce Type: new Abstract: Selecting anomalous samples and explanatory features under fixed budgets defines a coupled constrained-optimization problem. Sequential feature-first selection ranks features before choosing samples, which can overlook features whose utility depends on which samples are selected, especially when scores are calibrated from reference data that may be limited, noisy, or drifting. We instead formulate the task as joint sample-feature selection under the same fixed counts. In the analyzed formal model, calibration-error sensitivity grows linearly with the number of samples for feature-first ordering but stays constant for joint selection. We introduce Coupling-Grouped XY-QAOA, a constraint-preserving grouped-angle variant for the resulting optimization problem. On matched sparse IBM Heron R3 benchmarks, a hardware-aware implementation reduces circuit depth by 45.9%-61.3% and two-qubit gates by 2.6%-5.2% relative to Qiskit optimization level 3 on the CZ-basis target. It enables, to our knowledge, the largest reported width-depth configurations for constraint-preserving bipartite-selection QAOA hardware executions with feasible-sector retention: 64 qubits at p=2 and 36 qubits at p=3. The 20-qubit p=5 runs retain 63% valid samples. Across 36-64 qubits, fixed-angle runs yield lower-energy feasible samples than matched random-feasible sampling. Warm starts reduce the gap to strict-feasible classical references by 57.5%-80.5%, and near-budget repair matches the sparse classical reference at 36 qubits. Benchmarks show gains in balanced fixed-budget regimes, and noiseless simulations show that problem-structured angle grouping improves over same-depth XY-QAOA and matched-parameter, type-preserving randomization controls. Overall, the results support calibrated joint selection and hardware-realizable constrained-mixer execution in the tested regimes.

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25.
arXiv (CS.AI) 2026-06-24

Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation

作者:
Hongjun Liu↗Leyu Zhou↗Zijianghao Yang↗Chao Yao↗

arXiv:2606.23707v1 Announce Type: cross Abstract: EEG spatial super-resolution (EEGSR) in real deployments is challenged by random channel missingness, unstable electrode quality, and changing visible-channel patterns caused by bad contacts or device variability. Most existing EEGSR methods learn a fixed low-to-high channel mapping under pre-defined input-output layouts, which makes them brittle when missing channels vary at test time. In this paper, we reformulate EEGSR as learning a shared conditional scalp field from partially observed support channels. Specifically, a position-guided encoder summarizes the observed EEG channels and their coordinates into a latent condition, and a conditional implicit neural representation decoder reconstructs target EEG signals by querying this condition at desired electrode coordinates. During inference, the model directly reconstructs unseen electrode signals from the available EEG support and the queried coordinates. To strengthen the constraint of the encoded latent representation on the decoder and thereby construct a more stable scalp field consistent with the observed channels, we further introduce a fidelity-preserving channel corruption training strategy under mixed electrode states. Extensive experiments across multiple EEG datasets demonstrate the effectiveness of our framework for both random missing-channel reconstruction and strict unseen-electrode signal generation. Notably, under the strict held-out-electrode setting on AAD, our method reduces NMSE by 37.5\% and improves SNR by 2.12 dB over the strongest baseline, showing its ability to synthesize signals at electrode locations never exposed during training.

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