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01.
arXiv (math.PR) 2026-06-16

On stability of outliers from the circular law

arXiv:2606.16609v1 Announce Type: new Abstract: This work investigates the stability of outliers from the circular law, via the convergence of their associated diagonal overlaps between eigenvectors - also known as the squared eigenvalue condition numbers. We consider and compare two paradigmatic cases, namely: 1) the Complex Ginibre Ensemble conditioned on the existence of an outlier, and 2) the outlier induced by a rank-one Hermitian perturbation of a Complex Ginibre matrix. In both cases, we prove almost sure convergence towards a specific constant that only depends on the radius of the outlier and its status - either conditioned or induced. These results can be generalized to other complex integrable ensembles with the same techniques, and complement our understanding of eigenvalue stability in non-Hermitian ensembles.

02.
medRxiv (Medicine) 2026-06-12

Reduced nighttime smartphone use among cohabiting partners: a longitudinal study under the lens of social control of health behaviors theory

Objective: We examined the link between cohabitation with a partner and nighttime smartphone use through the social control of health behavior theory. Background: Nighttime smartphone use is a behavioral risk factor for sleep problems. While previous research has predominantly focused on individual-level risks of sleep disturbances, the role of social context remains underexplored. Theoretical frameworks, specifically the Social Control of Health Behavior, suggest that social relationships regulate health-related behaviors; however, it is unclear how far this regulation extends to modern digital behaviors among couples. Method: We analyzed survey data from three waves of the SmartSleep Study (2018, 2020, and 2023; total N = 25,028), including a longitudinal follow-up subset (N = 1,003). We tested multivariate associations between living with a partner, changes in cohabitation status and frequent nighttime smartphone use by fitting generalized linear mixed-effects models. Additionally, we mapped the complex interplay between indicators of social integration, social support, smartphone use, and sleep quality using hierarchical clustering of non-linear correlations. Results: Cohabiting participants had lower odds of frequent nighttime smartphone use compared to those living alone (OR = 0.66; 95% CI: 0.61, 0.72). This lower risk was driven primarily by cohabitation with a partner (OR = 0.49; 95% CI: 0.36, 0.66). Longitudinal analysis supported these findings, showing that sustained cohabitation was associated with less frequent nighttime use (OR = 0.56; 95% CI: 0.38, 0.82). Clustering analysis revealed that indicators of social integration and support clustered with favorable sleep quality. Conclusion: Our findings suggest that the health-protective effects of cohabitation with a partner extend to digital behaviors. Consistent with social control of health behavior theory, the presence of a partner appears to reduce frequent nighttime smartphone use, highlighting the critical importance of considering social context when addressing digital health hygiene and promoting sleep.

03.
arXiv (CS.CL) 2026-06-16

Agentic Retrieval and Reinforcement Learned Equation Chains: A Controlled Generation Framework for Complex and Novel Physics Word Problems

Generating high-quality Physics Word Problems (PWPs) that are novel, complex, and solvable remains a challenging and underexplored problem in educational content generation. Existing approaches, many adapted from Math Word Problem (MWP) generation, often produce ambiguous, unsolvable, or structurally simple questions with limited linguistic diversity. We introduce ARVRE (Agentic Retrieval Value Reinforced Equation-chain), a two-stage framework for generating diverse and mathematically valid PWPs. In the first stage, a form of offline temporal-difference learning is used to construct valid chains of physics equations, while an agentic retrieval-augmented generation (RAG) framework dynamically selects topic-specific concepts and vocabulary. This design enables explicit control over problem structure and difficulty. In the second stage, a Large Language Model (LLM) converts the equation chain and retrieved concepts into a natural-language physics question. By grounding generation in valid equation chains, our method preserves mathematical correctness while promoting linguistic diversity and contextual richness. Human and automated evaluations demonstrate that ARVRE generates PWPs that are more complex, novel, and solvable than those produced by existing approaches. These results highlight the potential of combining reinforcement learning, retrieval, and LLMs for reliable generation of educational physics content.

04.
bioRxiv (Bioinfo) 2026-06-12

PHI-Reason: evidence-grounded species-level phage-host prediction from structured biological text profiles

Phage–host interaction (PHI) prediction is a fundamental problem in microbiology with applications in microbial ecology and microbiome engineering. Existing computational approaches typically convert phage and host information into numerical representations derived from sequence similarity, protein content, genome composition or reference databases, then score candidate hosts or train host-prediction models. Although effective, such representations often make it difficult to inspect which biological evidence supports a prediction. Here, we present PHI-Reason, a species-level PHI prediction framework that reformulates host prediction as constrained biological text reasoning. Instead of embedding phages and hosts directly as numerical vectors, PHI-Reason converts heterogeneous PHI-related evidence from phage genomes, host genomes, functional annotations, homology searches and biological metadata into modular natural-language profiles. A frozen large language model then performs species-level candidate-host ranking or pairwise PHI assessment by integrating the supplied evidence at inference time. Across species-level benchmarks, PHI-Reason achieved competitive host-prediction performance and recovered complementary correct assignments relative to established sequence- and reference-based methods. Its explicit profile design enabled systematic evidence perturbation and rationale-grounding analyses, showing that predictions depend on coherent multi-source biological evidence and that hallucination risk from unsupported or incomplete profiles can be made operationally measurable. These results position PHI-Reason as a constrained evidence-integration framework for species-level PHI prediction. Rather than replacing sequence-based predictors, it provides an interpretable layer that shows how far explicit biological evidence can support host inference, and where that evidence falls short.

05.
arXiv (CS.AI) 2026-06-17

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models

arXiv:2606.18114v1 Announce Type: cross Abstract: State Space Models (SSMs) such as Mamba-2 offer linear-time inference but their memory footprint limits edge deployment. Prior ternary SSM work (Slender-Mamba) trains from scratch on 150B tokens; we show a pretrained checkpoint suffices, reducing the marginal token budget by 1,000x. Using grouped quantization-aware training (QAT) with knowledge distillation from a frozen FP16 teacher, we compress Mamba-2 1.3B to 3.61x (2,687 to 744 MB) and achieve 48.1% zero-shot accuracy (7-task average) in just 102M tokens (4 GPU-hours, single H100) – approaching Bi-Mamba's 48.4% (within +/-0.9pp CI). This QAT-from-pretrained setting reveals zero-ratio collapse, a novel instability caused by learnable quantization scales that does not arise in from-scratch training. We further show that post-hoc correction strategies effective for Transformers fail for SSMs due to error accumulation through the recurrence. These results demonstrate that ternary SSMs do not require expensive from-scratch training: QAT from pretrained checkpoints with KD is a data-efficient alternative.

06.
bioRxiv (Bioinfo) 2026-06-14

Structural Analysis of Prostate Cancer N-Glycans Using Graph-Based Structural Metrics

The N-linked glycans are structurally complex carbohydrate modifications that regulate protein folding, immune recognition, and cellular signaling, and their expression is extensively remodeled during cancer progression, making them promising biomarkers. In this study, prostate cancer-associated N-glycans from a range of relevant peer-reviewed studies were curated and digitized to develop a versatile computational framework that quantitatively encodes their spatial complexity across diverse biological systems. We invented two indices – the Distance & Connectivity Index (DCI) and the Position & Composition Index (PCI) – to capture the spatial information in N-glycans as layered architectures, enabling calculation of residue-level path lengths, branching structure, and compositional diversity. DCI summarizes glycan structure as both a scalar and matrix representation, while PCI does the same but also captures monosaccharide diversity, linkage heterogeneity, and cross-layer branching features. These metrics were computed with GlycoAssessor, an open-source platform that extracts information for the DCI and PCI from glycans drawn via Symbol Nomenclature for Glycans (SNFG) notation. Principal Component Analysis (PCA) was applied to evaluate whether glycans from prostate cancer tissues cluster distinctly in a disease-relevant manner. Results show that the spatial information in N-glycans: (1) increased in a multi-dimensional, non-linear manner, (2) objectively segregated structural themes, (3) could function as a potential prostate cancer biomarker that is distinct from mass-to-charge ratio and relative abundance, and (4) could objectively quantify novel subtype classifications of glycans associated with disease states and progression.

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

We Need to Rethink Benchmarking in Anomaly Detection

arXiv:2507.15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms. In this position paper, we argue that this stagnation is due to limitations in how we evaluate anomaly detection algorithms. In current benchmarks, a trivial algorithm that only checks for extreme values in individual features performs competitively with state-of-the-art deep learning methods, despite failing on simple cases such as anomalies within an annulus of normal points. Moreover, existing benchmarks do not adequately reflect the diversity of anomaly detection applications, making it difficult for practitioners to reliably select algorithms for their applications. Consequently, we need to rethink benchmarking in anomaly detection. In our opinion, anomaly detection should be studied using scenarios that group applications sharing relevant characteristics, defined through a common taxonomy. Benchmarking within scenarios enables scenario-specific choices for preprocessing, metrics, and model selection, clarifying which advances transfer across similar applications and providing practitioners with reliable guidance for their specific contexts.

08.
medRxiv (Medicine) 2026-06-17

Performance of five risk stratification tools for paediatric pneumonia against WHO scores using data from the PediCAP trial in sub-Saharan Africa

Background Risk stratification tools for childhood pneumonia have been proposed to improve identification of children at highest risk of death, particularly in low-resource settings. However, their added value over the WHO Integrated Management of Childhood Illness (IMCI) criteria and danger signs remains uncertain. Methods We conducted a secondary analysis of a multi-country randomised controlled trial of children without HIV hospitalised with pneumonia in Mozambique, South Africa, Uganda, Zambia, and Zimbabwe. We evaluated the performance of five published risk scores alongside WHO IMCI severity classification and danger signs. Discrimination for (1) in-hospital mortality, (2) 28-day mortality, and (3) 28-day readmission or death was assessed using area under the receiver operating characteristic curve (AUC). Comparative performance and clinical utility were examined. Results Of the 1010 participants, 18 (1.8%) died in hospital, 22 (2.2%) died in hospital or in the 7 days post-discharge, and 63 (6.2%) died or were readmitted by day 28. Univariate case-fatality rates were highest for variables associated with malnutrition, convulsions, and hypoxaemia. All risk scores demonstrated moderate discrimination for in-hospital and in-hospital+7-day mortality (AUC range approximately 0.75-0.84), with no meaningful differences between models, and performed similarly to the WHO danger signs and IMCI severity classification. In contrast, all approaches performed poorly in predicting 28-day readmission or death (AUC approximately 0.54-0.58). No risk score consistently outperformed simple clinical criteria. Conclusions In this multi-country dataset, we found no evidence that published paediatric pneumonia risk scores meaningfully outperform WHO IMCI-based clinical assessment for predicting mortality. The relatively small number of mortality events limits precision, and modest differences cannot be excluded. These findings suggest that, in low-resource settings, strengthening implementation of existing WHO clinical criteria may be more effective than adopting more complex prediction tools.

09.
arXiv (CS.CL) 2026-06-19

TerraMARS: A Domain-Adapted Small-Language-Model Pipeline for Mars Terraforming Literature

Researchers are interested in learning about Mars so that it may eventually become habitable for humans. To achieve this, there is a need for comprehensive knowledge of the planet's atmosphere, hydrology, surface chemistry, radiation environment, and spatial features through the scientific literature. These contain valuable information and meaningful quantitative constraints that can be used in other models and studies, such as habitability assessment and future terraforming studies. We present TerraMARS, an end-to-end information extraction pipeline that combines a domain-adapted Small Language Model to answer Mars terraforming-related questions and convert unstructured Mars science text into machine-readable structured outputs in JavaScript Object Notation (JSON) format. A corpus of open-access papers is collected and processed using a multistage retrieval and chunking framework. Google Gemma 3 1B was adapted to the domain using Quantized Low-Rank Adaptation (QLoRA) fine-tuning on Mars-specific question-answering and information extraction datasets. The resulting pipeline generates both types of output and provides a foundation for integrating knowledge from scientific literature into downstream applications like digital twins and habitability modeling for Mars. The output from this pipeline looks promising, but further improvements are needed to increase extraction accuracy and factual consistency.

10.
arXiv (CS.CL) 2026-06-12

Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning

Latent chain-of-thought compresses reasoning by replacing visible reasoning traces with continuous hidden-state recurrence, but existing formulations are difficult to optimize with standard on-policy reinforcement learning (RL) and hard to interpret causally. Our key insight is that a single pair of explicit boundary tokens can address both issues at once: discrete entry and exit anchors make the latent block compatible with standard on-policy RL, and the same anchors offer a natural foothold for mechanistic analysis. Motivated by this, we propose SWITCH, a switchable latent reasoning framework. The model emits to enter latent mode and to exit. Because the boundaries are ordinary discrete tokens, the GRPO policy ratio is well-defined at every decision point. The same anchors also expose the latent steps to direct probing and causal intervention. We train the model with a visible-to-latent curriculum and a Switch-GRPO objective that propagates gradients through recurrent latent computation. SWITCH consistently outperforms prior hidden-state-recurrence latent reasoning approaches at similar scale. Mechanistic analysis through the boundary tokens further reveals three findings: (i) is a sharply localised, learned switching policy rather than a stylistic artefact; (ii) the latent step it opens performs problem-specific, causally important computation rather than acting as an inert placeholder; and (iii) that computation is concentrated at a single hidden-state transition on entry. Together, these results show that hidden-state-recurrence latent reasoning is both RL-trainable and open to direct mechanistic analysis, including of how on-policy RL itself improves the model from the inside.

11.
arXiv (CS.CV) 2026-06-12

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.

12.
arXiv (CS.LG) 2026-06-17

Geometrical fairness in graph neural networks

arXiv:2606.17684v1 Announce Type: cross Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications. Among these, recent frameworks grounded in diffusion processes provide a unifying perspective that extends traditional graph neural network formulations while addressing limitations of standard message-passing mechanisms. Despite these advances, concerns remain regarding the fairness of such models, as they may propagate or amplify biases present in the data. In this work, we introduce a fairness-aware adaptation of graph-based diffusion by modifying the underlying Laplacian operator. Our approach incorporates multiple complementary transformations, including subspace projections, spectral adjustments, and frequency-based filtering, to mitigate bias-related components. Leveraging the intrinsic smoothing properties of graph diffusion, we provide a principled analysis of the resulting behavior and establish theoretical insights into fairness properties. We evaluate the proposed framework on both synthetic and real-world datasets, demonstrating that it achieves competitive performance while improving fairness metrics with limited additional computational cost.

13.
medRxiv (Medicine) 2026-06-15

Natural Language Processing Based Solution for Labeling Brain Metastasis Identified in Radiology Reports

Abstract Purpose: Brain metastases (BM) far exceed primary CNS tumours and constitute the majority workload for neuro-oncology care providers. Currently, the cancer registries only capture synchronous BMs, which is only a small proportion of all BMs. We aim to develop and validate a natural language processing (NLP) algorithm that identifies brain metastases in radiology reports, enabling scalable surveillance of asynchronous BMs. Methods: Using population-based cancer registry data in Alberta, Canada, we identified a cancer cohort diagnosed between 2012–2019 with follow-up to 2022. All brain/head radiology reports at and post-cancer diagnosis were identified. Reports were sampled through a multi-phase approach and manually labeled for BM presence. We trained two Bio_ClinicalBERT models on the "Findings" and "Impressions" sections, respectively, and took the maximum predicted probability as the report-level prediction. Internal and external validation used reports from the Canadian provinces of Alberta, Ontario, and British Columbia. Results: The models were trained on 1,879 samples. For internal validation, 1,833 reports from 357 patients were tested. At a probability threshold of 0.4, the model achieved a sensitivity of 0.888 and precision of 0.499. The ensemble substantially outperformed single-section models, which achieved sensitivities of only 67.8% (Findings) and 74.2% (Impressions). On external validation, sensitivity was 0.918 in Ontario and 0.726 in British Columbia, demonstrating robustness across diverse data distributions. Conclusions: An NLP-based pipeline processing both Findings and Impressions sections has been developed and validated in three Canadian provinces. It meets cancer registry operational requirements and to be implemented into the surveillance workflow in Alberta and British Columbia, providing a foundation for population-level BM surveillance.

14.
arXiv (CS.CL) 2026-06-16

Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals

Physiological stress and emotion recognition are important for health monitoring and affective computing. In this work, we present a comprehensive evaluation of deep learning models such as Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer on the WESAD dataset for multimodal affect recognition using wrist and chest sensor signals. We perform ablation studies to assess the individual contributions of each modality by training models on wrist-only and chest-only inputs. In addition, we implement a late-fusion ensemble strategy that combines predictions from all three architectures trained on multimodal input. We also employ early fusion at the sensor level by concatenating wrist and chest signals before feeding them into each model. Our results show that Transformer models consistently achieve the highest accuracy in multimodal settings, while TCN models perform best in the wrist-only configuration. The ensemble method yields the highest overall accuracy (98.91 +/- 0.13%) and macro-F1 score (98.56 +/- 0.17%). These findings demonstrate the effectiveness of sensor fusion and ensemble-based fusion in developing robust systems for physiological emotion recognition.

15.
arXiv (quant-ph) 2026-06-16

Stronger Entanglement Dies Faster: Quantum Mpemba Effect in Dissipative Qubits

arXiv:2605.23197v3 Announce Type: replace Abstract: In classical thermodynamics, the Mpemba effect refers to the counterintuitive observation that hot water can freeze faster than cold water, manifesting as an anomalous crossing of dynamical trajectories. While analogues of this phenomenon have been explored in open quantum systems and spin-chain entanglement asymmetry, its connection to the finite-time decoupling of quantum correlations remains elusive. In this work, we report a distinct Mpemba effect for quantum entanglement in a dissipative quantum system associated with entanglement sudden death (ESD). By analyzing two qubits interacting with local amplitude damping reservoirs, we demonstrate that a more strongly entangled initial state can experience a faster collapse into a separable state than a more weakly entangled state. This anomalous decay stems from the competition between initial coherence and excited-state population, where the latter acts as a catalyst for ESD. We provide exact analytical derivations for the trajectory crossover and ESD time, and map the phase diagram to precisely identify the parameter regime where the effect occurs. Our results offer a new strategy for controlling the lifetime of quantum resources in dissipative environments.

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

Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification

arXiv:2606.16987v1 Announce Type: new Abstract: Accurate Harmonized Tariff Schedule (HTS) code classification is essential for customs clearance, duty assessment, trade statistics, and regulatory compliance in maritime logistics. However, exact HTS classification remains challenging because product descriptions are often short, incomplete, or ambiguous, while correct classification depends on hierarchical tariff structures, legal notes, and jurisdiction-specific rules. This paper proposes an agentic large language model (LLM) framework for Canadian 10-digit HTS code classification in smart-port and maritime logistics environments. The framework integrates multi-agent information retrieval, semantic retrieval over official tariff documents, evidence-grounded reasoning, consensus-based validation, element-wise voting across hierarchical code components, confidence estimation, and human-in-the-loop escalation. We evaluate the framework on a private dataset of 3,300 domain-expert-labeled product records collected from logistics and delivery contexts. Experimental results show that exact 10-digit classification remains difficult even for advanced LLMs, with performance decreasing from coarse chapter-level prediction to fine-grained tariff and statistical suffix assignment. These findings demonstrate the need for evidence-grounded, uncertainty-aware, and human-centered classification workflows rather than fully autonomous single-step prediction. The proposed framework supports more interpretable, accountable, and compliance-oriented HTS classification for maritime logistics and smart-port operations. Our code is available at https://github.com/Analytics-Everywhere-Lab/hts.

17.
arXiv (CS.CV) 2026-06-18

Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation

Action-conditioned world models have emerged as a promising paradigm for robot learning, offering a scalable alternative to costly real-world experimentation by generating action-consistent video rollouts. However, persistent world modeling remains challenging in manipulation: frequent end-effector occlusions and rapid wrist-camera motion make the current observation insufficient for predicting future views, causing models to forget or hallucinate scene details seen in earlier frames. Existing memory retrieval strategies often fail to identify informative history in dynamic manipulation scenarios. To address this limitation, we propose Mem-World, a memory-augmented multi-view action-conditioned world model. At its core, we present W-VMem, a 4D wrist-view-centered surfel-indexed memory that anchors historical observations to temporally evolving surface elements. By explicitly modeling when and where scene elements are observed, W-VMem enables geometry-aware retrieval of relevant history frames conditioned on future actions. During generation, relevant history frames are selected via surfel-based rendering and scoring, providing informative and non-redundant context for prediction. Extensive experiments show that Mem-World generates persistent rollouts in complex manipulation scenarios, enables more reliable policy evaluation than Ctrl-World, improving the Pearson correlation with real-world performance by 14.5\%, and supports effective policy improvement through synthetic data generation, increasing success rates from 58\% to 72\% on long-horizon tasks.

18.
arXiv (CS.AI) 2026-06-16

Discovering Lattice Reduction Strategies via Self-Play

arXiv:2606.15301v1 Announce Type: cross Abstract: The Lenstra-Lenstra-Lovász (LLL) algorithm is a seminal contribution to computer science used for lattice basis reduction, yet its polynomial-time outputs produce bases that are far from optimal as the dimension grows. We show that deep reinforcement learning can discover strictly superior, generalizable reduction strategies by interacting with the primitive action space of LLL. We formulate lattice reduction as a single-player Markov Decision Process (MDP) and train a deep residual network using an AlphaZero-style self-play pipeline augmented with adaptive-horizon MCTS (Monte Carlo Tree Search), which couples multi-step network predictions with an entropy-gated expansion mechanism. The resulting policy, DeltaStar, is trained exclusively on small $8$-dimensional $q$-ary lattices and requires fewer primitive row operations than LLL. Crucially, it generalizes zero-shot to unseen moduli and higher dimensions up to $n=32$ without retraining.

19.
arXiv (quant-ph) 2026-06-16

Retrocausal capacity of a quantum channel: Communicating through noisy closed timelike curves

arXiv:2509.08965v3 Announce Type: replace Abstract: We study the capacity of a quantum channel for retrocausal communication, where messages are transmitted backward in time, from a sender in the future to a receiver in the past, through a noisy postselected closed timelike curve mathematically represented by the channel. We completely characterize the one-shot retrocausal quantum and classical capacities, and we show that the corresponding asymptotic capacities are equal to the average and sum, respectively, of the channel's max-information and its regularized Doeblin information. This endows these information measures with a novel operational interpretation. Furthermore, our characterization can be generalized beyond quantum channels to all completely positive maps. This imposes information-theoretic limits on transmitting messages via postselected-teleportation-like mechanisms with arbitrary initial- and final-state boundary conditions, including those considered in various black-hole final-state models.

20.
arXiv (CS.CV) 2026-06-11

OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglement

Supervised Contrastive Learning (SupCon) has achieved strong performance by explicitly modeling pairwise relationships among samples. However, existing SupCon-based methods suffer from two key limitations: negative-sample dilution induced by the standard InfoNCE loss, and feature-space entanglement caused by the lack of explicit constraints separating category-relevant (common) and category-irrelevant (style) features. These limitations reduce feature discriminability and generalization ability. To address these issues, we propose OSCS-SupCon (Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning), a unified framework that combines a sigmoid-based pairwise contrastive objective with explicit orthogonality constraints. Specifically, we introduce a sigmoid-based contrastive loss with two learnable parameters, temperature and bias, which adaptively modulate pairwise decision boundaries and alleviate negative-sample dilution. Furthermore, we enforce orthogonality between common and style feature subspaces via a linear projection with ReLU nonlinearity, thereby reducing feature overlap and improving disentanglement of style-irrelevant representations. Extensive experiments on six benchmark datasets demonstrate that OSCS-SupCon consistently outperforms state-of-the-art supervised contrastive learning methods across multiple backbone architectures. In particular, on the fine-grained CUB200-2011 dataset with a ResNet-18 backbone, the proposed method achieves a 3.4% improvement in classification accuracy over CS-SupCon, highlighting its robustness and generalization capability. Ablation studies further confirm the effectiveness of each component.

21.
arXiv (CS.CL) 2026-06-12

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision-Language-Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.

22.
arXiv (CS.CL) 2026-06-12

Authorship Attribution in Multilingual Machine-Generated Texts

As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult. While early efforts in MGT detection have focused on binary classification, the growing landscape and diversity of LLMs require a more fine-grained yet challenging authorship attribution (AA), i.e., being able to identify the precise generator (LLM or human) behind a text. However, AA remains nowadays confined to a monolingual setting, with English being the most investigated one, overlooking the multilingual nature and usage of modern LLMs. In this work, we introduce the problem of Multilingual Authorship Attribution, which involves attributing texts to human or multiple LLM generators across diverse languages. Focusing on 18 languages – covering multiple families and writing scripts – and 8 generators (7 LLMs and the human-authored class), we investigate the multilingual suitability of monolingual AA methods in terms of their cross-lingual transferability, and the impact of generators on attribution performance. Our results reveal that while certain monolingual AA methods can be adapted to multilingual settings, significant limitations and challenges remain, particularly in transferring across diverse language families, underscoring the complexity of multilingual AA and the need for more robust approaches to better match real-world scenarios.

23.
arXiv (quant-ph) 2026-06-11

Quantum optimal control of the Dicke manifold in dipolar Rydberg atom arrays

arXiv:2606.02283v2 Announce Type: replace Abstract: The ability to engineer and control quantum states of many-body systems is a central challenge in quantum information science. For a register of $N$ qubits, the full Hilbert space dimension grows exponentially as $2^N$, rendering generic state preparation and control infeasible without exploiting structure or symmetry. A particularly important and physically motivated restriction is to the fully symmetric subspace, spanned by the Dicke states, which are simultaneous eigenstates of collective spin $J=N/2$. Ensembles of Rydberg atoms interacting via electric dipoles in two-dimensional tweezer arrays form a promising platform for achieving such control. However, the finite range of dipole-dipole interactions poses a challenge to generating and controlling the Dicke manifold because the Hamiltonian incurs leakage from the computational subspace. To counteract this leakage, we perform quantum optimal control algorithms on a truncated Hilbert space according to our newly developed method of ``irrep distillation'' (IRD), which captures the process by which the symmetric subspace couples to leakage error-spaces, using only linear-scaling Hilbert dimension. We implement gradient ascent pulse engineering (GrAPE) on control schemes with little or no local addressing, to generate resourceful states like Greenberger-Horne-Zeilinger, Dicke, and extremal quantum states. We benchmark each scheme of IRD-GrAPE for its quantum speed limit (QSL), as well as exactly testing pulse fidelities on small system sizes and predicting fidelities using higher-order IRD on larger systems.

24.
medRxiv (Medicine) 2026-06-15

Investigation of Intra-Fraction Stability and Inter-Fraction Reproducibility of Deep Inspiration Breath-Hold Across Two Hypofractionated Radiotherapy Regimens in the HYPORT Adjuvant Study.

Background: Deep Inspiration Breath Hold (DIBH) is a widely used respiratory motion management technique for minimizing cardiac dose in left-sided breast radiotherapy. In the Breast HYPORT Adjuvant study, DIBH was employed for cardiac sparing in patients without nodal irradiation using a standardized institutional protocol with the Varian Real-time Position Management (RPM) system. Both moderate-hypofractionation (control arm - 40Gy in 15 fractions) and one-week hypofractionation (experimental arm - 26 Gy in 5 fractions) regimens were delivered using this protocol. This study aimed to evaluate the robustness of DIBH by analyzing intra-fraction stability and inter-fraction reproducibility of breath-hold amplitude across the two treatment regimens. Methods: Respiratory waveforms acquired during each treatment session were analyzed to determine the median breath-hold amplitude and its standard deviation during beam delivery. Intra-fraction stability was assessed from vari- ations within individual treatment sessions, while inter-fraction reproducibility was evaluated relative to the simula- tion waveform amplitude across all treatment sessions. These parameters were compared between the two HYPORT regimens to examine breath-hold consistency during treatment delivery. Moreover, an additional comparison was made between the one-week hypofractionation regimen and the first five fractions of the moderate-hypofractionation regimen to evaluate the effect of treatment duration . Lung volumes from free-breathing and DIBH CT scans were analyzed to assess the effectiveness of patient breath-hold training. Results: Both arms demonstrated an average 1.7-fold increase of air volume in lung during the breath-hold position, confirming the effective implementation of DIBH during treatment planning and delivery. Structured training resulted in increased breath-hold amplitudes, with gains of 22.87% and 24.16% with respect to the first trial session in the experimental and control arms, respectively. Both regimens receive equivalent doses for approximately the same air volume in lung . Despite the different prescription doses in the two arms (26 Gy vs. 40 Gy), the experimental arm achieved an equivalent mean heart dose of 2.91% (75.6 cGy) compared with 2.95% (118.51 cGy) in the control arm, suggesting a similar cardiac preservation protocol adopted during treatment planning. Intra-fraction stability was similar between the control arm and the experimental arm, with median amplitude variations of 1.006 mm (95% CI: [0.998-1.015]) and 1.079 mm (95% CI: [1.067-1.097]), respectively. In contrast, inter-fraction reproducibility improved in the experimental arm, with lower deviation from simulation amplitude (0.44 {+/-} 0.24 mm vs. 0.66 {+/-} 0.25 mm) for the entire treatment schedule. The stability and reproducibility of experimental arm were further compared with the first five fractions of the control arm. The results were similar to those of the experimental arm. Conclusion: In this study, we compared two treatment regimens in terms of intra-fraction stability and inter-fraction reproducibility during DIBH radiotherapy. Both regimens demonstrated comparable intra-fraction stability, indicating effective motion management irrespective of treatment duration. However, the experimental arm showed better inter- fraction reproducibility, suggesting more consistent breath-hold performance throughout the treatment course. Based on stability and reproducibility, a reasonable narrowing of the DIBH gating window may be implemented with minor changes to the institutional protocol. The observed trend highlights the potential for improved consistency with the experimental approach and supports further investigation to better understand the underlying factors and strengthen these findings in future studies.

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arXiv (CS.CL) 2026-06-12

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks

As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the trade-off between performance and efficiency without retraining is critical. We propose Drop-by-Drop, a novel multi-bitwidth post-training quantization framework that enables inference-time precision control over LLM weights from a single trained model. Our method is theoretically grounded in information theory and successive refinement. We establish that LLM weights, which commonly follow a Gaussian distribution, can be optimally reconstructed with increasing fidelity as additional bits are incorporated, under a weighted mean squared error distortion motivated by LLM loss functions. To realize this in practice, Drop-by-Drop incorporates Matryoshka-style supervision into the loss function, exploiting the structure of additive codebooks. Drop-by-Drop produces a single model where ordered subsets of codebooks yield accurate partial reconstructions at each precision level. This approach significantly reduces storage and memory overhead by allowing a single checkpoint to serve multiple bitwidths, while maintaining competitive perplexity and accuracy across major architectures, such as Qwen, LLaMA, Gemma, and Mistral.