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01.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11199

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

作者:

We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track. Rather than targeting benchmark maximization, this work proposes a principled pipeline that decomposes knowledge synthesis into three coordinated phases: retrieval, curation, and composition, each governed by explicit intermediate representations and handoff contracts. Inspired by Agentic Context Engineering (ACE), the system introduces temporal-semantic reranking, bounded contradiction reconciliation, and citation-preserving composition as core architectural primitives. Competition results show that NightFeats surpasses proprietary baselines including Claude-SonnetV2 and Nova-Pro on LLM-as-a-Judge and Human Likert evaluations, confirming that architectural transparency and verifiable evidence grounding are better aligned with human preferences than systems optimizing narrowly for automatic similarity metrics.

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02.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.13507

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data

作者:

Qixu Chen ↗Satoshi Nakamura ↗

Large-scale mined corpora provide abundant training data for end-to-end speech-to-speech translation (S2ST) but may contain noise, misalignment, and semantic errors. Filtering noisy data is crucial to maintain robust speech translation performance. We study how to train an audio-language model to make keep/drop decisions on paired speech directly from audio. To obtain reliable supervision without manual labels, we adopt a scalable two-stage Rank-to-Distill strategy. A lightweight ranker generates keep/drop pseudo-labels from noisy speech pairs, then trains an audio large language model to predict keep/drop directly from raw paired speech. The resulting model jointly captures acoustic fidelity and cross-lingual semantic consistency for the selection of speech-conditioned data. Experiments on CVSS-C and SpeechMatrix show consistent improvements over unfiltered training, yielding up to +1.4 ASR-BLEU for end-to-end S2ST.

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03.

Science (Express) 2026-05-07 DOI: 10.1126/science.aec8514

TranscriptFormer: A generative cell atlas across 1.5 billion years of evolution | Science

作者: 未知作者

Single-cell transcriptomics is revolutionizing our understanding of cellular diversity, yet comparing transcriptional programs across the tree of life remains challenging. We developed TranscriptFormer, a family of generative foundation models trained on up to 112 million cells spanning 1.53 billion years of evolution across 12 species. We demonstrate state-of-the-art performance on cell type classification, even for species separated over 685 million years of evolution, and zero-shot disease state identification in human cells. Developmental trajectories, phylogenetic relationships and cellular hierarchies emerge naturally in TranscriptFormer’s representations without any explicit training on these annotations. This work establishes a powerful framework for quantitative single-cell analysis and comparative cellular biology, thus demonstrating that universal principles of cellular organization can be learned and predicted across the tree of life.

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04.

arXiv (CS.CV) 2026-06-17 DOI: arXiv:2606.17958

Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation

作者:

Yuming Chen ↗Yuxin Xie ↗Tao Zhou ↗Yi Zhou ↗

Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data. However, existing approaches operate predominantly via visual pattern matching, relying heavily on pixel-level similarities. This visual-centric dependency often falters in clinical scenarios characterized by the visual-semantic mismatch, where visually similar lesions warrant distinct diagnostic conclusions, thus failing to capture the underlying diagnostic logic used by experts. To address this, we move beyond visual cues and propose CERS (CoT-Enhanced Reasoning Segmentation), a framework that integrates Chain-of-Thought (CoT) reasoning to distinguish pathologically distinct cases. Specifically, we construct a knowledge pool enriched with linguistic reasoning descriptions generated by large language models (LLMs). A semantic-aware reference selection strategy is introduced to identify historical evidence, filtering candidates first by morphology, and then refining them via CoT consistency to eliminate hard negatives. Furthermore, a multi-scale coordinate attention module (MCAM) is designed to effectively fuse this reasoning-derived context into the decoding process. Extensive experiments demonstrate the superiority of CERS against state-of-the-art approaches, particularly in resolving boundary ambiguities and semantic inconsistencies. The code is available at https://github.com/cymasuna/CERS.

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05.

arXiv (CS.CL) 2026-06-17 DOI: arXiv:2606.17634

Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs

作者:

Dong Huang ↗Jianbo Sun ↗Pengkun Yang ↗

Evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice. For open-ended tasks, pairwise evaluation has become a popular paradigm, in which two responses to the same prompt are compared and the resulting judgments are aggregated into an overall ranking. A central challenge of this paradigm is intransitivity: the induced comparison outcomes may fail to support any coherent global ranking. For example, one may observe cyclic preferences such as $A \succ B \succ C \succ A$, or inconsistencies involving ties such as $A \equiv B\equiv C\neq A$. Such contradictions make the resulting leaderboard unstable and challenging to interpret. In this paper, we propose a prompt perturbation framework for improving the consistency of pairwise LLM evaluation. Our approach generates perturbed variants of each prompt, uses the resulting comparison graphs to identify and filter out structurally inconsistent comparison patterns, and then applies standard ranking methods to the filtered comparisons. A key feature of the proposed framework is that graph-level structural consistency is incorporated explicitly into the evaluation pipeline before ranking aggregation. This provides a simple and principled way to reduce cyclic inconsistencies and improve the reliability of LLM rankings.

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06.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2601.06468

Emission of time-ordered photon pairs from a coherently-driven Kerr microcavity

作者:

arXiv:2601.06468v2 Announce Type: replace-cross Abstract: Weakly-interacting many-body systems possess remarkable quantum properties that are essential components of quantum technologies, and constitute a topic of fundamental interest. Here we show that in a solid-state nonlinear microcavity embedding discrete modes of exciton-dressed photons, we can isolate a single eigenmode of quantum fluctuations from the much brighter coherent fraction of the field. In this regime, we perform frequency- and time-resolved correlations measurements between photons on the red and blue side of the fluctuations spectrum. When the average number of fluctuation quanta is smaller than one, we observe the formation of large pairwise time-ordered correlations: red photon first and blue photon second. We show that this peculiar time-ordering correlation emerges spontaneously from the interplay between frequency-resolved detection, and the non-trivial internal quantum structure of the elementary fluctuations.

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07.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14725

Interpolation between Convolution and Attention via K-Nearest Neighbors

作者:

Mingi Kang ↗

The shift from Convolutional Neural Networks to Transformers has reshaped computer vision, yet these two architectural families are typically viewed as fundamentally distinct. Convolutional Neural Networks are defined by spatially local convolution operations, while Transformers rely on global self-attention. We argue that convolution and self-attention, despite their apparent differences, can be unified within a single k-nearest neighbor aggregation framework. The critical insight is that both operations are special cases of neighbor selection and weighted aggregation. Convolution selects neighbors by spatial proximity while self-attention selects by feature similarity, revealing that they lie on a continuous spectrum rather than representing categorically different computations. We introduce Convolutional Nearest Neighbors (ConvNN), a unified framework that formalizes this connection. ConvNN exactly recovers standard and depthwise convolution by restricting neighbor selection to normalized spatial coordinates, and exactly recovers self-attention and its sparse variants, including KVT-attention, by replacing spatial proximity with scaled dot-product similarity. Beyond these special cases, ConvNN serves as a drop-in replacement for both convolution and attention layers, enabling systematic exploration of the intermediate spectrum between local and global aggregation through configurable similarity functions, neighbor selection strategies, positional encodings, and aggregation kernels.

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08.

arXiv (CS.CL) 2026-06-17 DOI: arXiv:2606.17522

An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars

作者:

Vinoth Nandakumar ↗Qiang Qu ↗Pramod Thebe ↗Sakshi Khachariya ↗Tongliang Liu ↗

Deep neural networks are widely believed to derive their expressive power from their ability to form hierarchical representations, capturing progressively more abstract and compositional features across layers. In language modeling, transformers have emerged as the dominant architecture, with early layers capturing local syntactic patterns and later layers encoding more complex clause-level dependencies. While this intuition has shaped model design, there remains a lack of rigorous theoretical work demonstrating how deep transformers represent such hierarchical structures. In this work, we analyze the expressiveness of deep transformer models through the formal lens of bounded-depth, non-recursive context-free grammars. For this class of grammars, we explicitly construct transformers with positional attention whose depth grows linearly with grammar depth, while the neuron count scales with the number of derivation-tree shapes and quadratically with the number of production rules. Our theoretical results support the linear representation hypothesis by demonstrating that these architectures possess the structural capacity to encode abstract grammatical states into low-dimensional, linearly separable subspaces within the residual stream.

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09.

arXiv (CS.CV) 2026-06-15 DOI: arXiv:2606.14703

Gaze Heads: How VLMs Look at What They Describe

作者:

Rohit Gandikota ↗David Bau ↗

How a vision-language model internally solves the task of describing an image is far from obvious. We find that the model develops a specific mechanism for this: a small set of attention heads in its language-model backbone, which we call gaze heads, whose attention tracks the image region the model is currently describing. We find them with a simple correlation score from a few forward passes, using comic strips as a controlled testbed where narrative order is laid out spatially. These gaze heads do not just track the image tokens being described: redirecting their attention to a chosen region forces the VLM to describe that region instead. A single attention-mask intervention on the top-100 gaze heads, fewer than 9% of all heads, steers the model's answer to any chosen comic panel at 83.1% accuracy, while the same intervention on random heads fails to redirect the answer, and intervening on all heads destroys generation. The same lever also extends to continuous control: switching the gaze target mid-generation makes the model wrap up its current panel description and move to the new one within a few tokens. Beyond comics, the same intervention redirects answers to chosen regions in natural COCO images. The mechanism further recurs across model sizes from 2B to 32B parameters and across other VLM architectures, although some frozen-encoder families show no comparable head set. More broadly, this shows that targeted edits identified through mechanistic analysis can serve as practical inference-time levers for steering multimodal model behavior, without any retraining. Our code, interactive demo, and datasets are available at https://gaze.baulab.info/

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10.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2605.10157

MolSight: Molecular Property Prediction with Images

作者:

Aaditya Baranwal ↗Akshaj Gupta ↗Yogesh S Rawat ↗Shruti Vyas ↗

Every molecule ever synthesised can be drawn as a 2D skeletal diagram, yet in modern property prediction this universally available representation has received less focus in favour of molecular graphs, 3D conformers, or billion-parameter language models, each imposing its own computational and data-engineering overhead. We present $MolSight$, the first systematic large-scale study of vision-based Molecular Property Prediction (MPP). Using 10 vision architectures, 7 pre-training strategies, and $2\,M$ molecule images, we evaluate performance across 10 downstream tasks spanning physical-property regression, drug-discovery classification, and quantum-chemistry prediction. To account for the wide variation in structural complexity across pre-training molecules, we further propose a $chemistry-informed curriculum$: five structural complexity descriptors partition the corpus into five tiers of increasing chemical difficulty, consistently outperforming non-curriculum baselines. We show that a single rendered bond-line image, processed by a vision encoder, is sufficient for competitive molecular property prediction, i.e. $chemical insight from sight alone$. The best curriculum-trained configuration achieves the top result on $5 of 10$ benchmarks and top two on $all 10$, at $$80$\times$ lower$$ FLOPs than the nearest multi-modal competitor.

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11.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2604.02751

Understanding Latent Diffusability via Fisher Geometry

作者:

Jing Gu ↗Morteza Mardani ↗Wonjun Lee ↗Dongmian Zou ↗Gilad Lerman ↗

arXiv:2604.02751v2 Announce Type: replace Abstract: Diffusion models often degrade in latent spaces, yet the formal causes remain poorly understood. We quantify latent-space diffusability via the rate of change of the Minimum Mean Squared Error (MMSE) along the diffusion trajectory. Our framework decomposes this MMSE rate into contributions from Fisher Information (FI) and Fisher Information Rate (FIR). We demonstrate that while global isometry ensures FI alignment, FIR is governed by the interplay between encoder and data geometries. Our analysis decouples diffusion degradation into four penalties: dimensional compression, tangential distortion, high-frequency encoder curvature, and intrinsic data curvature. We derive theoretical conditions for FIR preservation to ensure stable diffusability. Experiments across diverse autoencoding architectures demonstrate the implications of our theoretical bounds. We establish FI and FIR as a comprehensive analytical framework for understanding latent diffusability.

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12.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2604.09998

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

作者:

Souradip Nath ↗Chih-Yi Huang ↗Aditi Ganapathi ↗Kashyap Thimmaraju ↗Jaron Mink ↗Gail-Joon Ahn ↗

arXiv:2604.09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs. However, there remains a limited empirical understanding of how such tools are used, perceived, and adopted by real-world security practitioners. To address this gap, we conduct a mixed-methods analysis of discussions in cybersecurity-focused forums to learn how a diverse group of practitioners use and perceive modern LLM tools for security operations. More specifically, we analyzed 892 posts between December 2022 and September 2025 from three cybersecurity-focused forums on Reddit, and, using a combination of qualitative coding and statistical analysis, examined how security practitioners discuss LLM tools across three dimensions: (1) their stated tools and use cases, (2) the perceived pros and cons of each tool across a set of critical factors, and (3) their adoption of such tools and the expected impacts on the cybersecurity industry and individual analysts. Overall, our findings reveal nuanced patterns in LLM tools adoption, highlighting independent use of LLMs for low-risk, productivity-oriented tasks, alongside active interest around enterprise-grade, security-focused LLM platforms. Although practitioners report meaningful gains in efficiency and effectiveness in LLM-assisted workflows, persistent issues with reliability, verification overheads, and security risks sharply constrain the autonomy granted to LLM tools. Based on these results, we also provide recommendations for developing and adopting LLM tools to ensure the security of organizations and the safety of cybersecurity practitioners.

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13.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2606.18782

RedactionBench

作者:

Sean Brynj\'olfsson ↗Shashvat Jayakrishnan ↗Esha Sali ↗Diptanshu Purwar ↗Madhav Aggarwal ↗

Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII). While redacting PII is a data cleaning prerequisite, existing benchmarks conflate extraction mechanics with privacy semantics. A public phone number is not equivalent to a phone number in a medical record. Whether information constitutes a violation depends heavily on who holds it, why, and in what context, fundamentally differentiating redaction from simple entity recognition. Grounded in contextual integrity, we introduce RedactionBench, a manually annotated benchmark comprising 200 diverse documents across 11 domains, mostly seeded from real-world sources. We also introduce R-Score, a novel character-level metric that treats semantically similar redactions equally and nullifies shallow formatting choices, such as varying masking styles for phone numbers. Evaluations across Named Entity Recognition models, entity extraction Small Language Models, and frontier models equipped with agentic tools demonstrate that contextual redaction remains an unsolved problem. A human evaluation with over 80 users on RedactionBench reveals a stark dichotomy in privacy perceptions. Annotators show consensus with target labels for mandatory redactions (89.4 percent) and safe text preservations (94.1 percent), but fail to agree on contextual redactions (47.7 percent). This variance demonstrates the subjective nature of contextual privacy and motivates R-Score, which decouples contextual ambiguity from strict precision. We compare 35 models across families and report their performance in redacting PII. Finally, we release RedactionBench to establish a baseline for future privacy-preserving systems, hoping to inspire efficient model design and standardized evaluations.

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14.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.13748

FedSPC: Shared Parameter Correction for Personalized Federated Learning

作者:

Kannanthodath Induchoodan Ajay Menon ↗Christian Prehofer ↗Yunfei Xu ↗Toru Hirano ↗

arXiv:2606.13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.

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15.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2506.06010

Magnetic control of an exciton-polariton condensate in a van der Waals magnet

作者:

arXiv:2506.06010v3 Announce Type: replace-cross Abstract: Quasiparticle condensates are among the most spectacular solid-state manifestations of quantum physics. Coupling macroscopic real-space wavefunctions to additional degrees of freedom, such as the electron spin, would add valuable control knobs for quantum applications. While creating spin-carrying superconducting condensates has attracted enormous attention, man-made condensates of light-matter hybrids known as exciton-polaritons have lacked an analogous spin-based perspective. Here we open a new door by demonstrating magnetically tunable exciton-polariton condensation in the van der Waals magnet CrSBr. Under photoexcitation, CrSBr microwires embedded in an optical cavity show the hallmarks of polariton condensation: a dramatic increase of the emission intensity from an excited laterally confined polariton state by multiple orders of magnitude, spectral narrowing of the emission line, and a continuous shift of the peak energy. Interferometry evidences an increase in spatial and temporal coherence. Owing to the strong coupling between the spin order and excitonic correlation, the energy of the condensate can be tuned by up to 10.5 meV by an external magnetic field of only 2 Tesla. Our results establish CrSBr microcavities as a powerful platform for exploring magnetic control of polariton condensates and mark a significant step toward spin-controlled coherent quantum light sources.

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16.

medRxiv (Medicine) 2026-06-16 DOI: HASH:43c052820bca0f6476da51137b21e080

Doctors, Wellness Influencers, and Probiotic Gummies: A Cross-Sectional Analysis of Gut Health Claims and Financial Conflicts on TikTok

作者:

Vu ↗Garcia-Rogers ↗Li ↗Swaroop ↗Gilani ↗Tang ↗A. M ↗Siegel ↗

TikTok has emerged as a major source of health information, yet concerns persist regarding the accuracy of content and influence of financial conflicts. Gut health content is particularly vulnerable to misinformation. This study examined the relationship between creator profession ("medical" versus "non-medical") and the quality of gut health claims and the presence of financial conflicts on TikTok. We conducted a cross-sectional study of 412 TikTok creator accounts identified using the search terms "guthealth," "gutcleansing," and "digestion." One video per creator was analyzed. Creator profession was categorized as medical or non-medical. Health claim quality was coded as high, moderate, or poor. Financial conflicts (Showcase, Subscription, external links) were assessed. Modified Poisson regression was used to estimate prevalence ratios (PRs) of health claim quality (high versus poor- or moderate-quality) and financial conflicts between medical and non-medical creators, and negative binomial regression was used to evaluate associations between claim quality and number of video likes. Non-medical creators were more likely than medical creators to present poor- or moderate-quality health claims (adjusted PR: 2.33; 95% CI: 1.50-3.62). Most creators (92%) exhibited at least one financial conflict, and Showcase use was greater among non-medical creators (adjusted PR: 1.57; 95% CI: 1.02-2.42). Videos containing moderate- and poor-quality health claims received three times as many likes as videos containing high-quality claims. Non-medical creators disproportionately produced lower-quality gut health content on TikTok, and misleading claims received greater engagement. These findings highlight a misalignment between information quality and visibility, emphasizing the need for interventions promoting evidence-based health communication.

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17.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.12068

StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse

作者:

Kholoud K. Aldous ↗Md Rafiul Biswas ↗Mabrouka Bessghaier ↗Shimaa Ibrahim ↗Kais Attia ↗Wajdi Zaghouani ↗

We present StanceNakba 2026, a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict, organized as part of Nakba-NLP 2026 at LREC-COLING 2026. The task introduces two subtasks: Subtask A (Actor-Level Stance Detection), which classifies English social media posts as Pro-Palestine, Pro-Israel, or Neutral; and Subtask B (Cross-Topic Stance Detection), which identifies Favor, Against, or Neither stances in Arabic posts toward two conflict-related topics, normalization with Israel and refugee presence in Jordan. The task is grounded in an annotated dataset of 2,606 social media posts. A total of 7 teams participated in Subtask A and 6 teams in Subtask B. Participating systems primarily fine-tuned Arabic and multilingual transformer-based models, including MARBERT, AraBERT, and DeBERTa-v3 variants, with several teams employing cross-validation, ensemble methods, and topic-conditioned architectures. The best-performing systems achieved a Macro F1 of 0.9620 on Subtask A and 0.8724 on Subtask B, demonstrating that transformer-based approaches are highly effective for conflict-domain stance detection while highlighting persistent challenges in cross-topic generalization and neutral class prediction.

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18.

arXiv (CS.CV) 2026-06-18 DOI: arXiv:2606.18555

Rethinking Text-to-Image as Semantic-Aware Data Augmentation for Indoor Scene Recognition

作者:

Trong-Vu Hoang ↗Quang-Binh Nguyen ↗Dinh-Khoi Vo ↗Hoai-Danh Vo ↗Minh-Triet Tran ↗Trung-Nghia Le ↗

In the realm of computer vision, indoor image recognition presents challenges due to the intricate interplay of lighting conditions, occlusions, and diverse object arrangements within confined spaces. To address the lacks of training indoor images, we introduce a novel approach leveraging Stable Diffusion (SD) for the generation of synthetic images, which serve as a powerful data augmentation tool. The utilization of SD offers a principled framework for synthesizing diverse and realistic indoor scenes, thereby enriching the training data pool for robust indoor image recognition models. Experimental findings on the MIT Indoor Scene dataset reveal the potential of our proposed approach in enhancing the training of deep models when authentic data is limited. Furthermore, to prevent the misuse of SD synthetic images, we introduce a counter measure based on DIffusion Reconstruction Error (DIRE). The powerful DIRE presentation enables training robust classifiers only using lightweight deep models. Experiments show that our approach can perfectly recognize SD generated images with the accuracy of 100% using MobilenetV3.

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19.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11429

Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains

作者:

Zilai Wang ↗Natarajan Balaji Shankar ↗Mohan Shi ↗Kaiyuan Zhang ↗Abeer Alwan ↗

Speech foundation models often struggle in low-resource domains due to domain mismatch and data scarcity. We propose Gumbel-BEARD, a domain adaptation framework that automates Whisper encoder layer selection via an end-to-end trainable hard Gumbel-Softmax selector. It enables self-supervised adaptation with a BEST-RQ objective that dynamically adapts to target acoustic characteristics without manual tuning. Experiments on the MyST child speech corpus demonstrate efficiency and scalability: with 10 h of labeled data for fine-tuning, our method matches a fully supervised baseline trained on the complete 133 h labeled set. We establish new state-of-the-art word error rates (WERs) of 8.21% using Whisper-medium on MyST and 11.06% using Whisper-small on the OGI Spontaneous dataset. Evaluation on CORAAL further confirms robustness to adult dialectal domain shifts, with up to 6% relative WER reduction, highlighting the generalizability of our approach to diverse low-resource conditions.

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20.

medRxiv (Medicine) 2026-06-12 DOI: HASH:b8d85219779eb06d30e21224cfd45430

Cancer care disruption during the COVID-19 pandemic in Ontario, Canada: A sequential mixed-methods study

作者:

Timilshina ↗Jacobson ↗Birze ↗Wodchis ↗W. P ↗Kuluski ↗Strumpf ↗Ammi ↗

Introduction The COVID-19 pandemic profoundly disrupted healthcare delivery worldwide, with cancer care among the most affected services. Prior studies documented delays in referrals, reduced specialist access, and increased provider burden. However, the extent to which these experiences were reflected at the system level remains unclear. Objective To document cancer care experiences and examine whether these experiences were reflected in population-level health system indicators across Ontario, Canada. Methods We used an exploratory sequential mixed-methods design. Qualitative data were collected through focus groups and semi-structured interviews with 32 participants, including patients with cancer (n=8), caregivers (n=5), healthcare providers (n=14), and decision-makers (n=5) across two hospital settings in Ontario, Canada. Emergent themes informed the development of quantitative indicators. We then conducted a retrospective population-based analysis of linked administrative health databases for cancer patients in Ontario (n=87,786) to assess the prevalence of identified themes. Results Four themes emerged: (I) delays in diagnosis and screening; (II) disrupted access to primary care; (III) barriers to specialist and mental health services; and (IV) fragmented care for patients with multimorbidity. Quantitative findings corroborated major themes. Screening rates declined for cervical (64.8% to 57.5%) and breast cancer (64.5% to 57.2%). While in-person primary care shifted almost entirely to virtual modalities (8.5% to 95.4%), overall visit volumes remained stable. Specialist care showed uneven patterns, with increased oncology visits but declines in cardiology and mental health services. Patients with multiple comorbidities experienced the largest reductions in non-oncology specialist care. Conclusion The pandemic disrupted key components of cancer care, particularly screening, access to certain specialist services, and care for patients with complex needs. Integrating qualitative and quantitative evidence highlights areas of system vulnerability and underscores the need for coordinated, resilient cancer care capable of maintaining essential services during future crises.

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21.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2601.03040

PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms

作者:

Arup Kumar Sahoo ↗Itzik Klein ↗

arXiv:2601.03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pure inertial navigation. However, the inherent noise and other error terms of the inertial sensors in such real-world scenarios will cause the navigation solution to drift over time. Although conventional deep-learning models have emerged as a possible approach to inertial navigation, they are inherently black-box in nature. Furthermore, they struggle to learn effectively with limited supervised sensor data and often fail to preserve physical principles. To address these limitations, we propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms in situations of pure inertial navigation. PiDR offers transparency by explicitly integrating inertial navigation principles into the network training process through the physics-informed residual component. PiDR plays a crucial role in mitigating abrupt trajectory deviations even under limited or sparse supervision. We evaluated PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle. We obtained more than 29% positioning improvement in both datasets, demonstrating the ability of PiDR to generalize different platforms operating in various environments and dynamics. Thus, PiDR offers a robust, lightweight, yet effective architecture and can be deployed on resource-constrained platforms, enabling real-time pure inertial navigation in adverse scenarios.

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22.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.20110

FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model

作者:

Yuhwan Jeong ↗Hyeonseong Kim ↗Daehyun We ↗Seonkyu Song ↗Jinnyeong Yang ↗Hyun-Kurl Jang ↗Youngho Yoon ↗Kuk-Jin Yoon ↗

Synthetic data for autonomous driving is surging, powered by diffusion models that promise scalable scene generation. Yet key obstacles remain, as enforcing multi-view and temporal consistency often relies on backbone fine-tuning or added layers, which erodes pre-trained knowledge and weakens text alignment. Models also stay close to the training distribution, struggling under adverse weather and unseen configurations, and fidelity favors frequent over rare classes. We address these gaps with FrozenDrive, a controllable generative framework that preserves a pretrained diffusion models knowledge while achieving strong consistency. FrozenDrive conditions on rich driving-stack signals and text prompts, and introduces knowledge-preserving spatio-temporal attention to impose cross-view alignment and temporal coherence in a single pass within a parameter-free frozen diffusion backbone. An additional object-focused constraint improves per-object fidelity for rare categories. Without any weather- or scene-specific fine-tuning, our model synthesizes globally coherent multi-view driving scenes from text, particularly under adverse and rare conditions, and surpasses prior baselines. On nuScenes, FrozenDrive augmented data significantly improves AD models performance, especially at night and in rain, demonstrating stronger robustness when trained with our scenario-targeted data.

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23.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2605.16713

GeoWorld-VLM: Geometry from World Models for Vision-Language Models

作者:

Renjie Gu ↗Kaichen Zhou ↗Yan Luo ↗Mengyu Wang ↗

Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behind, and between. One cause of this failure arises before language reasoning begins: the visual pathway may compress or discard critical 3D structural cues during feature extraction, so the language model receives image representations that are already insufficient for reliable spatial judgment. We introduce GeoWorld-VLM, a VLM-side distillation framework that transfers geometric structure from frozen camera-conditioned video world models into VLMs. GeoWorld-VLM fine-tunes only the image encoder and multimodal projector, aligning post-projector image features with intermediate world-model representations while leaving the main backbone frozen. Given images, a prompt, and a sampled camera trajectory, the world-model teacher converts static visual input into a synthetic multi-view spatial signal. Training combines spatial answer supervision, teacher-student feature alignment, and a preservation anchor to the original VLM. Since the language model remains frozen, GeoWorld-VLM preserves the original model's linguistic capabilities while attributing spatial improvements to the enhanced visual pathway. To evaluate the effectiveness and generality of the proposed method, we apply GeoWorld-VLM to two distinct VLM architectures and observe consistent improvements across both backbones. GeoWorld-VLM improves performance by approximately 4 percent on both the What'sUp and VSR benchmarks, suggesting that world-model-guided visual alignment generalizes across model structures and spatial reasoning datasets.

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24.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2606.19170

Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition

作者:

Shiho Matta ↗Yin Jou Huang ↗Fei Cheng ↗Takashi Kodama ↗Hirokazu Kiyomaru ↗Yugo Murawaki ↗

We introduce Dango, a 1.8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition (SLA). While previous studies have explored SLA in language models, they have predominantly relied on smaller or non-decoder models, limiting their ability to generate open-ended text and reducing their suitability as practical L2 simulators. We identify a key challenge when scaling models to this size: L2 contamination within the "monolingual" pretraining corpus used for L1 acquisition. To address this, we propose a filtering method to reduce premature exposure to English while preserving realistic, minimal exposure. We then fine-tune the model on LLM-generated L2-learning lessons to simulate the L2 acquisition process. Our evaluations confirm that Dango develops human-like L2 production patterns, outperforming both unfiltered and standard multilingual baselines. We release the model, data, and code to facilitate reproducible computational SLA research and learner-facing applications.

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25.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2606.19912

Structure-Oriented Randomized Neural Networks for Poisson-Nernst-Planck and Poisson-Nernst-Planck-Navier-Stokes Systems

作者:

Yunlong Li ↗Fei Wang ↗

arXiv:2606.19912v1 Announce Type: cross Abstract: We develop a structure-oriented randomized neural network framework, termed SO-RaNN, for the Poisson-Nernst-Planck (PNP) system and the Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system. The decoupled linearized subproblems are solved iteratively by randomized neural networks in a space-time framework. For the concentration variables, a pointwise cut-off is used to enforce positivity at the value level, and discrete mass-scaling factors are computed at selected correction instants and interpolated in time, so as to ensure exact mass matching at those instants and to promote approximate mass preservation between them. To introduce an auxiliary discrete dissipation mechanism, we further employ an SAV-type post-processing correction, which yields monotonicity of the SAV auxiliary variable under the ideal SAV update. For the PNP-NS system, a structure-preserving randomized neural network (SP-RaNN) is used for the velocity field, so that the velocity approximation satisfies the incompressibility constraint pointwise by construction. On the theoretical side, we derive residual-based estimates for the raw, uncorrected RaNN solvers of the linearized subproblems, formulate a conditional local-in-time convergence result for the raw outer Picard iteration of the PNP system, and analyze the value-level positivity correction together with the mass-correction and SAV post-processing steps. For the PNP-NS system, we establish an approximation result for the SP-RaNN space and provide a conditional error statement for the corresponding linearized Oseen-type problem. Numerical experiments demonstrate approximation accuracy in the source-driven manufactured tests and illustrate the intended value-level positivity correction, selected-time mass matching, computed free-energy curves based on the final gauge-fixed potential, and divergence-free approximation in benchmark tests.

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