探索全球前沿学术脉络

01.

medRxiv (Medicine) 2026-06-15 DOI: HASH:ca0df469f0e50db3973b6b49a4680a2e

Two Blood-based Endotypes Reveal Divergent Clinical Outcomes of Fibrotic Hypersensitivity Pneumonitis

作者:

Huang↗Ma↗S.-F↗Kim↗J. S↗Strickland↗Receveur↗B. A↗Bonham↗C. S↗Paul↗T. K↗…

Rationale: Fibrotic hypersensitivity pneumonitis (fHP) is an antigen-driven, life-threatening interstitial lung disease characterized by heterogeneous radiologic features, clinical outcomes, and treatment responses. Objectives: To identify blood-based fHP endotypes that inform mechanism, prognosis and therapeutic response. Methods: We performed integrative analyses of multi-compartment transcriptomic data derived from whole blood, peripheral blood mononuclear cells, bronchoalveolar lavage, and surgical lung biopsies, alongside circulating plasma proteomics. Multiple clustering algorithms were cross-compared to ensure robustness and reproducibility of endotypes identification. Immune cell composition was inferred using bulk RNA-seq deconvolution and annotated with BAL single-cell RNA-seq. Pathway activities were characterized using Gene Set Enrichment Analysis. Transplant-free survival (TFS) was evaluated for endotype and corticosteroid exposure by Kaplan-Meier methods, with hazard ratios analyzed using multivariable Cox proportional hazards models. Results: Two molecular endotypes, lymphocytic-associated (L-fHP) and non-lymphocytic-associated (N-fHP), were identified and validated. L-fHP showed enrichment of adaptive immune signaling and lymphocyte predominance, whereas N-fHP demonstrated myeloid-cell activation with neutrophil and macrophage predominance. Corticosteroid exposure was associated with worse TFS in L-fHP but not in N-fHP after adjusting for age, sex, and baseline pulmonary function. Compared to L-fHP, N-fHP had poorer baseline pulmonary function, faster 12-month FVC decline, and shorter TFS. N-fHP also exhibited elevated neutrophil-associated markers, including matrix metalloproteinase-9, across paired transcriptomic and proteomic datasets, supporting a neutrophil-driven, cross-compartment disease process. Conclusion: Multi-omic, multi-compartment analysis identifies two reproducible fHP endotypes with distinct clinical outcomes and corticosteroid responses, supporting a precision medicine approach beyond current clinical and radiologic classification.

阅读与讨论 → 访问原文 →

02.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2606.12412

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models

作者:

Cheng-Yu Yang↗Shao-Yuan Lo↗Yu-Lun Liu↗

Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV-cache memory. Existing visual-token reduction methods largely follow a rank-and-remove paradigm: they score visual tokens, keep a compact subset, and permanently discard the rest. We show that this irreversible action is fragile because visual-token importance changes across decoder depth; tokens ranked low at one stage may become relevant in later layers, especially for grounding-sensitive queries. We propose Reroute, a training-free plug-in that replaces removal with recoverable routing. At each routing stage, selected vision tokens pass through decoder blocks, while deferred tokens bypass the stage and re-enter the candidate pool at the next routing decision. Reroute reuses existing attention-score ranking rules and stage-wise schedules, preserving the theoretical TFLOPs and KV-cache budget class of the pruning method it augments. Across FastV, PDrop, and Nüwa variants on LLaVA-1.5 and Qwen backbones, reroute improves grounding under aggressive token reduction while maintaining general VQA performance. These results suggest that VLM token reduction should not be viewed only as irreversible pruning, but also as recoverable routing. The code can be found here: https://github.com/elmma/mllm-reroute/

阅读与讨论 → 访问原文 →

03.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2510.10981

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

作者:

Tomoya Wakayama↗Taiji Suzuki↗

arXiv:2510.10981v3 Announce Type: replace-cross Abstract: This paper develops a finite-sample statistical theory for in-context learning (ICL), analyzed within a meta-learning framework that accommodates mixtures of diverse task types. We introduce a principled risk decomposition that separates the total ICL risk into two orthogonal components: Bayes Gap and Posterior Variance. The Bayes Gap quantifies how well the trained model approximates the Bayes-optimal in-context predictor. For a uniform-attention Transformer, we derive a non-asymptotic upper bound on this gap, which explicitly clarifies the dependence on the number of pretraining prompts and their context length. The Posterior Variance is a model-independent risk representing the intrinsic task uncertainty. Our key finding is that this term is determined solely by the difficulty of the true underlying task, while the uncertainty arising from the task mixture vanishes exponentially fast with only a few in-context examples. Together, these results provide a unified view of ICL: the Transformer selects the optimal meta-algorithm during pretraining and rapidly converges to the optimal algorithm for the true task at test time.

阅读与讨论 → 访问原文 →

04.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14750

Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech

作者:

Adarsh Arigala↗Arjun Gangwar↗S Umesh↗Yova Kementchedjhieva↗

Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understanding. Grounding text in its visual form allows structurally similar characters with different Unicode encodings to produce similar embeddings, benefiting cross-lingual and zero-shot scenarios. Conventional text-based approaches treat each character independently, limiting generalization to unseen characters and requiring embedding expansion during cross-lingual adaptation. We propose Pixel-TTS, the first framework for visually grounded speech synthesis. It renders text as images and projects them through a 2D convolutional layer to generate embeddings. This design eliminates embedding matrix expansion during fine-tuning while improving robustness to unseen characters and orthographic variations. Extensive experiments show Pixel-TTS achieves competitive performance with strong baselines, faster convergence and robust zero-shot generalization.

阅读与讨论 → 访问原文 →

05.

medRxiv (Medicine) 2026-06-22 DOI: HASH:94fbfddf2f15611118eb40cbabce196c

Maternal-Fetal immune networks and viral signatures in the healthy amniotic cavity

作者:

Gonzalez-Rovira↗Garcia-Diaz↗Martinez-Pancorbo↗Rodriguez-Herrera↗Sanchez↗Bernardo↗Karatas↗Garcia-Mejido↗J. A↗Sousa↗Mellado↗Sainz-Bueno↗…

The intrauterine environment has traditionally been viewed as a privileged site protected by the placental barrier. However, emerging evidence suggests that early in utero microbial exposure may prime the developing fetal immune system. Here, using target-enriched metagenomics and high-dimensional proteomics, we characterized the intra-amniotic viral landscape and immune networks in 114 healthy pregnancies including both normal and anomalous fetuses. We identify a sparse yet heterogeneous human viral signature in 26% of samples, predominantly composed of Herpesviridae, Polyomaviridae, and Picornaviridae. Although viral reads abundance was associated with fetal abnormalities, viral detection generally did not induce overt inflammatory activation, supporting a state of immune homeostasis within the amniotic cavity. Instead, viral presence was associated with subtle and selective immune modulation, including altered inducible antimicrobial peptide expression (HBD-2 and HBD-3), coupled with an attenuation of regulatory cytokines. Our results further reveal that the amniotic immune environment is primarily governed by gestational age, transitioning from a Th1-predominant "alert" phase to innate-readiness preceding parturition. These findings suggest that fragments of viral genetic material within the amniotic cavity may contribute to fetal immune instruction without triggering overt inflammation, providing a foundational framework for understanding how "silent" viral-exposure during gestation influences the developmental origins of neonatal immunity.

阅读与讨论 → 访问原文 →

06.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2309.15769

Benign overfitting beyond prediction: The ordinary least squares interpolator

作者:

Dennis Shen↗Dogyoon Song↗Peng Ding↗Jasjeet S. Sekhon↗

arXiv:2309.15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations. Owing to its simplicity and practical relevance, the ordinary least squares (OLS) interpolator has become a key object of study for gaining theoretical insight into this phenomenon. While the properties of OLS are well understood in classical underparameterized settings, its behavior in the overparameterized regime – unlike that of ridge regression or the lasso – remains comparatively less explored. We contribute to this growing literature by deriving new algebraic and statistical results for the minimum $\ell_2$-norm OLS interpolator. In contrast to much of the existing work, which focuses on prediction risk, we center our analysis on parameter estimation and inference, which are fundamental for many statistics and causal inference applications. Specifically, we establish overparameterized analogues of (i) the leave-$k$-out formulas, (ii) the omitted variable bias formula, and (iii) the Frisch-Waugh-Lovell theorem. Under the Gauss-Markov model, we further extend the Gauss-Markov theorem and analyze variance estimation under homoskedasticity in the overparameterized setting. Collectively, these results provide a systematic framework for studying parameter estimation and inference in overparameterized linear models, offering a novel perspective on benign overfitting beyond its implications for prediction.

阅读与讨论 → 访问原文 →

07.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.14923

Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems

作者:

Yujiao Chen↗

arXiv:2606.14923v1 Announce Type: new Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates. Yet we lack a standard way to measure trust between AI agents. We propose a behavioral measure based on costly verification. In a cooperative survival game, checking a teammate's work consumes resources, while trusting a wrong answer can be fatal. Relative to a memoryless version of the same model, reduced verification provides an observable measure of trust. Using this framework, we study trust formation, breakage, and recovery across six frontier model snapshots. When paired with a consistently reliable teammate, four snapshots (Claude Opus 4.6, Claude Sonnet 4.6, GPT-5.1, and Gemini 3.1 Pro) reduce verification by roughly 60-85%, whereas two smaller snapshots show little or no such adjustment. Failures reverse this discount, but models differ in how they respond. Some concentrate renewed scrutiny on the culprit, while others become more cautious toward the entire team. Recovery is slower than formation, and clustered failures sustain suspicion far longer than the same number of failures spread apart. These differences have practical consequences. Models that form trust verify less, decide more quickly, and achieve higher payoffs in our environment. By contrast, persistent over-verification is associated with indecision rather than safety. Our results show that trust dispositions can be measured before deployment and suggest that calibration, rather than maximal suspicion, should be the central concern in the governance of multi-agent AI systems.

阅读与讨论 → 访问原文 →

08.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2509.18428

Latent Action Pretraining Through World Modeling

作者:

Bahey Tharwat↗Yara Nasser↗Ali Abouzeid↗Ian Reid↗

Vision-Language-Action (VLA) models have gained popularity for learning robotic manipulation tasks that follow language instructions. State-of-the-art VLAs, such as OpenVLA and $\pi_{0}$, were trained on large-scale, manually labeled action datasets collected through teleoperation. More recent approaches, including LAPA and villa-X, introduce latent action representations that enable unsupervised pretraining on unlabeled datasets by modeling abstract visual changes between frames. Although these methods have shown strong results, their large model sizes make deployment in real-world settings challenging. In this work, we propose LAWM, a model-agnostic framework to pretrain imitation learning models in a self-supervised way, by learning latent action representations from unlabeled video data through world modeling. These videos can be sourced from robot recordings or videos of humans performing actions with everyday objects. Our framework is able to transfer learned knowledge across tasks, environments, and embodiments. It outperforms models pretrained with ground-truth robot actions and other similar pretraining methods on the LIBERO benchmark and real-world setup, while being efficient and practical for real-world settings.

阅读与讨论 → 访问原文 →

09.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2603.21396

Mechanisms of Introspective Awareness

作者:

Uzay Macar↗Li Yang↗Atticus Wang↗Peter Wallich↗Emmanuel Ameisen↗Jack Lindsey↗

arXiv:2603.21396v5 Announce Type: replace Abstract: Recent work has shown that LLMs can sometimes detect when steering vectors are injected into their residual stream and identify the injected concept – a phenomenon termed "introspective awareness." We investigate the mechanisms underlying this capability in open-weights models. First, we find that it is behaviorally robust: models detect injected steering vectors at moderate rates with 0% false positives across diverse prompts and dialogue formats. Notably, this capability emerges specifically from post-training; we show that preference optimization algorithms like DPO can elicit it, but standard supervised finetuning does not. We provide evidence that detection cannot be explained by simple linear association between certain steering vectors and directions promoting affirmative responses. We trace the detection mechanism to a two-stage circuit in which "evidence carrier" features in early post-injection layers detect perturbations monotonically along diverse directions, suppressing downstream "gate" features that implement a default negative response. This circuit is absent in base models and robust to refusal ablation. Identification of injected concepts relies on largely distinct later-layer mechanisms that only weakly overlap with those involved in detection. Finally, we show that introspective capability is substantially underelicited: ablating refusal directions improves detection by +53%, and a trained bias vector improves it by +75% on held-out concepts, both without meaningfully increasing false positives. Our results suggest that this introspective awareness of injected concepts is robust and mechanistically nontrivial, and could be substantially amplified in future models. Code: https://github.com/safety-research/introspection-mechanisms.

阅读与讨论 → 访问原文 →

10.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2508.10523

Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies

作者:

Ayushman Sarkar↗Zhenyu Yu↗Mohd Yamani Idna Idris↗

Visual reasoning matters for many computer vision tasks that go beyond surface-level object detection and classification. Despite progress in relational, symbolic, temporal, causal, and commonsense reasoning, existing surveys typically cover only one part of the problem, such as visual question answering, scene-graph generation, neuro-symbolic AI, or multimodal chain-of-thought, and rarely analyze reasoning types, methodologies, and evaluation protocols together. This survey addresses that gap. Following a structured literature review, we group visual reasoning into five major types (relational, symbolic, temporal, causal, and commonsense) and examine how each is implemented across methods that range from graph-based models, memory networks, attention mechanisms, and neuro-symbolic systems to reasoning with vision-language models (VLMs) and multimodal large language models (MLLMs), including visual chain-of-thought, visual programming, and tool-augmented and test-time reasoning. We then review evaluation protocols for functional correctness, structural consistency, and causal validity, and we analyze their limits in generalizability, reproducibility, faithfulness, and explanatory power. We also identify open challenges: scaling to complex scenes, integrating symbolic and neural paradigms more deeply, the shortage of comprehensive benchmarks, language-prior shortcuts and hallucination in foundation models, and reasoning under weak supervision. Finally, we set out a research agenda for vision systems and argue that connecting perception and reasoning is necessary for transparent, trustworthy, and cross-domain models, especially in high-stakes settings such as autonomous driving and medical diagnostics.

阅读与讨论 → 访问原文 →

11.

arXiv (math.PR) 2026-06-16 DOI: arXiv:2606.14876

High-Order Talagrand and Eldan–Gross Inequalities via Besov-Type Variance Functionals

作者:

Guangyue Han↗Peijie Li↗

arXiv:2606.14876v1 Announce Type: new Abstract: By introducing high-order Besov-type variance functionals that generalize the canonical variance, we develop a unified framework for proving high-order Talagrand-type inequalities that relate high-order energies to Fourier weights. Applying this machinery, we establish high-order Poincaré-type, $L^p$–$L^q$, isoperimetric-type, Falik–Samorodnitsky and Eldan–Gross inequalities, all with explicit constants, in both the Boolean and Gaussian settings. Fundamentally, our semigroup-based framework relies primarily on hypercontractivity and high-order Bismut-type derivative estimates, and is broadly applicable.

阅读与讨论 → 访问原文 →

12.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.14832

PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions

作者:

Chenxin Li↗Zhengyao Fang↗Zhengyang Tang↗Pengyuan Lyu↗Xingran Zhou↗Xin Lai↗Fei Tang↗Liang Wu↗Yiduo Guo↗Weinong Wang↗Junyi Li↗Yi Zhang↗…

Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature still evaluates agents primarily as GUI controllers that observe a screen, emit taps and swipes, and are scored by target app state. Real phone-use tasks are broader: they require deciding when to use app GUIs, device-side commands, or structured tools, while leaving evidence that the intended side effect actually occurred. We introduce PhoneHarness, a mixed-action benchmark and execution harness for studying phone-use agents on verifiable mobile workflows. PhoneHarness runs a device-side agent loop over GUI, CLI, and host-side tool actions, combining deterministic action routing with bounded GUI delegation and auditable execution traces. Its benchmark, PhoneHarness Bench, evaluates whether agents complete tasks with observable side effects, not only whether they produce plausible final answers. On the annotated evaluation split, PhoneHarness reaches a 75.0% pass rate, outperforming the strongest non-PhoneHarness settings by 12.9 percentage points. PhoneHarness and PhoneHarness Bench therefore play distinct but mutually dependent roles: the harness makes mixed phone workflows executable, while the benchmark measures whether agents can use that harness reliably and safely. Our findings suggest that reliable phone automation depends on action-surface routing and verifiable execution, not only visual GUI control.

阅读与讨论 → 访问原文 →

13.

arXiv (CS.LG) 2026-06-17 DOI: arXiv:2507.11366

Characterizing Nash Equilibria in Zero-Sum Games: A Physics-Inspired, Parallelizable Approach with a Linear Number of Gradient Queries

作者:

Taemin Kim↗James P. Bailey↗

arXiv:2507.11366v2 Announce Type: replace-cross Abstract: We study online optimization methods for zero-sum games, a fundamental problem in adversarial learning in machine learning, economics, and many other domains. Traditional methods approximate Nash equilibria (NE) using either regret-based methods (time-average convergence) or contraction-map-based methods (last-iterate convergence). We propose a new method based on Hamiltonian dynamics in physics and prove that it can characterize the set of NE in a finite (linear) number of iterations of alternating gradient descent in the unbounded setting, modulo degeneracy, a first in online optimization. Unlike standard methods for computing NE, our proposed approach can be parallelized and works with arbitrary learning rates, both firsts in algorithmic game theory. Experimentally, we support our results by showing our approach drastically outperforms standard methods.

阅读与讨论 → 访问原文 →

14.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.12337

Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems

作者:

Zhen Zhang↗Alessandro Alla↗George Em Karniadakis↗

arXiv:2606.12337v1 Announce Type: cross Abstract: Inverse problems governed by partial differential equations (PDEs) are central to computational mechanics and are commonly solved by adjoint-based optimization, while physics-informed neural networks (PINNs) have emerged as a flexible alternative. Their relative performance remains difficult to assess because the two approaches are often compared under different formulations, parameterizations, optimizers, and regularization choices. We present a fair comparison of adjoint optimization and PINNs for PDE-constrained inverse problems. From a common abstract formulation, we instantiate both methods on identical domains, governing equations, observation models, and regularization terms, while matching the optimizer, unknown parameterization, and arithmetic precision wherever applicable. The benchmarks include unsteady Burgers, noisy Darcy permeability inversion, three-dimensional Allen–Cahn reaction identification, and unsteady Navier–Stokes viscosity identification. The results show that the representation of the unknown largely determines the preferred method: grid-based fields favor the discrete adjoint, whereas neural representations are native to PINNs and relevant for closure and constitutive modeling. For time-dependent problems, adjoint inversion can be dominated by trajectory storage and differentiation, while PINNs provide satisfactory reconstructions at lower cost. A PINN-warm-started adjoint strategy then recovers adjoint-level accuracy at substantially reduced cost.

阅读与讨论 → 访问原文 →

15.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.13626

Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches

作者:

Kyuil Lee↗Dezhi Yu↗Yongkang Huang↗

arXiv:2606.13626v1 Announce Type: cross Abstract: We study generative modeling of Bach-style symbolic piano music using a shared MIDI corpus and three model families: autoregressive LSTMs with attention, latent-variable models including recurrent VAEs and vector-quantized VAEs, and generative adversarial networks. We compare their ability to model polyphonic note sequences, learn useful latent representations, and generate stylistically coherent compositions. Our experiments show that the autoregressive LSTM with attention produces the most musically coherent samples, while vector quantization helps mitigate posterior collapse and yields more structured outputs than conventional recurrent VAEs. The adversarial approach captures local pitch patterns but remains difficult to train and generalizes less reliably to Bach's style. These results highlight the relative strengths and failure modes of autoregressive, latent-variable, and adversarial approaches for symbolic music generation.

阅读与讨论 → 访问原文 →

16.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2104.08928

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

作者:

Kan Xu↗Xuanyi Zhao↗Hamsa Bastani↗Osbert Bastani↗

Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare. To leverage this information, words are typically translated into word embeddings – vectors that encode the semantic relationships between words – through unsupervised learning algorithms such as matrix factorization. However, learning word embeddings from new domains with limited training data can be challenging, because the meaning/usage may be different in the new domain, e.g., the word ``positive'' typically has positive sentiment, but often has negative sentiment in medical notes since it may imply that a patient tested positive for a disease. In practice, we expect that only a small number of domain-specific words may have new meanings. We propose an intuitive two-stage estimator that exploits this structure via a group-sparse penalty to efficiently transfer learn domain-specific word embeddings by combining large-scale text corpora (such as Wikipedia) with limited domain-specific text data. We bound the generalization error of our transfer learning estimator, proving that it can achieve high accuracy with substantially less domain-specific data when only a small number of embeddings are altered between domains. Furthermore, we prove that all local minima identified by our nonconvex objective function are statistically indistinguishable from the global minimum under standard regularization conditions, implying that our estimator can be computed efficiently. Our results provide the first bounds on group-sparse matrix factorization, which may be of independent interest. We empirically evaluate our approach compared to state-of-the-art fine-tuning heuristics from natural language processing.

阅读与讨论 → 访问原文 →

17.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2606.13275

Zero-Shot Captioning for Cultural Heritage: Automated Image Analysis of Traditional Indonesian Clothing

作者:

Anugrah Aidin Yotolembah↗Novanto Yudistira↗Gembong Edhi Setyawan↗

This paper presents Custom ZeroCLIP, a retrieval-augmented vision-language framework for zero-shot captioning of Indonesian traditional garments. The dataset contains 3,800 expert-annotated images from all 38 Indonesian provinces. Using a province-level inductive zero-shot protocol, the model is trained on 24 seen provinces, validated on 6 seen provinces, and evaluated on 8 unseen provinces. The framework combines a frozen CLIP ViT-B/32 image encoder, a CLIP text encoder, a BERT text encoder, and an LSTM caption decoder. During inference, unseen-province labels and captions are unavailable, and retrieval uses only captions from training provinces. No unseen-province image, label, or caption is used during training, validation, or retrieval-bank construction. Custom ZeroCLIP achieves a CLIPScore of 0.8536, BLEU-4 of 0.3342, and METEOR of 0.4859, outperforming existing baselines. Ablation results show that retrieval improves cultural vocabulary recovery with a 19.3\% METEOR gain, while human evaluation confirms stronger cultural accuracy and fluency. The results demonstrate the effectiveness of retrieval-augmented domain adaptation for culturally grounded caption generation in low-resource heritage settings. The dataset is publicly available at https://github.com/AnugrahAidinYotolembah/Traditional-Indonesian-Clothing-Captioning-Dataset.

阅读与讨论 → 访问原文 →

18.

arXiv (math.PR) 2026-06-16 DOI: arXiv:2605.31054

On the empirical spectral distribution of matrix perpetuities

作者:

Bartosz Ko{\l}odziejek↗Kamil Szpojankowski↗

arXiv:2605.31054v2 Announce Type: replace Abstract: We study matrix perpetuities, that is, solutions to affine fixed-point equations of the form \[ \mathbf{X} \stackrel{d}{=} \mathbf{A}\,\mathbf{X} \,\mathbf{A}^\top+\mathbf{B},\qquad (\mathbf{A},\mathbf{B})\mbox{ and }\mathbf{X} \mbox{ are independent}, \] with particular emphasis on the empirical spectral distribution of the solution. We first establish existence and uniqueness results by relating the problem to classical vector perpetuities, and then develop tools that preserve the matrix structure under orthogonal invariance. For positive semidefinite, orthogonally invariant models, we obtain power-law tail asymptotics for the expected empirical spectral distribution and show that the tail is governed by the largest eigenvalue. We also prove that, in the subcritical regime, the expected empirical spectral distribution of matrix perpetuities converges weakly, as the dimension tends to infinity, to the distribution of the corresponding free perpetuity. Our results are illustrated by matrix Beta prime perpetuities, for which explicit limiting spectral distributions are available.

阅读与讨论 → 访问原文 →

19.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2512.03199

Does Head Pose Correction Improve Biometric Facial Recognition?

作者:

Justin Norman↗Hany Farid↗

Biometric facial recognition models often demonstrate significant decreases in accuracy when processing real-world images, often characterized by poor quality, non-frontal subject poses, and subject occlusions. We investigate whether targeted, AI-driven, head-pose correction and image restoration can improve recognition accuracy. Using a model-agnostic, large-scale, forensic-evaluation pipeline, we assess the impact of three restoration approaches: 3D reconstruction (NextFace), 2D frontalization (CFR-GAN), and feature enhancement (CodeFormer). We find that naive application of these techniques substantially degrades facial recognition accuracy. However, we also find that selective application of CFR-GAN combined with CodeFormer yields meaningful improvements.

阅读与讨论 → 访问原文 →

20.

medRxiv (Medicine) 2026-06-15 DOI: HASH:a1bc08bd0359f3dc44d745c7db841012

ICD-10 Code Ambiguity Obscures Treatment-Eligible Adults with Spinal Muscular Atrophy: A Single-Center Chart Review and Patient Outreach Study

作者:

Holly↗Bean↗Beshay↗Edwards↗Streicher↗N. S↗

Background. Three disease-modifying therapies (DMTs) for spinal muscular atrophy (SMA) have been approved since 2016, yet many adults remain untreated. Identifying them depends on ICD-10 codes that capture SMA but do not reliably distinguish it from other related conditions. We examined, in one U.S. health system, both patients' engagement with therapy and the accuracy of the codes used to find them. Methods. We conducted a retrospective chart review of adults in an academic health system identified by SMA-associated ICD-10 codes, with manual adjudication of diagnosis and DMT status. Confirmed SMA-positive, DMT-naive patients were invited to a structured telephone interview on treatment awareness and barriers. Results. Of 60 charts, 22 (36.7%; 95% CI 25.6-49.3%) were appropriately coded for SMA or a related disorder; only 16 (26.7%) had molecularly confirmed SMA. The other 38 (63.3%) were miscoded, spanning spinal and bulbar muscular atrophy, asymptomatic carriers, prenatal screening, and conditions unrelated to SMA. Ten of the 16 confirmed patients (62.5%) were DMT-naive; one was interviewed, one declined, and eight could not be reached. The non-response is itself a finding: the patients least visible to administrative data are the hardest to reach. Conclusions. ICD-10 ambiguity is a barrier to treatment access in adult SMA, as is loss to follow-up. We make two recommendations: continuous documentation-coding alignment that uses natural language processing to verify the genetic precondition, and type-specific SMA codes (subcodes for Types 0-4) anchored on molecular SMN1 confirmation. Together these would support cohort identification, outreach, and evidence generation without adding to clinician burden.

阅读与讨论 → 访问原文 →

21.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.14662

Beyond task performance: Decoding bioacoustic embeddings with speech features

作者:

Ines Nolasco↗Jules Cauzinille↗Marius Miron↗Gagan Narula↗Milad Alizadeh↗Emmanuel Fernandez↗Matthieu Geist↗Ellen Gilsenan-McMahon↗Olivier Pietquin↗Emmanuel Chemla↗Sara Keen↗

arXiv:2606.14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task. This hinders transparency and limits extension to rare species or data-scarce domains. Here we reveal which speech-like features are encoded in bioacoustic representations. Using the 88~eGeMAPS features across six taxonomic groups, we apply linear and nonlinear regression probes to quantify which acoustic properties each model captures. Results confirm a ``no free lunch'' pattern: no single model captures the full feature space. A concatenated embedding achieves the highest performance, suggesting complementary acoustic space coverage across models. Loudness features are best encoded ($R^2 = 0.76$) while F0 is hardest to recover ($R^2 = 0.33$). By cross-referencing recoverability with per-species feature salience (NMI), we derive data-driven model selection guidance for bioacoustics.

阅读与讨论 → 访问原文 →

22.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.12501

Policy-driven Conformal Prediction for Trustworthy QoT Estimation

作者:

Kiarash Rezaei↗Omran Ayoub↗Paolo Monti↗Carlos Natalino↗

arXiv:2606.12501v1 Announce Type: new Abstract: We propose Conformal QoT, a policy-driven framework that combines statistically guaranteed QoT estimation with operational decision policies, enabling reliable lightpath-feasibility predictions under domain shift and improving accuracy from 92\% to 99.6\% on open datasets.

阅读与讨论 → 访问原文 →

23.

medRxiv (Medicine) 2026-06-15 DOI: HASH:af392520ec2edad7fbe5f3b715dfc1e5

Comparative Analysis of Machine Learning Models vs. Traditional Clinical Calculators for Cardiovascular Risk Prediction

作者:

Arango Plaza↗Salcedo Echeverry↗G. E↗Arenas-Soto↗A. F↗

Background: Cardiovascular diseases (CVD) remain the leading global cause of mortality, responsible for approximately 31% of all deaths worldwide in 2021. Traditional risk calculators, including Framingham, ASCVD, SCORE, and SCORE2, have long constituted the cornerstone of primary prevention strategies; however, they were derived predominantly from high-income European and North American populations, thereby limiting their predictive accuracy in diverse epidemiological contexts, particularly among Hispanic/Latino communities. Machine learning (ML) offers an alternative to capture the non-linear interactions inherent in biomedical data. Objective: The present study develops and validates ML-based models for cardiovascular mortality prediction using the National Health and Nutrition Examination Survey (NHANES) 1999-2018 dataset, and systematically compares their discriminative performance against eleven conventional clinical CVD risk calculators. Materials and Methods: A dedicated software platform, "CardioPrediQ," was designed to integrate multiple CVD calculators with ML-based risk assessment. A cohort of 12,847 participants with 16 predictor variables was derived from NHANES. Six algorithms (Logistic Regression, Cox Proportional Hazards, Gradient Boosting, AdaBoost, Random Forest, and Extra Trees) were trained in combination with six class-balancing strategies, yielding 36 model configurations. All models were trained on a stratified 70/30 split and calibrated using the Saerens prior probability adjustment method. Performance was evaluated using AUC-ROC, sensitivity, specificity, F1-score, and a weighted composite score. DeLong's test was employed to assess the statistical significance of AUC differences between the best-performing ML model and each conventional calculator. Results: Gradient Boosting with 2:1 oversampling and Saerens calibration achieved the best overall performance (AUC = 0.8934; composite score = 0.7904), outperforming all traditional calculators in composite ranking. The top six positions were occupied exclusively by ML and statistical models. The mean age of cardiovascular decedents was 67.43 years compared with 47.74 years among survivors. DeLong's test confirmed statistical superiority over six traditional CVD calculators (p < 0.05), whereas the difference against the top-performing calculators (ASCVD, HEARTS Caribbean, ASCVD Colombia, SCORE2, HEARTS North America) did not reach statistical significance. Age dominated feature importance at 41.2% relative weight, followed by systolic blood pressure (18.7%). Saerens calibration reduced the Brier score from 0.1286 to 0.1158, substantially improving probability calibration. Conclusions: ML models demonstrated superior composite performance over traditional calculators. The statistical equivalence with the highest-performing conventional calculators in the NHANES cohort is context-dependent and validates the methodological pipeline. The CardioPrediQ platform addresses the critical need for integrated, scalable CVD risk assessment tools, which is particularly relevant for Latin American populations where calculator validation remains limited. These findings support the integration of calibrated ML-based risk prediction into clinical practice while underscoring the importance of probability calibration for informed clinical decision-making.

阅读与讨论 → 访问原文 →

24.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20236

A Multi-Agent system for Multi-Objective constrained optimization

作者:

Federica Filippini↗

arXiv:2606.20236v1 Announce Type: new Abstract: Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints. In dynamic environments, reinforcement learning (RL) is often used to solve such problems at runtime by embedding both costs and constraint violations into a single scalar reward through weighted penalty terms, following a Lagrangian-inspired formulation. However, in this context the behavior of the learned policy critically depends on the choice of these weights, which are typically selected manually. This makes it difficult to identify an appropriate trade-off between optimizing the primary objective and effectively avoiding constraint violations, particularly in non-stationary environments where their relative importance may change. This paper presents MAMO (Multi-Agent system for Multi-Objective constrained optimization), an approach to tackle this balancing problem through multi-agent RL. MAMO decouples task execution from objective design by formulating the selection of reward weights as a learning problem, providing a !rst step towards more autonomous and robust RL-based solutions for constrained optimization problems in dynamic environments.

阅读与讨论 → 访问原文 →

25.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.12917

Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function

作者:

Atharva Gupta↗Dhruv Kumar↗Murari Mandal↗Saurabh Deshpande↗

arXiv:2606.12917v1 Announce Type: new Abstract: We present the first causal mechanistic analysis of a tabular foundation model, investigating how TabPFN 2.5's feature wise attention heads distribute computation across layers. Using activation patching, ablation, and attention entropy across two synthetic regression datasets, we find clear temporal specialisation: one head's causal necessity dominates that of the others by 2 to 5 times at peak layer, with its dominant layer shifting across tasks of different complexity, while the remaining heads exhibit symmetric late layer profiles. Attention entropy and patching provide convergent evidence for the computationally active layers of the dominant head. We additionally investigate inference time steerability via contrastive activation steering, which fails to transfer across samples. We attribute this result to TabPFN's in context learning mechanism, which encodes task structure through context dependent attention rather than the stable parametric directions that make steering tractable in language models.

阅读与讨论 → 访问原文 →