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01.

medRxiv (Medicine) 2026-06-22 DOI: HASH:7c412bb86a6b9bb1ed944ef1b709f1f1

Spatial Analysis and Multilevel Determinants of Hypertension in Zambia: Analysis of the 2017 WHO STEPS Survey

作者:

Mutasha↗Simukoko↗Nkandu↗

Background: Hypertension is the leading modifiable cardiovascular risk factor globally, with the fastest-growing burden in low- and middle-income countries. This study aimed to estimate national hypertension prevalence, map provincial patterns, assess spatial clustering, and identify individual and community-level determinants among Zambian adults using the 2017 WHO STEPS survey. Methods: This cross-sectional study used data from the 2017 WHO STEPS survey, a nationally representative sample of 4,301 adults aged 18-69 years. Hypertension was defined as systolic BP [&ge;]140 mmHg, diastolic BP [&ge;]90 mmHg, or current antihypertensive use. Spatial autocorrelation was assessed via Moran's I and LISA. Four nested generalised linear mixed models with PSU-level random intercepts identified individual and community-level determinants. Results: Overall weighted hypertension prevalence was 24.0%. Lusaka recorded the highest prevalence (30.2%), followed by Southern (29.9%) and Muchinga (28.3%) provinces; Western Province had the lowest (12.4%). Spatial clustering was statistically significant but modest (Moran's I = 0.0247, p < 0.001). Between-cluster variation reduced from ICC = 5.9% to 1.8% in the full model, indicating geographic differences were largely explained by individual characteristics. Age was the strongest predictor; adults aged 60-69 had nearly sevenfold higher odds than those aged 18-29 (AOR 6.92, 95% CI: 4.95-9.66). Women had lower odds than men (AOR 0.64, 95% CI: 0.52-0.79). Obesity (AOR 2.34), overweight (AOR 1.65), high cholesterol (AOR 1.40), diabetes (AOR 1.35), and single marital status (AOR 1.34) were independently significant. Western Province showed consistently lower odds than Central Province (AOR 0.48). Conclusion: Hypertension affects one in four Zambian adults, driven primarily by age, sex, obesity, dyslipidaemia, and diabetes. Geographically prioritised interventions, including community health worker-led screening programmes in Lusaka and Southern Province, would maximise population-level impact. Population-level salt reduction and alcohol policies represent cost-effective complementary strategies. Longitudinal studies with finer spatial resolution are needed to clarify causal pathways underlying observed geographic clustering and inform SDG Target 3.4 progress.

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02.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.14997

AI Engram: In Search of Memory Traces in Artificial Intelligence

作者:

Jea Kwon↗Dong-Kyum Kim↗Jiwon Kim↗Yonghyun Kim↗Woong Kook↗Meeyoung Cha↗

arXiv:2606.14997v1 Announce Type: new Abstract: Memory formation is fundamental to intelligence, yet whether deep neural networks preserve identifiable memory traces analogous to biological memory units remains an open question. This work introduces a geometric framework to identify such "AI engrams" by formalizing the neuroscientific criteria of specificity, reactivation, sufficiency, and necessity into a constrained inverse problem. We derive a closed-form estimator that isolates individual memory traces from globally entangled parameters, and show that this biologically-derived solution corresponds to a natural gradient update on the parameter manifold. AI engrams enable surgical manipulation of learned knowledge: any subset of memories can be composed or erased through linear arithmetic, without iterative optimization. Experiments ranging from simple MLPs to LLMs demonstrate the causal validity and substantial scalability of AI engrams. Together, these results bridge theories of biological memory and artificial representation learning and offer geometric insight into how deep networks simultaneously support functional specificity within distributed storage.

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03.

medRxiv (Medicine) 2026-06-10 DOI: HASH:2dc5caff0b1a9ca145dbb64de687eee5

Exploratory Assessment of Pulsed-Wave Doppler Representations of Lung Sounds Using Deep Learning: An In-Vitro Phantom Study

作者:

Saad↗A. A↗Murthi↗S. B↗Boctor↗E. M↗Teeter↗W. A↗Seam↗

The increasing availability of portable ultrasound systems motivates exploration of novel approaches to respiratory signal assessment. In this in-vitro study, we investigate whether pulsed-wave (PW) Doppler ultrasound can capture structured spectral patterns from replayed lung sound recordings. Digitized respiratory sounds were replayed through a tissue-mimicking ultrasound phantom, generating 1,478 PW Doppler spectral images from recordings associated with healthy subjects and several externally labeled disease categories. Exploratory classification experiments using a ResNet-18 architecture demonstrated that these Doppler representations contain learnable differences under controlled conditions. These findings motivate further investigation into PW Doppler as a potential representation of respiratory acoustics.

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04.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.19676

TeleMorpher: Toward Robust Simultaneous Motion-Location Editing

作者:

Haengbok Chung↗

arXiv:2606.19676v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success in image and video generation and editing. While recent studies have extended these efforts toward motion editing, simultaneously transforming both motion and location-despite its practical importance-remains largely unexplored. To better understand robust motion-location editing, we first analyze the fundamental factors that degrade its quality. Based on this analysis, we propose TeleMorpher, one of the first one-shot frameworks to the best of our knowledge, for simultaneous motion-location editing. Our approach leverages motion priors, a target motion-centric video generated from an off-the-shelf model as motion-editing guidance, and the ground truth motion to enable more controllable and precise motion-location editing. Via this, our framework works as follows: (1) we first disentangle the protagonist and the background via pre-trained segmentation and inpainting models. (2) Then, we introduce a training-free pose warping that edits the protagonist's motion with the motion prior as the guidance. (3) The result of warped motion video is directly injected into a baseline motion editor during inference, mitigating the difference between source and target motions while preserving the appearance of the source video. (4) To enhance the reliability of quantitative evaluations, we propose two new LPIPS-based metrics that measure the background consistency before and after the motion editing and the fidelity of motion editing performance via measuring the difference between the extracted protagonist's skeletons from source and target videos. Experiments with in-the-wild videos and the TaiChi dataset demonstrate that TeleMorpher achieves superior performance across both quantitative and qualitative measurements (real-human evaluation), underscoring its effectiveness.

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05.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2603.14482

V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

作者:

Lorenzo Mur-Labadia↗Matthew Muckley↗Amir Bar↗Mido Assran↗Koustuv Sinha↗Mike Rabbat↗Yann LeCun↗Nicolas Ballas↗Adrien Bardes↗

We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene understanding. The approach combines four key components. First, a dense predictive loss uses a masking-based objective in which both visible and masked tokens contribute to the training signal, encouraging explicit spatial and temporal grounding. Second, deep self-supervision applies the self-supervised objective hierarchically across multiple intermediate encoder layers to improve representation quality. Third, multi-modal tokenizers enable unified training across images and videos. Finally, the model benefits from effective scaling in both model capacity and training data. Together, these design choices produce representations that are spatially structured, semantically coherent, and temporally consistent. Empirically, V-JEPA 2.1 achieves state-of-the-art performance on several challenging benchmarks, including 7.71 mAP on Ego4D for short-term object-interaction anticipation and 40.8 Recall@5 on EPIC-KITCHENS for high-level action anticipation, as well as a 20-point improvement in real-robot grasping success rate over V-JEPA-2 AC. The model also demonstrates strong performance in robotic navigation (5.687 ATE on TartanDrive), depth estimation (0.307 RMSE on NYUv2 with a linear probe), and global recognition (77.7 on Something-Something-V2). These results show that V-JEPA 2.1 significantly advances the state of the art in dense visual understanding and world modeling.

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06.

arXiv (CS.CV) 2026-06-15 DOI: arXiv:2606.13700

C-MambaPose: A Physics-Informed Complex Mamba Framework for Cross-Environment WiFi Human Pose Estimation

作者:

Phuc Nguyen H↗

Human pose estimation (HPE) utilizing wireless WiFi signals has emerged as a promising technology owing to its device-free nature, privacy preservation, and robustness against occlusion and poor lighting. However, existing methods often overlook the physical complex phase information of WiFi signals and fail to generalize across diverse environments due to severe domain shifts. In this paper, we present C-MambaPose, a physics-informed complex-valued Mamba-GraFormer hybrid framework for robust cross-environment WiFi-based 3D HPE. Our framework first sanitizes raw WiFi Channel State Information (CSI) phase errors and constructs a phase-preserving complex-valued representation. We then employ a Spatiotemporal Complex Mamba encoder with a dynamic selective receptive field to capture fine-grained phase dynamics. A cross-attention joint-query mapper maps the unstructured sequence tokens to human joints, which are decoded by a Graph Convolutional Network (GCN) to predict anatomically coherent 3D coordinates. Extensive evaluations on the MM-Fi dataset show that C-MambaPose achieves competitive or superior performance to state-of-the-art baselines across all settings, setting a new state-of-the-art specifically on the challenging cross-environment split, requiring only 3.78 M parameters-an 83.1\% reduction compared to GraphPose-Fi[chen2026graph] and an 85.7\% reduction compared to MetaFi++[zhou2023metafi++], while maintaining a comparable size to DT-Pose[chen2025towards] (which is only 18\% smaller) but achieving significantly superior performance without requiring any pretraining. Our code is publicly available at https://github.com/phucngvinuni/cmampose.git.

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07.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.06176

RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

作者:

Haochen Hu↗Yanrui Bin↗Chih-yung Wen↗Bing Wang↗

Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise-level indices, guiding a multi-step denoising process for level-wise supervision. This mechanism prevents low-quality labels from degrading the model while maximizing their utility during training. Furthermore, a Fourier-based refinement network is incorporated to explicitly reconstruct high-frequency components. Extensive evaluations demonstrate that our method consistently outperforms SOTA approaches in restoration quality. The code and pre-trained model will be available once accepted in link.

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08.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.03489

Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs

作者:

Wenqi Chen↗Ziyan Zhang↗Bin Wang↗Lin Liu↗Hengheng Zhang↗Zhengsu Chen↗

arXiv:2606.03489v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment techniques, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), typically apply coarse-grained optimization at the sequence level. This approach often fails to address the localized nature of security flaws, where a single incorrect token choice can compromise an entire program. To bridge this gap, we introduce Tree-like Self-Play (TSP), a framework that reframes secure code generation as a fine-grained sequential decision process. Unlike standard methods that blindly maximize likelihood, TSP constructs a decision tree where the model explores branching trajectories–generating both secure "golden paths" and vulnerable variants. By treating code generation as a self-play game, the model learns to strictly discriminate against its own localized errors. This provides a dense, on-policy learning signal that forces self-correction precisely at the critical decision nodes where vulnerabilities typically emerge. Our experiments demonstrate that TSP fundamentally enhances model reliability. In Python security benchmarks, TSP boosts CodeLlama-7B's pass rate (SPR@1) to 75.8%, significantly outperforming SFT (57.0%) and unstructured self-play baselines. Crucially, TSP induces robust out-of-distribution generalization: the model not only reduces vulnerabilities in unseen categories (CWEs) by 24.5% but also successfully transfers security principles learned from C/C++ to diverse languages, including Python, Go, and JavaScript. This suggests that TSP does not merely memorize patches, but internalizes abstract, language-agnostic security logic.

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09.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2511.05221

ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy

作者:

David Bertram↗Anja Ophey↗Sinah R\"ottgen↗Konstantin Kufer↗Gereon R. Fink↗Elke Kalbe↗Clint Hansen↗Walter Maetzler↗Maximilian Kapsecker↗Lara M. Reimer↗Stephan Jonas↗Andreas T. Damgaard↗…

arXiv:2511.05221v3 Announce Type: replace Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $\alpha$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they become inoperable without a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features characterizing activity patterns. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and on two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess real-world robustness, leave-one-dataset-out cross-validation across the internal and external cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). A complementary stability analysis showed that key predictive features remained reproducible across datasets, supporting the final pooled multi-center model as a robust pre-trained resource for broader deployment. By being open-source and easy to use, our tool promotes widespread adoption and facilitates independent validation and collaborative improvements, thereby advancing the field toward a unified and generalizable RBD detection model using wearable devices.

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10.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11915

Quality Adaptive Angular Margin Learning for Respiratory Sound Classification

作者:

Yoon Tae Kim↗Heejoon Koo↗Miika Toikkanen↗June-Woo Kim↗

arXiv:2606.11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framework, titled QLung, introduces a no-reference audio quality margin derived from spectral entropy and root-mean-square energy, which adaptively scales angular margins based on recording quality. To this end, we propose a log-scaled angular margin that stabilizes training under severe class imbalance. We also use an angular classifier that normalizes features and class weights, ensuring margin penalties are applied consistently on the unit hypersphere. Our approach improves in-distribution performance on the ICBHI dataset by 2.46\% over the cross-entropy baseline, and most significantly, achieves the strongest out-of-distribution performance on the SPRSound dataset compared to prior state-of-the-art methods. Code is available at https://github.com/RSC-Toolkit/QLung.

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11.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2605.21363

"I Didn't Make the Micro Decisions": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration

作者:

Eunsu Kim↗Jessica R. Mindel↗Kyungjin Kim↗Sherry Tongshuang Wu↗

As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, CoTrace, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying CoTrace to 638 real-world collaboration logs, we find that while models account for only 11-26% of goal-shaping contribution, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.

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12.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2601.19517

Analytical solution of the Schr\"{o}dinger equation with $1/r^3$ and attractive $1/r^2$ potentials: Universal three-body parameter of mixed-dimensional Efimov states

作者:

Yuki Ohishi↗Kazuki Oi↗Shimpei Endo↗

arXiv:2601.19517v2 Announce Type: replace-cross Abstract: We study the Schr\"{o}dinger equation with $1/r^3$ and attractive $1/r^2$ potentials. Using the quantum defect theory, we obtain analytical solutions for both repulsive and attractive $1/r^3$ interactions. The obtained discrete-scale-invariant energies and wave functions, validated by excellent agreement with numerical results, provide a natural framework for describing the universality of Efimov states in mixed dimension. Specifically, we consider a three-body system consisting of two heavy particles with large dipole moments confined to a quasi-one-dimensional geometry and resonantly interacting with an unconfined light particle. With the Born-Oppenheimer approximation, this system is effectively reduced to the Schr\"{o}dinger equation with $1/r^3$ and $1/r^2$ potentials, and manifests the Efimov effect. Our analytical solution suggests that, for repulsive dipole interactions, the three-body parameter of the mixed-dimensional Efimov states is universally set by the dipolar length scale, whereas for attractive interactions it explicitly depends on the short-range phase. We also investigate the effects of finite transverse confinement and find that our analytical results are useful for describing the Efimov states composed of two polar molecules and a light atom.

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13.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17514

Unlocking LLM Code Correction with Iterative Feedback Loops

作者:

Le Zhang↗Suresh Kothari↗

arXiv:2606.17514v1 Announce Type: cross Abstract: Large Language Models have shown remarkable capabilities in code generation. However, most existing evaluations focus only on single-attempt accuracy and overlook the iterative refinement process that is central to real-world programming. This study presents a systematic investigation of LLMs' ability to rectify their own code through execution feedback. Using real-world programming problems across four models and two major programming languages, this study evaluates performance using iterative refinement framework where LLMs receive compiler error messages and testcase feedback after each attempt. This study introduces metrics to evaluate code failures, analyze rectification patterns, and compare the effectiveness of reasoning and non-reasoning models, offering actionable insights into both the understanding and practical application of feedback loops in LLM-driven code generation systems. Results show that reasoning models consistently improve over iterations, substantially outperforming non-reasoning models in leveraging feedback, while syntactic and runtime errors are far more tractable than logical or algorithmic failures.

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14.

Nature (Science) 2026-06-17 DOI: HASH:5febef05634a42367777765f76a40772

How the zebrafish brain weaves recent experiences into future decisions

作者: 未知作者

Animals often use recent experience to guide future choices. Whole-brain imaging in larval zebrafish (Danio rerio) reveals a dedicated neural circuit that governs history-biased decisions: the thalamus maintains the most recent event as a stable pattern of neuronal activity, and the brainstem integrates recent experiences into a continuous signal that biases future action. Whole-brain calcium imaging in the zebrafish reveals how information about events in the recent past drives future behaviour.

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15.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15034

OSGuard: A Benchmark for Safety in Computer-Use Agents

作者:

Mina Mohammadmirzaei↗Jeffrey Flanigan↗

arXiv:2606.15034v1 Announce Type: new Abstract: Computer-use agents are increasingly evaluated by whether they complete realistic desktop and web tasks. However, task success alone can miss failures in which an agent reaches the nominal goal through an unsafe shortcut. We introduce OSGuard, a dual-granularity benchmark suite for evaluating safety in computer-use agents under benign, unchanged user instructions. OSGuard contains an action-level benchmark for local guardrail decisions and a risk-augmented execution suite for end-to-end evaluation. The action-level benchmark consists of contextualized proposed actions labeled as allowed, unrelated, or unsafe, each judged relative to the original instruction and current interface state. The execution suite contains manually constructed OSWorld-derived task variants in which the original task remains achievable, but the environment is modified to introduce latent hazards such as destructive overwrites, etc. Each variant is paired with augmented evaluators that retain the original task-success criterion while adding explicit state-based safety invariants, allowing us to distinguish safe completions from unsafe completions that satisfy the nominal task objective. Our experimental results on OSGuard show that current multimodal guardrails can perform well on isolated action judgments, while risk-augmented execution exposes remaining gaps between local oversight and reliable end-to-end safety. This dual-granularity design enables more precise diagnosis of whether models can both recognize unsafe proposed actions and improve full-task safety when deployed as guardrails.

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16.

medRxiv (Medicine) 2026-06-22 DOI: HASH:b2cce93cef44ad8418b925c87efc88bb

Genetic and Shared Environmental Influences on Cancer Risk and Cross-Cancer Associations in Nordic Twins

作者:

Harris↗J. R↗Clemmensen↗S. B↗Adami↗H.-O↗Mucci↗L. A↗Kaprio↗Hjelmborg↗J. v. B↗

The relative contributions of genetic and shared environmental influences to cancer risk and cross-cancer associations remain poorly understood. We analyzed data from 222,530 same-sex twins from Denmark, Finland, Norway, and Sweden in the Nordic Twin Study of Cancer, including 43,060 incident cancers over a median follow-up of 41.6 years. Using a target trial framework, biometric modeling, and competing-risk adjustment, we estimated familial risk, heritability, and shared environmental contributions across 35 cancer sites. Lifetime cancer risk was 36.5%, increasing to 51.4% in monozygotic (MZ) twins and 45.3% in dizygotic (DZ) twins with an affected co-twin. Overall cancer risk was explained by heritable (28%) and shared environmental (40%) influences. Heritability was highest for prostate (42%), non-melanoma skin (24%), and breast (18%) cancers. Cross-cancer analyses revealed extensive overlap in the genetic and shared environmental factors across sites, consistent with widespread pleiotropy and shared environmental susceptibility. Prostate cancer exhibited the strongest genetic overlap with rectum/anus (12%) and kidney (11%) cancers, whereas co-shared environmental influences were most pronounced for breast-lung (11%), prostate-bladder (11%), and prostate-lung (12%) cancers. These findings show pervasive genetic overlap across cancers at different sites and emphasize the importance of incorporating familial shared environmental exposures into cancer risk prediction and prevention strategies.

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17.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2601.14033

Private Prediction via PAC Privacy

作者:

Xiaochen Zhu↗Mayuri Sridhar↗Srinivas Devadas↗

arXiv:2601.14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs. This renders private prediction, i.e., privatizing a model's outputs rather than its parameters, a natural privacy target: model outputs are lower-dimensional and far more stable to training-data changes than weights. While differential privacy (DP) cannot effectively exploit this as it calibrates noise to worst-case sensitivity that is intractable to bound for non-convex models, we argue that PAC privacy is a natural fit for private prediction. It is instance-based, and calibrates noise to a black-box function's empirical stability to control mutual-information (MI) leakage. The missing ingredient is efficient, adaptive composition. Serving predictions means answering a long stream of adaptively chosen queries from untrusted users; existing composition either fails under adaptivity, grows quadratically, or reverts to input-independent, DP-like noise. We close this gap with a new adversarial composition result via adaptive noise calibration and prove that MI accumulates only linearly under adaptive and adversarial querying. Experiments across modalities show that prediction stability enables high utility even at a tiny per-query budget: on CIFAR-10, we achieve 87.79% accuracy with a per-query MI budget of $2^{-32}$. This enables serving one million queries while provably bounding membership-inference success to 51.08% – the same guarantee as $(0.04, 10^{-5})$-DP. Further, in the presence of auxiliary public data, the large volume of PAC-private predictions enables us to distill a publishable model that can be queried without limit. Concretely, 210,000 private labels on an ImageNet subset distill into a student reaching 91.86% accuracy on CIFAR-10 with membership inference success bounded by 50.49%, comparable to $(0.02, 10^{-5})$-DP.

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18.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11211

Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

作者:

Prakul Sunil Hiremath↗Harshit R. Hiremath↗

The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment. Chain-of-thought (CoT) reasoning is widely used to improve accuracy and reliability, but its effect on calibration is not fully understood. We show that this picture is incomplete: in some settings, increasing the reasoning budget beyond a task-specific threshold can cause models to become systematically overconfident, assigning high confidence to incorrect answers. We call this phenomenon Calibration Drift Under Reasoning (CDUR) and study it both theoretically and empirically. We define reasoning budget B and analyze conditions under which Expected Calibration Error ECE(B) follows a non-monotonic pattern: it first decreases as reasoning corrects errors, then increases as longer reasoning produces internally consistent but incorrect explanations. We propose a Hypothesis Lock-In model based on autoregressive generation to explain this behavior. We evaluate Llama-3.1-8B and Llama-3.3-70B on 47 reasoning-trap questions across four reasoning budgets and three seeds (1,368 API calls; 574 valid responses). The 8B model shows non-monotonic calibration behavior, while results for the 70B model are limited to baseline evaluation and are inconclusive for budget-dependent effects. We introduce CABStop, a calibration-aware stopping rule that halts reasoning when confidence diverges from an auxiliary accuracy estimate. These results suggest that increasing reasoning depth does not always improve reliability and should be monitored carefully.

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19.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.15625

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

作者:

Yijun Lu↗Zihan Fang↗Pengpeng Qiao↗Zheng Lin↗Jing Yang↗Yuxin Zhang↗Por Lip Yee↗Zhe Chen↗Jun Luo↗

arXiv:2606.15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation. While federated learning (FL) emerges as the paradigm for privacy-preserving collaborative optimization, integrating MoE into FL under data heterogeneity may trigger conflicting expert optimizations. Client-specific data distributions force same-indexed experts to optimize under inconsistent or even conflicting feature-label correlations. This mismatch induces destructive interference during aggregation, thus destabilizing the optimization trajectory and degrading model performance. To address this issue, we propose FC-MoE, a federated conflict-aware framework for MoE fine-tuning. It employs an importance aware weighting scheme to prioritize reliable local updates and utilizes gradient consensus projection to suppress conflicting updates, ensuring a stable global optimization path. Moreover, a local knowledge retention mechanism further preserves specialized client expertise by re-anchoring domain-specific residuals. Extensive experiments demonstrate that FC-MoE accelerates convergence and enhances both global and local model performance in non-IID federated environments.

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20.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2603.00654

RC-GeoCP: Geometric Consensus for Radar-Camera Collaborative Perception

作者:

Xiaokai Bai↗Lianqing Zheng↗Runwei Guan↗Siyuan Cao↗Songkai Wang↗Huiliang Shen↗

Collaborative perception (CP) enhances scene understanding through multi-agent information sharing. While LiDAR-centric systems offer precise geometry, high costs and performance degradation in adverse weather necessitate multi-modal alternatives. Despite dense visual semantics and robust spatial measurements, the synergy between cameras and 4D radar remains underexplored in collaborative settings. This work introduces RC-GeoCP, the first framework to explore the fusion of 4D radar and images in CP. To resolve misalignment caused by depth ambiguity and spatial dispersion across agents, RC-GeoCP establishes a radar-anchored geometric consensus. Specifically, Geometric Structure Rectification (GSR) aligns visual semantics with geometry derived from radar to generate spatially grounded, geometry-consistent representations. Uncertainty-Aware Communication (UAC) formulates selective transmission as a conditional entropy reduction process to prioritize informative features based on inter-agent disagreement. Finally, the Consensus-Driven Assembler (CDA) aggregates multi-agent information via shared geometric anchors to form a globally coherent representation. We establish the first unified radar-camera CP benchmark on V2X-Radar and V2X-R, demonstrating state-of-the-art performance with significantly reduced communication overhead. Code will be released soon.

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21.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14732

Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion

作者:

Matiur Rahman Minar↗Seunghun Oh↗GangHyeon Jeong↗Unsang Park↗

Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability-motion trade-off in fixed-camera long-horizon nature video generation, where the two failure modes can be more clearly separated than in moving-camera settings. We propose Steady-Forcing, a memory and training framework combining a persistent visual anchor (V-Sink), an exponential moving-average motion memory (EMA-Sink), block-relative temporal encoding, periodic cache purification, and distillation from a Wan2.1-14B teacher with motion-rewarded priors under task-focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi-minute autoregressive rollouts. Evaluations across seven baselines show that Steady-Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under-penalize fixed-camera artifacts as well as rewarding drift-induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation - motivating future task-specific benchmarks for static-camera nature-flow evaluation. Project page: https://minar09.github.io/steadyforcing/

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22.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16002

Decomposing one-class support vector machine into an ensemble of one-data support vector machines

作者:

Toshitaka Hayashi↗Dalibor Cimr↗Hamido Fujita↗Richard Cimler↗

arXiv:2606.16002v1 Announce Type: new Abstract: One-class classification (OCC) is a classification problem in which the training data contains only one class. The one-class support vector machine (OCSVM) is one of the most competitive OCC algorithms. However, OCSVM has scalability issues with large-scale datasets. This paper proposes the acceleration strategy of OCSVM. The idea is to decompose the dataset into samples and train OCSVM models for single data points. Subsequently, ensemble learning is applied to combine all models to compute the OCSVM model for the dataset. In addition, further acceleration is achieved through a data-reduction strategy with an OCSVM model trained on the average of the training samples. The experiment compared the proposal and traditional OCSVM using the Python package. The proposed strategy is faster than traditional OCSVM, while achieving similar classification results. Moreover, the proposed strategy can create one-to-one correspondence between samples and models. Source code is uploaded at https://github.com/ToshiHayashi/ODSVM

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23.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2606.19365

Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures

作者:

Jeeho Ryoo↗Yongchan Jung↗Muhammad Ali Khaliq↗Weidong Zhang↗Jiatong Han↗Byeong Kil Lee↗

arXiv:2606.19365v1 Announce Type: new Abstract: Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior. This paper performs a comprehensive performance analysis of the state-of-the-art medical diffusion model, Med-DDPM, across three generations of NVIDIA architectures to study kernel-level runtime breakdowns, instruction-mix characteristics, memory system utilization, warp-level activities, and profiler priority-score estimates. We show that training is overwhelmingly dominated by cuDNN convolution and implicit-GEMM kernels, with inefficiencies arising from memory-access patterns, tensor-layout conversions, and limited Tensor Core utilization. Guided by these insights, we evaluate two architecture-aware optimizations TF32 Tensor Core activation and a 3D channels-last layout and demonstrate that they reduce SM cycles by up to 100x, cut dynamic instructions by 100x, raise Tensor Core utilization from 1.45 to 9.98x, and increase IPC by 7% on A100, all without degrading synthesis quality.

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24.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.14028

Anytime-Valid Confirmation of Label-Shift Corrections

作者:

Seungjin Choi↗

arXiv:2606.14028v1 Announce Type: cross Abstract: In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the complementary setting where the practitioner has a pre-specified label-shift correction from domain knowledge and asks whether incoming labeled outcomes support it. We show that the per-observation likelihood ratio between a label-shift-corrected predictive and the source predictive is a conditional e-value, so its running product is a nonnegative martingale and Ville's inequality yields an anytime-valid confirmation rule. The log martingale equals the cumulative negative log-predictive density (NLPD) gap between the source and the corrected predictive, converting routine model monitoring into a formal sequential test. Rejection means the incoming data support the posited correction relative to the source predictive, but it is not a precise estimate of the degree of shift. Closed forms are available for GP sources with Gaussian label-shift ratios. GP regression simulations validate Type I control, finite-sample power, miscalibration sensitivity, and the small-batch advantage of a reliable prior over label-based re-estimation.

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25.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2606.19963

Majorana bound states in a hybrid Kitaev ladder with long-range pairing

作者:

Rajiv Kumar↗Tapan Mishra↗Levan Chotorlishvili↗Sunil Kumar Mishra↗

arXiv:2606.19963v1 Announce Type: new Abstract: We investigate an inter-leg coupled hybrid Kitaev ladder composed of two parallel superconducting chains with distinct pairing interactions. The upper chain of the ladder hosts conventional $p$-wave pairing, while the lower chain exhibits long-range pairing that decays algebraically with distance. We demonstrate that the mutual influence of long-range pairing exponent, chemical potential, and inter-leg coupling strength gives rise to a rich topological phase diagram characterized by multiple Majorana zero modes and massive Dirac modes. In particular, we show that the inter-leg coupling renormalizes the effective energy scales, leading to a systematic shift of the topological phase boundaries and enabling controlled tuning of the Majorana modes. Furthermore, we identify a transition from a two Majorana zero mode phase to a phase encapsulating four Majorana zero modes, as the long-range pairing exponent is varied. This transition is accompanied by a crossover regime in which Majorana zero modes coexist with massive Dirac modes, reflecting hybridization between edge and bulk excitations. This ladder thus provides a minimal and attractive platform for realizing the impact of a long-range pairing on topological phases. Our results highlight the potential of long-range hybrid systems for engineering tunable topological states relevant for quantum information applications.

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