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01.
Nature (Science) 2026-06-09

Scientists have a bad case of AI FOMO, <i>Nature</i> poll reveals

Authors:
Mohana Basu↗

Almost half of the scientists who responded said that they feel broadly negative towards artificial intelligence, but they think that some tools are better than others. Almost half of the scientists who responded said that they feel broadly negative towards artificial intelligence, but they think that some tools are better than others.

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02.
arXiv (CS.LG) 2026-06-16

Bridging data-driven priors via the score function for posterior sampling – Comparative review and experimental study

Authors:
Elhadji Cisse Faye↗Mame Diarra Fall↗Sylvain Delchini↗Nicolas Dobigeon↗

arXiv:2606.14800v1 Announce Type: cross Abstract: This paper reviews how a diverse set of popular data-driven priors commonly used in Bayesian inverse problems can be unified through their respective score functions. By framing these priors under this common perspective, we show that they can benefit from their straightfoward and effective integration into a recently proposed sampling algorithm. The applicability of this common framework is illustrated by considering several data-driven priors, namely regularization-by-denoising, normalizing flow-based priors, score-based generative models, and convex-ridge regularizers. For these four particular priors, the performance of the method is evaluated when conducting image inpainting and single image super-resolution. These results, as well as those obtained when restoring real images acquired in a geological context, demonstrate the efficiency of the method. This unified framework proves versatile enough to handle any posterior distribution defined by a broad class of score function-based priors, beyond the specific cases considered in this paper.

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03.
arXiv (CS.LG) 2026-06-19

Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

Authors:
Farhin Farhad Riya↗Shahinul Hoque↗Yingyuan Yang↗Jinyuan Sun↗Kevin Tomsovic↗

arXiv:2606.20415v1 Announce Type: new Abstract: Deep Neural Networks DNNs have achieved remarkable accuracy in various tasks including their application in CyberPhysical Systems CPS for detecting False Data Injection Attacks FDIA during critical operations However the unique infrastructure of CPS makes DNNs vulnerable to exploitation by attackers aiming to evade detection Additionally the distinct nature of CPS presents challenges for conventional defense mechanisms against FDIA This paper proposes an innovative defense framework that strengthens DNNs against such attacks by introducing an additional input layer that performs padding in the input samples using pseudofeature values derived from the inputs statistical distribution This padding increases the input dimensionality in a randomized and dataaware manner making adversarial attacks computationally infeasible due to the nontransferable nature of crafted perturbations and the unpredictability of the padded structure Our method is lightweight modelagnostic and requires no modifications to the core architecture making it highly deployable in realworld CPS settings We evaluated our framework on critical power grid applications such as state estimation using the IEEE 14bus 30bus 118bus and 300bus systems Experiments under adversarial settings demonstrate that our padding strategy significantly improves model robustness with negligible impact on performance and effectively mitigates attacks that would otherwise bypass conventional defenses

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04.
arXiv (quant-ph) 2026-06-17

When Renormalisation Remembers: UV/IR Mixing as an Entanglement Bridge

Authors:
Steven Abel↗

arXiv:2606.17147v1 Announce Type: cross Abstract: Renormalisation is traditionally understood to be a Wilsonian memoryless process in which ultraviolet (UV) degrees of freedom gradually decouple, leaving an autonomous infrared (IR) description. However this need not be the case: in UV/IR mixed theories correlations between widely separated scales can persist. In this work I recast UV/IR mixing as a Hilbert-space phenomenon, realised as correlations across renormalisation scales. This formulation is implemented using the Born-Reciprocal Tensor Network (BRTN), a new configuration of tensor network that is globally symmetric under phase-space reciprocity. On this network I prepare the vacuum and reproduce the expected radiative corrections. The resulting renormalisation geometry exhibits memory, with a bridge linking reciprocal representations of IR physics, whose cross-bridge entanglement provides a precise criterion for the viability of an effective description. I analyse when this criterion is met, and show that there is a large-volume limit, with the fundamental scale held fixed, in which the obstruction to a local description scales away: Wilsonian behaviour is restored and renormalisation forgets. The BRTN therefore provides a concrete and calculable platform for UV/IR mixing.

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05.
medRxiv (Medicine) 2026-06-22

Effect of Lowering the Drink-Driving Blood Alcohol Limit in Scotland on Road Traffic Crashes: a Synthetic Difference-in-Differences Study

Authors:
Jafari↗Anupriya↗Graham↗D. J↗

Objective: To evaluate the road safety impact arising from Scotlands 2014 reduction in the legal blood alcohol concentration (BAC) limit for drivers, and to assess whether the effect of the reform varied across different spatial contexts. Design: A quasi-experimental statistical longitudinal study using a Synthetic Difference-in-Differences (SDID) approach. Setting: Small-area panel data for Great Britain, with areas (Middle-layer Super Output Areas, MSOAs, in England and Wales and Intermediate Zones, IZs, in Scotland) classed into control and treatment groups according to whether they were exposed to Scotlands BAC reform. The control and treatment groups comprise 7088 spatial units in England and Wales and 852 spatial units in Scotland, respectively, observed over the period 2008-2019. Participants: The study primarily analyses police-reported road traffic collision data from the UK Department for Transports STATS19 system. Data were analysed at the MSOA/IZ level. This is a secondary dataset, and we therefore did not involve patients or the public in formulating the research question, determining outcome measures, or designing and conducting the study. Main Outcome Measures: The main outcome measures were log-transformed rates of total road traffic crashes, and (weekend) night-time crashes (22:00-04:00) per 100,000 population. The latter is used as a proxy measure for drunk driving. Results: Our results indicate that the reduction in the legal BAC limit led to statistically significant declines in road traffic crash rates. Aggregate estimates suggest reductions of 12.0% (95% confidence interval (CI): [-13.7%, -10.3%]) in total crashes, 15.6% (95% CI: [-20.7%, -10.2%]) in night-time crashes, and 12.4% (95% CI: [-16.7%, -7.9%]) in weekend night-time crashes. We also find substantial heterogeneity in treatment effects across spatial contexts. Effects were strongest in rural and less densely populated areas, where reductions exceeded 16% (95% CI: [-18.7%, -13.9%]) for total crashes and reached up to 29.6% (95% CI: [-35.8%, -22.8%]) for night-time and 21.4% (95% CI: [-28.3%, -13.9%]) for weekend night-time crashes. Moderate but statistically significant effects were also observed in dense urban areas, whereas effects in suburban and transitional areas were smaller and not statistically significant. Conclusions: Our analysis suggests that lowering the legal BAC limit in Scotland led to meaningful reductions in road traffic crashes, particularly during higher-risk periods and in rural areas. The findings further suggest that the effectiveness of BAC regulation may vary across local contexts, highlighting the importance of accounting for spatial heterogeneity when evaluating road safety policies.

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06.
arXiv (CS.AI) 2026-06-12

Towards Personalized Federated Learning for Dysarthric Speech Recognition

Authors:
Tao Zhong↗Mengzhe Geng↗Jiajun Deng↗Shujie Hu↗Xunying Liu↗

arXiv:2606.13253v1 Announce Type: cross Abstract: Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all speakers to share the same model components can be suboptimal under such heterogeneity, making personalization a promising direction; however, related research on dysarthric speech remains limited. To this end, this paper explores two aggregation strategies to achieve personalization, including the parameter-based averaging strategy and the embedding-based averaging strategy. Experiments on UASpeech and TORGO show that the proposed methods outperform the baseline regularized FedAvg by statistically significant WER reductions of up to 0.99% absolute (3.15% relative) on UASpeech and 0.56% absolute (4.73% relative) on TORGO, respectively.

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07.
arXiv (CS.CL) 2026-06-15

Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression

Authors:
Yong Zhang↗Heng Li↗Yanwen Huang↗Ning Cheng↗Yang Guo↗Yun Zhu↗Yanmeng Wang↗Shaojun Wang↗Jing Xiao↗

Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or supervised compression models rather than on how LLMs utilize retrieved context during inference. We propose Sentinel, a lightweight sentence-level compression framework that decodes inference-time contextual utilization behaviors from head-wise attention patterns of frozen LLMs. To ground supervision in retrieval-dependent answering behavior, Sentinel trains a lightweight probe using QA examples where the model succeeds only when retrieved context is available. Sentinel performs compression using only a single non-autoregressive forward pass without dedicated compression training or autoregressive scoring. Empirically, we find that effective contextual utilization signals remain accessible even in compact proxy models. On LongBench, Sentinel with a 0.5B proxy model achieves up to 5$\times$ compression while attaining question-answering performance competitive with compression methods built on 7B-scale models. Despite being trained only on English QA data, Sentinel also generalizes effectively to Chinese and out-of-domain settings.

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08.
arXiv (CS.LG) 2026-06-17

Edge Flow: A Tractable and Predictive Continuous-Time Model for Gradient Descent at the Edge of Stability

Authors:
Pierre Marion↗

arXiv:2606.18080v1 Announce Type: new Abstract: Gradient descent in deep learning may operate at the edge of stability (EoS), a regime in which the largest eigenvalue of the loss Hessian hovers near the stability threshold $2/\eta$, where $\eta$ is the learning rate. Classical analysis tools such as gradient flow and the descent lemma do not apply here, motivating the search for a continuous-time model valid at EoS. We propose Edge Flow, a system of three coupled ordinary differential equations that provides a tractable, faithful, and predictive model of gradient descent dynamics at EoS. Edge Flow decomposes the dynamics into a center, an oscillation direction, and an oscillation magnitude. The center follows a modified gradient flow on a symmetrized loss; the direction tracks a top eigenvector of the Hessian via Rayleigh quotient dynamics; and the magnitude grows or decays exponentially depending on whether the sharpness exceeds or falls below the threshold $2/\eta$. Crucially, sharpness stabilization emerges from the coupled dynamics via a self-stabilization feedback loop. Discretizing Edge Flow only requires two gradient evaluations and one Hessian–vector product at each iteration. We demonstrate empirically that Edge Flow tracks the dynamics of gradient descent at least as faithfully as previously proposed continuous-time EoS models, while in addition resolving the oscillation of the sharpness at the onset of EoS, and that it provides a principled framework for understanding and mitigating instabilities in this regime.

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09.
arXiv (CS.CV) 2026-06-17

MagicSim: A Unified Infrastructure for Executable Embodied Interaction

Authors:
Haoran Lu↗Songling Liu↗Yue Chen↗Guo Ye↗Mutian Shen↗Shuyang Yu↗Yu Xiao↗Jihai Zhao↗Shang Wu↗Jianshu Zhang↗Xiangtian Gui↗Chuye Hong↗…

Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment. Existing pipelines split these layers with "magic" actions, disconnected training environments, or forward-only renders that cannot reproduce, evaluate, and annotate the same episode. We present MagicSim, an embodied interaction infrastructure built around one deterministic batched runtime and a shared Markov decision process (MDP). From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop. A common execution interface grounds high-level commands through controllers, atomicskills, planner primitives, and asynchronous planning, realizing them as robot actions rather than simulator-side state edits. One task definition supports three capabilities: benchmark and RL evaluation, an autocollect interface that automatically turns commands into grounded trajectories, and agent/VLM-facing interaction. For automatic execution, commands flow through a Command->Skill->Planner->Robot->Record pipeline, while per-environment command, skill, planning, retry, annotation, and episode states advance independently above the shared physics tick. Successful rollouts are saved as structured multimodal trajectories aligning language supervision, action representations, visual/geometric representations, and task-level status with the executed episode. MagicSim thus unifies diverse world construction, embodied execution, task evaluation, automatic rollout generation, and interactive agent interfaces in one planner-in-the-loop runtime.

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10.
arXiv (quant-ph) 2026-06-19

Unveiling coherent dynamics in non-Markovian open quantum systems: exact expression and recursive perturbation expansion

Authors:
Alessandra Colla↗Heinz-Peter Breuer↗Giulio Gasbarri↗

arXiv:2506.04097v2 Announce Type: replace Abstract: We introduce a systematic framework to derive the effective Hamiltonian governing the coherent dynamics of non-Markovian open quantum systems. By applying the minimal dissipation principle, we uniquely isolate the coherent contribution to the time-local generator of the reduced dynamics. We derive a general expression for the effective Hamiltonian and develop a recursive perturbative expansion that expresses it in terms of system-bath interaction terms and bath correlation functions. This expansion provides a systematic tool for analyzing energy renormalization effects across different coupling regimes. Applying our framework to paradigmatic spin systems, we reveal how environmental correlations influence energy shifts and eigenbasis rotations, offering new insights into strong-coupling effects and non-Markovian quantum thermodynamics.

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11.
Nature Medicine 2026-06-11

Clinical Profile and Genomic Characterization of the 2026 Bundibugyo Virus Index Case in Uganda

Authors:
Andrew Nsawotebba↗

Bundibugyo virus disease (BVD) remains a high-consequence threat in Eastern and Central Africa, where cross-border mobility, nonspecific early symptoms, and delayed recognition can obscure transmission. In this case report, we describe Uganda’s 2026 BVD index case: a male patient who traveled from the Democratic Republic of the Congo to Uganda and was admitted to a private hospital in Kampala on 11 May 2026 after more than two weeks of vomiting and diarrhea, with epigastric pain, weakness, and hiccups. He deteriorated rapidly, developing acute kidney injury, pulmonary edema, hepatic dysfunction, hypoxemia, delirium, atrial flutter, possible disseminated intravascular coagulation, and multiorgan failure, and died on 14 May. A posthumous EDTA whole-blood specimen tested at the Central Emergency Response and Surveillance Laboratory was positive for orthoebolavirus RNA and confirmed as Bundibugyo virus (BDBV) by RT-qPCR. Sequencing achieved 99% genome coverage at ≥100× depth. The 2026 BDBV genome formed a distinct lineage approximately equidistant from the 2007–2008 Butalya and 2012 Isiro variants, differing by 216–227 nucleotides (~1.2% sequence divergence). Here, we demonstrate the value of fatality surveillance, private-sector surveillance, diagnostic optimization through national specimen referral, and rapid molecular-genomic diagnostics for early detection, transmission chain interruption, and public health response coordination.

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12.
arXiv (CS.CV) 2026-06-15

HumP-KD: A Hybrid Uncertainty-Aware Multi-Stage Progressive Knowledge Distillation Framework for Efficient Fire Classification

Authors:
Mohammed Arif Mainuddin↗Najifa Tabassum↗Omar Ibne Shahid↗Riasat Khan↗

Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware. This work proposes HumP-KD, a Hybrid Uncertainty-aware Multi-stage Progressive Knowledge Distillation framework for efficient fire classification. Two datasets, FlameVision and Dataset-II, containing 8,600 and 31,309 images, are used. Various CNN and transformer baselines are applied under standard preprocessing, online augmentation, Gaussian noise and motion blur robustness conditions. The proposed HumP-KD model distills knowledge from two frozen heterogeneous transformer teachers, Swin-Tiny and ViT-Base, along with their Meta-MLP ensemble, into a lightweight MobileViT-S student via three tightly integrated components. Hierarchical Progressive Knowledge Distillation employs a Hierarchical Feature Builder. It generates a fused spatial attention mask to guide distillation toward discriminative regions selectively. Multi-Stage Knowledge Distillation progressively activates three distillation stages across training. On Dataset-II, HumP-KD achieves a mean F1 score of $0.9876 \pm 0.0063$ across 10 independent trials, significantly outperforming the MobileViT-S baseline trained without distillation ($0.9537 \pm 0.0351$), with statistical significance confirmed by both independent t-test ($p = 0.0195$) and Wilcoxon signed-rank test ($W = 1$, $p = 0.0039$). The proposed method also demonstrates strong generalization across datasets and robustness under degraded visual conditions. The student model retains only 4.94M parameters and 19.01Mb model size, representing a $5.7\times$ parameter reduction over Swin-Tiny and a $17.5\times$ reduction over ViT-Base, while achieving 37.72 CPU FPS, making it suitable for real-time deployment.

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13.
arXiv (CS.CL) 2026-06-15

AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization

Authors:
Junlong Tong↗Wenqi Xu↗Yingqi Fan↗Anhao Zhao↗Xuan Lu↗Yang Tan↗Xiaoyu Shen↗

Large reasoning models typically follow a read-then-think paradigm: they observe the complete input, reason over a static context, and then produce the answer. Yet many real-world scenarios are inherently dynamic, such as audio and video stream, where information arrives as a continuous stream and models must reason, update, and respond under partial observations. Recent streaming reasoning methods allow models to think while reading, but they largely rely on supervised imitation of pre-constructed trajectories, which limits their flexibility. In this paper, we propose AdaSR, an adaptive streaming reasoning framework that enables models to reason during input streaming and perform final deliberation once the stream is complete, learning when to think, and how much computation to allocate across different stages. To optimize this hierarchical reasoning process, we introduce Hierarchical Relative Policy Optimization (HRPO), which decomposes policy optimization into streaming reasoning and deep reasoning phases, providing more fine-grained advantage assignment instead of uniformly distributing a single sequence-level advantage over all tokens. HRPO integrates format, accuracy, and adaptive thinking rewards to enforce valid reasoning protocols, preserve final task performance, and encourage latency-aware computation allocation. Experiments show that AdaSR achieves a better balance among reasoning accuracy, computational efficiency, and streaming latency compared with supervised fine-tuning baseline. We release our code at https://github.com/EIT-NLP/StreamingLLM/tree/main/AdaSR.

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14.
arXiv (CS.AI) 2026-06-19

See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View

Authors:
Fanfu Xue↗En Yu↗Yantian Shen↗Zhikun Hu↗Hongjun Wang↗Yang Yang↗Xindi Wang↗Jiande Sun↗

arXiv:2606.20045v1 Announce Type: cross Abstract: UAV Vision-Language Navigation (UAV-VLN) is typically formulated as a holistic search-and-reach problem, where long-range target discovery and final target approach are optimized and evaluated jointly. This formulation makes it difficult to assess a critical capability of aerial embodied agents, namely whether a UAV can accurately ground a visible target and translate vision-language evidence into precise 3D motion once the target enters its field of view. To address this limitation, we introduce UAV-VLN-FOV, a target-visible navigation task that isolates the see-and-reach stage and enables a more diagnostic evaluation of terminal reaching ability. We further propose 3DG-VLN, a vision-language waypoint prediction framework guided by dynamic 3D direction cues to enhance fine-grained visual grounding and spatial direction alignment for precise target reaching. Specifically, 3DG-VLN adaptively processes high-resolution front-view and downward-view observations to preserve fine-grained visual and geometric details for target grounding. It also updates the target-relative direction online during closed-loop navigation, allowing the agent to maintain spatial alignment with the target and reduce accumulated direction drift. To support this task, we construct a dedicated high-resolution benchmark which contains 2,717 trajectories with target-oriented high-level instructions, high-resolution front-view and downward-view egocentric observations, and continuous 3D waypoint annotations. Experiments show that 3DG-VLN outperforms competitive UAV-VLN baselines, achieving a 13.82\% improvement in success rate. Real-world trials further demonstrate the potential of 3DG-VLN for practical see-and-reach navigation. The source code and benchmark are available at https://github.com/xuefanfu/3DG-VLN.

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15.
Science (Express) 2026-06-18

Indium-free perovskite/silicon tandem solar cells with tin oxide recombination layer and electrodes | Science

Authors: Unknown Author

Indium-based transparent conductive oxides are widely used as electrodes and recombination layers in perovskite/silicon tandem solar cells, yet their scalability is constrained by indium scarcity and sputtering-induced damage. Here we report high efficiency and stable indium-free perovskite/silicon tandem solar cells enabled by reactive plasma deposited tin oxide (RPD-SnO x ). For RPD-SnO x as the recombination layer, a certified efficiency of 33.6% is achieved. Fully indium-free tandems that used RPD-SnO x as both recombination layer and electrodes delivering a champion PCE of 33.2% (1 cm 2 ) and a mini-module with a certified efficiency of 31.0% (207.9 cm 2 ). Dense and uniform self-assembled monolayer anchoring enabled by RPD-SnO x suppressed non-radiative recombination and reduced halide migration. Indium-free mini-modules exhibited high thermal, damp-heat, and outdoor operational stability and retained 65% of their maximum initial efficiency after 105 days of outdoor operation.

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16.
arXiv (CS.CV) 2026-06-19

MMD-SLAM: Structure-Enhanced Multi-Meta Gaussian Distribution-Guided Visual SLAM

Authors:
Fan Zhu↗Ziyu Chen↗Peichen Liu↗Yifan Zhao↗Zhisong Xu↗Hui Zhu↗Hongxing Zhou↗Sixun Liu↗Chunmao Jiang↗

3D Gaussian Splatting (3DGS) has significantly boosted novel view synthesis and high-fidelity scene reconstruction, expanding the potential of 3DGS-based Visual Simultaneous Localization and Mapping (SLAM) methods. However, most existing systems fail to fully exploit the underlying structural information, which limits rendering quality and often leads to inconsistent maps. To address these limitations, we propose MMD-SLAM, a structure-enhanced Visual SLAM framework that leverages the Atlanta World (AW) assumption to guide a Multi-Meta Gaussian representation for photorealistic mapping. First, we introduce a point-line fusion strategy for pose optimization, where 3D line segments are incorporated to improve tracking robustness and provide additional constraints for mapping. Second, we design a Multi-Meta Gaussian representation with dominant directions, explicitly encoding structural priors from the AW hypothesis. Finally, we propose a Gaussian evolution strategy that adapts to scene geometry and incorporates structural cues into global optimization. Extensive experiments demonstrate that these innovations enable MMD-SLAM to achieve state-of-the-art performance in both tracking accuracy and mapping quality. e.g., our method achieves a 48.56% reduction in ATE RMSE on ScanNet and a 5.71% improvement in PSNR on Replica, compared with MonoGS.

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17.
arXiv (CS.AI) 2026-06-16

AIChilles: Automatically Uncovering Hidden Weaknesses in AI-Evolved Systems

Authors:
Yajie Zhou↗Ao Li↗Ashwin Silla↗Zaoxing Liu↗Vyas Sekar↗

arXiv:2606.15834v1 Announce Type: new Abstract: The computer systems community has recently seen growing interest in AI-driven system evolution, where AI agents iteratively rewrite systems. Frameworks such as AdaEvolve and Engram report 12-60% score improvements over human-designed algorithms. While these results are promising, there are practical concerns if these AI-evolved programs can perform worse on unseen workloads and exhibit scalability regressions. Given the speed and scale of AI-generated code, we need automated mechanisms to uncover such identify hidden weaknesses in AI-evolved systems programs. To this end, we develop AIChilles that takes as input a baseline program $P$ and an AI-evolved program $P'$, AIChilles searches for valid workloads where $P'$ regresses relative to $P$ in correctness, runtime, memory usage, or output quality. To tackle the diversity in system applications, weakness types and potential bugs, AIChilles combines deterministic workload-parameter extraction, agent-based constraint inference, differential oracles, and code-frequency coverage to discover diverse failures. Across five system applications and 30 AI-evolved programs, AIChilles finds 49 distinct hidden weaknesses. We also show that explicitly including AIChilles in the AI-driven development lifecycle can mitigate several of these weaknesses.

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

Online Realizable Regression and Applications for ReLU Networks

Authors:
Ilan Doron-Arad↗Idan Mehalel↗Elchanan Mossel↗

arXiv:2602.19172v2 Announce Type: replace Abstract: Realizable online regression can behave very differently from online classification. Even without any margin or stochastic assumptions, realizability may enforce horizon-free (finite) cumulative loss under metric-like losses, even when the analogous classification problem has an infinite mistake bound. We study realizable online regression in the adversarial model under losses that satisfy an approximate triangle inequality (approximate pseudo-metrics). Recent work of Attias et al. shows that the minimax realizable cumulative loss is characterized by the scaled Littlestone/online dimension $\mathbb{D}_{\mathrm{onl}}$, but this quantity can be difficult to analyze. Our main technical contribution is a generic potential method that upper bounds $\mathbb{D}_{\mathrm{onl}}$ by a concrete Dudley-type entropy integral that depends only on covering numbers of the hypothesis class under the induced sup pseudo-metric. We define an entropy potential $\Phi(\mathcal{H})=\int_{0}^{diam(\mathcal{H})} \log N(\mathcal{H},\varepsilon)\,d\varepsilon$, where $N(\mathcal{H},\varepsilon)$ is the $\varepsilon$-covering number of $\mathcal{H}$, and show that for every $c$-approximate pseudo-metric loss, $\mathbb{D}_{\mathrm{onl}}(\mathcal{H})\le O(c)\,\Phi(\mathcal{H})$. In particular, polynomial metric entropy implies $\Phi(\mathcal{H})d$, otherwise infinite), and for bounded-norm $k$-ReLU networks separate regression (finite loss, even $\widetilde O(k^2)$, and $O(1)$ for one ReLU) from classification (impossible already for $k=2,d=1$).

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19.
arXiv (CS.AI) 2026-06-16

Training and Evaluating Diffusion Policies with Long Context Lengths

Authors:
Abhinav Agarwal↗Adam Wei↗Taylan Kargin↗Michael Zeng↗Cole Becker↗Arif Kerem Dayi↗Pablo Parrilo↗Asuman Ozdaglar↗Russ Tedrake↗

arXiv:2606.16447v1 Announce Type: cross Abstract: Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only a short history of observations. These policies cannot solve tasks that require memory and can get stuck repeatedly executing the same failing motions. In this work, we first benchmark policy performance as context length is incrementally increased from short to long, across a spectrum of tasks with varying local stability and memory requirements, and in multiple data regimes. To our knowledge, this is the first study to investigate context length in imitation learning at this level of detail. Our results challenge prior claims: naively scaling context length is not as brittle as advertised in literature. With an appropriate conditioning method and denoising backbone (UNet+Cross-Attention), single-task policies achieve high success rates on many tasks in the usual data regime even with naive scaling. Next, we propose a training algorithm to jointly train policies at multiple context lengths, further reducing the sample complexity of long-context learning. Finally, we apply our findings to re-evaluate some previously proposed solutions to long-context imitation learning.

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

Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaboration

Authors:
Sadman Sakib Enan↗Junaed Sattar↗

Effective multi-human-robot collaboration is essential for expanding human-led operations in the challenging and high-risk underwater environment. For autonomous underwater vehicles (AUVs) to become true teammates, they must be able to comprehend their surroundings and recognize a diver's activities to offer assistance and ensure safety. Towards this goal, we introduce DAR-Net, a novel transformer-based framework that analyzes complex underwater scenes to classify diver activities. Our contribution lies in a semantically guided learning formulation that couples transformer-based temporal reasoning with pixel-level scene supervision. This multi-loss training strategy explicitly aligns global activity recognition with local human-robot interaction semantics, which is particularly critical in low-visibility underwater conditions. To address the significant challenge of data scarcity in this domain, we present the first-ever Underwater Diver Activity (UDA) dataset, a foundational resource containing over 2,600 annotated images with pixel-level masks. Through rigorous experimental evaluations in a controlled environment, we demonstrate that DAR-Net achieves promising accuracy in recognizing six distinct diver activities, outperforming state-of-the-art models. While this dataset provides a crucial baseline, our work serves as a pioneering step, laying the groundwork for future research and facilitating the development of more intelligent, collaborative underwater robotic systems.

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21.
arXiv (CS.LG) 2026-06-15

More with LESS – Local Scene Representations for Tactile Imaging

Authors:
Zohar Rimon↗Elisei Shafer↗Tal Tepper↗Daniel Kozin↗Alon Malka↗Roy Holland↗Aviv Tamar↗

arXiv:2606.14344v1 Announce Type: new Abstract: Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supervised learning approaches have shown promising results, but rely on global, unstructured representations and robot-controlled sensing, limiting generalization and practical use. We propose Local Encoder for Spatial Sensing (LESS), an object-centric tactile representation that exploits the local nature of touch. The tactile scene is modeled as a grid of recurrent encoders with local receptive fields, whose states are fused to reconstruct 2D or 3D images of internal structure. This compositional design enables strong generalization: models trained on single-inclusion phantoms accurately image objects with multiple inclusions and varying sizes. The local structure further supports spatial uncertainty estimation. In addition, we enable hand-held tactile imaging via external pose tracking and human-like palpation data, and extend tactile imaging to full 3D reconstruction.

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23.
medRxiv (Medicine) 2026-06-15

Modelling the public-health impact of indoor air quality interventions on respiratory virus transmission

Authors:
Howes↗Jeyapragasan↗Williamson↗Carel↗Koos↗Swett↗J. L↗Montavon↗Belenky↗Lietar↗Fitzjohn↗Charles↗…

Respiratory virus transmission occurs in indoor settings where ventilation, occupancy, and dwell time determine exposure levels. Improving indoor air quality (IAQ) therefore could help reduce disease burden associated with respiratory viruses, yet its population-level impact remains poorly quantified. Here, we develop an individual-based transmission modelling framework that links within-location airborne dynamics to individual infection risk and population-level spread, whilst explicitly incorporating heterogeneity in ventilation and baseline indoor air quality across locations. We use this modelling approach to evaluate IAQ-improving interventions (air-quality interventions or AQIs), using hypothetical endemic and pandemic pathogen archetypes with properties similar to SARS-CoV-2 and influenza, and evaluate how effects on key epidemiological metrics (such as annualized incidence and epidemic final size) depend on AQI coverage, efficacy and allocation strategy. At 20% AQI intervention coverage and 80% efficacy, annualized incidence was reduced by approximately 7.2% for an endemic 'SARS-CoV-2-like' respiratory virus, and 17.0% for an endemic 'influenza-like' virus; at 60% coverage (80% efficacy) the reductions were 26.3% and 56.4%, respectively. Targeting AQI installation to the highest-risk locations outperformed random allocation: for SARS-CoV-2-like transmission, 20% coverage at 80% efficacy cut absolute incidence by 10.8% when targeted versus 7.2% when random; for influenza-like transmission, this comparison was 28.9% versus 17.0%. In epidemic scenarios, random installation at 40% coverage and 60% efficacy reduced final size by 23.7% (influenza-like) versus 6.3% (SARS-CoV-2-like). These results support treating clean indoor air as core public-health infrastructure and prioritising risk-based deployment of IAQ-improving interventions to maximise population-level benefit within budgetary and operational constraints.

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24.
arXiv (CS.LG) 2026-06-12

Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows

Authors:
Daniel Dehtyriov↗Jonathan F. MacArt↗Justin Sirignano↗

arXiv:2605.26358v2 Announce Type: replace-cross Abstract: Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equations provide savings exceeding ten orders of magnitude but introduce unclosed terms (the closure problem). Offline-trained machine-learning (ML) closures suffer distribution shift in predictive simulations, while ML methods that bypass the governing equations struggle to generalise from scarce high-fidelity data. We develop a physics-derived deep learning closure model for RANS, the Deep Algebraic Reynolds Stress Model (DARSM), which can be trained on small datasets and accurately generalise across Reynolds numbers, to unseen geometries, and to different flow regimes. A neural network maps flow invariants to empirical parameters in an implicit algebraic Reynolds stress equation, derived from the Reynolds stress transport equations under the weak-equilibrium assumption, imposing physics-based structure on the ML closure. End-to-end optimisation through the governing PDEs and the coupled implicit closure eliminates distribution shift, but both unrolled and implicit automatic differentiation fail on the stiff coupled solver. We derive adjoint equations that exploit the solver's implicit-explicit structure for efficient optimisation. On canonical square-duct and periodic-hill benchmarks, DARSM reduces average test velocity error over baseline RANS by $2$-$4\times$ across Reynolds number, geometries, and flow regimes, with peak case-level reductions of $12\times$. The model trained on attached, anisotropy-dominated flows (square duct) accurately generalises without retraining to separated flows (periodic hills), a regime change in the underlying physics. DARSM also outperforms five established ML methods: offline training, tensor-basis neural networks, field-inversion machine learning, DeepONets, and physics-informed neural networks.

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25.
arXiv (CS.AI) 2026-06-12

EA-WM: Event-Aware World Models with Task-Specification Grounding for Long-Horizon Manipulation

Authors:
Kailin Wang↗Haoxiang Jie↗Yaoyuan Yan↗Jiacheng Zhou↗Zhiyou Heng↗

arXiv:2606.13053v1 Announce Type: cross Abstract: Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant events. Long-horizon manipulation requires progress signals that are relational, predicate-level, and physically grounded: whether an object has moved, whether a drawer or contact state has changed, whether a placement predicate is satisfied, and whether a candidate future is reliable enough for execution. We introduce EA-WM, an event-aware world-model framework that augments frozen visual-feature dynamics with task-specification-grounded event prediction and verification. EA-WM rolls out candidate futures in pretrained visual-feature space, decodes them into structured event states, and scores them using task-progress, semantic-consistency, physical-feasibility, and uncertainty terms. The verifier guides sampling-based planning, gates candidate actions, and, in the contact-sensitive LIBERO wine-rack setting, selects among PPOgenerated proposals. Across navigation, deformable-object, wall-constrained, and languagedescribed manipulation studies, EA-WM shows that event-aware verification can make featurespace world models more interpretable and better aligned with task progress.

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