Explore the Frontier of Global Academia

01.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11243

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

Authors:

Chuanzhen Wang↗Meade Cleti↗Pete Jano↗

De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and flow matching approaches have achieved progress, they typically operate at single resolution and lack mechanisms for incorporating functional constraints. We introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refining to all-atom coordinates, reducing computational cost while maintaining accuracy; (2) functional guidance leveraging pretrained predictors to steer generation toward desired properties without retraining; (3) adaptive SE(3)-equivariant architecture for efficient multi-scale processing. Experiments on unconditional generation, motif scaffolding, and functional design demonstrate state-ofthe-art performance while requiring 4 fewer sampling steps. On enzyme active site scaffolding, ProHiFlo achieves 58.9% success rate compared to 41.2% for RFDiffusion.

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02.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2605.00600

Possibilistic Predictive Uncertainty for Deep Learning

Authors:

Yao Ni↗Jeremie Houssineau↗Yew-Soon Ong↗Piotr Koniusz↗

Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling. Existing methods for uncertainty modeling face a fundamental dilemma: Bayesian approaches provide principled estimates but remain computationally prohibitive, while efficient second-order predictors lack rigorous connections between their specific objectives and epistemic uncertainty quantification. To resolve this dilemma, we introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr), a principled framework grounded in possibility theory. We define a possibilistic posterior over parameters, project it to the prediction space via supremum operators, and approximate the projected posterior using learnable Dirichlet possibility functions. This projection-and-approximation strategy yields a simple training objective with closed-form solutions. Despite its simplicity, extensive experiments across diverse benchmarks show that DAPPr achieves competitive or superior uncertainty quantification performance over state-of-the-art second-order predictors while maintaining both principled derivation and computational efficiency. Code is available at https://github.com/MaxwellYaoNi/DAPPr.

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03.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.13407

Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms

Authors:

Hiba Ahmed↗Alexander E. I. Brownlee↗Jason Adair↗Simon T. Powers↗

arXiv:2606.13407v1 Announce Type: new Abstract: Renewable energy is essential for meeting future energy demands; however, solar energy generation, which occurs only during daylight hours often does not align with household consumption patterns. Appliances such as cookers, washing machines, and dryers are typically operated according to user preferred schedules rather than solar energy availability, creating a scheduling optimization problem. The objective is to determine optimal appliance start times to maximize renewable energy utilization while minimizing user inconvenience and adhering to system constraints. This paper presents a metaheuristic approach using Iterated Local Search (ILS) and Simulated Annealing (SA) to optimize appliance start times, while considering appliance operating durations, power consumption, inverter limit, battery state of charge constraints, and solar generation forecasts. Unlike most existing work, the scheduling is extended beyond a single day to accommodate unfinished tasks from previous days (spillover), ensuring operational continuity and enabling sequential operation across multiple days. Experimental results show that the sequential multi-day scheduling framework effectively manages system constraints while ensuring user convenience under exclusive solar generation. These findings also open opportunities for future research on multi-objective trade-offs between investment in equipment of various sizes, return on that investment, and user satisfaction.

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04.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2602.24012

InfoNCE Induces Gaussian Distribution

Authors:

Roy Betser↗Eyal Gofer↗Meir Yossef Levi↗Guy Gilboa↗

arXiv:2602.24012v2 Announce Type: replace Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) models. A prototypical loss in contrastive training is InfoNCE and its variants. In this work, we show that the InfoNCE objective induces Gaussian structure in representations that emerge from contrastive training. We establish this result in two complementary regimes. First, we show that under certain alignment and concentration assumptions, projections of the high-dimensional representation asymptotically approach a multivariate Gaussian distribution. Next, under less strict assumptions, we show that adding a small asymptotically vanishing regularization term that promotes low feature norm and high feature entropy leads to similar asymptotic results. We support our analysis with experiments on synthetic and CIFAR-10 datasets across multiple encoder architectures and sizes, demonstrating consistent Gaussian behavior. This perspective provides a principled explanation for commonly observed Gaussianity in contrastive representations. The resulting Gaussian model enables principled analytical treatment of learned representations and is expected to support a wide range of applications in contrastive learning.

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05.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2606.25434

Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection

Authors:

Yosef Bernardus Wirian↗Qiang Cheng↗

arXiv:2606.25434v1 Announce Type: cross Abstract: Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing EEG-based screening approaches are constrained by handcrafted feature pipelines that discard neurophysiologically meaningful domain structure and deep learning classifiers that sacrifice interpretability for performance. No existing work unifies physiologically organized concept encoders, cross-concept interaction modeling, and nonlinear tabular classification in a sleep EEG-based MCI detection framework. This study proposes Concept-guided Polynomial-transformed Tabular learning using Kolmogorov-Arnold Network (CPTabKAN), which maps heterogeneous EEG-derived features into domain-informed concept representations, expands them via degree-2 polynomial transformation to expose first- and second-order interactions, and applies a Fourier-parameterized TabKAN classifier to learn nonlinear decision boundaries. CPTabKAN was evaluated on the Study of Osteoporotic Fractures cohort (372 subjects, overnight polysomnography), using 1,379 features organized into ten physiologically motivated concept groups. Under 10-fold cross-validation, CPTabKAN-Second Order achieved a weighted F1-score of 0.9038 (SD 0.034), outperforming GradientBoosting by 5.65 percentage points (t(9)=1.934,p=0.043, one-sided paired test), with advantages persisting under SMOTE-based balancing. Ablation analysis confirmed independent contributions from each component. Concept importance analysis revealed that power spectral density, multi-scale entropy, and Hjorth parameters dominated first-order weights, while cross-concept interactions involving Lempel-Ziv-Welch complexity, statistics, demographics, and slow oscillations exceeded all first-order scores. These results demonstrate that concept-structured, interaction-aware tabular learning surfaces physiologically coherent reasoning, supporting clinical trust.

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06.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2512.12571

Measurement Plasticity: Sensor-Level Adaptation for Vision-Language Models

Authors:

Boyeong Im↗Wooseok Lee↗Yoojin Kwon↗Hyung-Sin Kim↗

We propose Multi-View Physical-prompt (MVP) for Test-Time Adaptation (TTA), a forward-only framework that moves TTA from tokens to photons by treating the camera exposure triangle (i.e., ISO, shutter speed, and aperture) as physical prompts. At inference, MVP acquires selected multiple physical views using a source-affinity score, evaluates digitally augmented variants of each retained view and filters the lowest-entropy predictions, and aggregates predictions with hard voting. This selection-then-vote design is simple, calibration-friendly, and requires no gradients or model modifications. On ImageNet-ES and ImageNet-ES-Diverse, MVP outperforms digital-only TTA on both Auto-Exposure and a combination with conventional sensor control. MVP remains effective under reduced parameter candidates that lower capture latency, demonstrating its practicality.

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07.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2606.19593

Stalls and Spequlation: Pipelined Execution for Fault Tolerant Quantum Computation

Authors:

Aditi Awasthi↗Gokul Subramanian Ravi↗Jonathan Mark Baker↗

arXiv:2606.19593v1 Announce Type: new Abstract: Fault-tolerant quantum computation requires the coordinated action of three distinct systems: classical control logic, quantum hardware, and classical error decoders. Current scheduling models treat logical operations as atomic, hiding the fact that these subsystems operate sequentially and spend significant time idle. We present a pipelined execution framework that decomposes each logical operation into its component stages i.e. Control, Execute, and Decode. Building on this, we discuss some speculation strategies that allow successor operations to begin processing before their predecessors have completed decoding. We evaluate our framework on several common benchmarks and show that pipelining with speculation reduces total pipeline steps by 20-40% compared to a no-speculation baseline. The most aggressive strategy consistently outperforms conservative alternatives, even though partial rollback is needed at times, because the per-rollback penalty is small relative to the parallelism gained. We further show that speculation facilitates load balancing by distributing work more evenly across the heterogeneous subsystems of a fault-tolerant quantum computer, converting idle time into useful computation while also saving on execution time.

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08.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2606.20529

LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents

Authors:

Md Nayem Uddin↗Amir Saeidi↗Eduardo Blanco↗Chitta Baral↗

Policy-adherent tool-calling agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies. Task states consist of relevant facts, identifiers, constraints, and conditions observed through user interaction and tool calls. In standard agents, task states are not represented separately. Observations, tool returns, and policy instructions are placed in the prompt, leaving agents to reconstruct the relevant states from the prompt each time they decide what to do next. This design makes state management implicit, creating two common failure modes. An agent may retrieve the right facts but later ground its decision in stale, missing, or incorrect information; and a syntactically valid tool call may still violate a domain policy that depends on the current task state. We introduce \textsc{LedgerAgent}, an inference-time method for tool-calling agents that maintains observed task states in a separate ledger and renders the states into the prompt. The ledger is also used to check state-dependent policy constraints before environment-changing tool calls are executed, blocking policy violations. Across four customer-service domains and a mixed panel of open- and closed-weight models, \textsc{LedgerAgent} improves average pass\textasciicircum{}k over a standard prompt-based tool-calling approach, with the largest gains under stricter multi-trial consistency metrics.

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09.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17577

Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow

Authors:

Osamu Ito↗Akihiko Katagiri↗Yoshikazu Nakagawa↗Shin Saeki↗Jun Shiraishi↗Masato Sasaki↗

arXiv:2606.17577v1 Announce Type: new Abstract: AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models. To the best of our knowledge, we present the first foundation model–orchestrated workflow for crash safety design that enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds. The workflow integrates four components: (1) a surrogate trained on CAE crash simulations to predict pedestrian leg injury metrics from design parameters, achieving an average $R^2=0.87$ and providing distribution-free conformal prediction intervals; (2) multiobjective evolutionary search (NSGA-II) to discover diverse feasible parameter sets under user-specified constraints; (3) a morphing-based geometry generator that maps parameters to topology-preserving 3D shapes; and (4) a natural-language interface in which an LLM orchestrates the workflow and a vision–language model supports semantic comparison of generated designs. In an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives from a single exploration, a process that would require weeks with conventional CAE iteration. These results suggest that foundation models can serve as integration layers between ML surrogates and physics-based simulation, helping bring AI capabilities to safety-critical engineering domains.

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10.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2606.25511

Preparing two-mode magnonic Schrödinger cat states in a cavity-magnon-qubit system

Authors:

Gen Li↗Gang Liu↗Rong-Can Yang↗Jie Li↗

arXiv:2606.25511v1 Announce Type: new Abstract: The cavity-magnon-qubit system has recently been demonstrated as a new platform for preparing macroscopic quantum states in magnonic systems. Here, we propose to prepare a two-mode magnonic cat state, which is also a non-Gaussian entangled state, based on this practical system involving two yttrium-iron-garnet (YIG) spheres and a superconducting qubit coupled to a common microwave cavity. By adiabatically eliminating the cavity and resonantly driving the qubit, an effective magnon-qubit conditional-displacement interaction is achieved. Further working in the magnon-magnon strong-coupling regime and considering two identical magnon frequencies and coupling strengths to the cavity, two hybridized magnon modes are formed, of which the bright mode is prepared in a cat state after a projective measurement on the qubit, while the dark mode remains in its initial vacuum state. Such a state corresponds to a two-mode cat state of two original magnon modes, which share strong non-Gaussian entanglement. We also discuss practical dissipation and dephasing effects on the cat state. The results indicate that strong nonclassicality and non-Gaussian entanglement are present in the two-mode cat state using fully feasible parameters.

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11.

medRxiv (Medicine) 2026-06-24 DOI: HASH:f2186605eb40ab0c8a153cfd5e28cba0

Durability and Seasonal Variation in the Effectiveness of Nirsevimab over Three Seasons in Connecticut

Authors:

Xu↗Aparicio-Llorente↗Warren↗Kennedy-Shaffer↗Pitzer↗V. E↗Weinberger↗D. M↗Oliveira↗C. R↗PROVE-ID Group Authors↗

Background Nirsevimab has been widely administered in the United States since 2023 to protect infants and young children from severe disease caused by respiratory syncytial virus (RSV). Although early post-licensure studies have shown high effectiveness against medically attended RSV infection, uncertainty remains about the durability of protection, effectiveness beyond the first RSV season, and the extent to which changing RSV seasonality influences real-world effectiveness. Objective To estimate the effectiveness of nirsevimab against medically attended RSV infection across three consecutive RSV seasons and to examine how effectiveness varies by season and time since immunization. Methods We conducted a test-negative case-control study utilizing electronic health records of infants and young children tested for RSV by polymerase chain reaction in outpatient and inpatient settings within the Yale New Haven Health System between October 1, 2023, and March 1, 2026. Effectiveness of nirsevimab was estimated using multivariable logistic regression, adjusting for age, weekly RSV activity, pre-existing risk factors, and other potential confounders. Variation in effectiveness was examined by season, encounter setting, and time since immunization up to 24 months. Results Overall, 17,755 infants and young children were tested for RSV infection, of whom 2,388 (13.4%) were cases and 15,367 (86.6%) were controls. The overall effectiveness of nirsevimab was 67.3% (95% confidence interval [CI]: 59.8, 73.3%) against all medically-attended RSV infections, 60.2% (95% CI: 49.6, 68.5%) against RSV-associated outpatient visits, and 88.9% (95% CI: 82.3, 93.0%) against RSV-associated hospitalization. Effectiveness against medically attended RSV infection declined across seasons, from 76.7% (95% CI: 60.5, 86.3%) in 2023/24 to 54.4% (95% CI: 33.0, 68.9%) in 2025/26. Lower season-specific effectiveness in later seasons corresponded with progressively delayed RSV activity over. Protection against RSV-associated hospitalization declined with increasing time since immunization, from 92.5% (95% credible interval [CrI]: 85.9, 96.4%) at 1 month, to 77.2% (95% CrI: 60.4, 87.6%) at 6 months, and 39.9% (95% CrI: 2.4, 63.3%) at 12 months post-immunization, after which effectiveness plateaued. Conclusions Nirsevimab remained effective against RSV-associated hospitalization through 6 to 12 months after immunization. Delayed RSV activity was associated with lower effectiveness, highlighting the importance of aligning administration with local RSV circulation.

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12.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24161

Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement

Authors:

Xiangqian Zhao↗Xinyang Jiang↗Zhipeng Xu↗Lingfeng He↗Zilong Wang↗Dongsheng Li↗De Cheng↗Nannan Wang↗

Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority groups. Bias mitigation remains challenging due to two fundamental issues. First, when group labels are unavailable, existing group-unsupervised methods typically infer spurious attributes implicitly from model behavior, making it difficult to identify spurious factors that are semantically aligned with real-world biases. Second, even with pseudo spurious supervision, most existing debiasing methods follow a single-branch design that operates within a single shared feature space, where target and spurious attributes are intrinsically entangled. To address the first challenge, we introduce Confidence-guided Bias Concept Mining (CBCM), which leverages diffusion-disentangled, semantically grounded concept representations to identify reliable spurious attributes without attribute annotations. To address the second challenge, we propose Dual-branch Cross-projection Debiasing (DCD), a prompt-tuning framework that separates target and spurious representations into two branches and explicitly removes spurious information through cross null-space projection while preserving target-relevant semantics. Extensive experiments on four benchmark datasets show that our method achieves state-of-the-art worst group accuracy among group-unsupervised approaches, while tuning at most 0.22% of the model parameters. The source code is available in the supplementary materials.

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13.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15308

Forced Deferral: Manipulating Routing Decisions in Multimodal LLM Cascades

Authors:

Zhongye Liu↗Yaopei Zeng↗Yurui Chang↗Lu Lin↗

arXiv:2606.15308v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) have shown strong visual reasoning abilities, serving a large model for every query is computationally expensive. MLLM cascades mitigate this cost by first querying a weak but cheaper model and deferring to a strong model when the weak model's output is unconfident. However, since the weak model's confidence directly controls compute allocation, these systems expose a new attack surface: an adversary can manipulate confidence so that their queries are consistently deferred to the strong model. Motivated by this vulnerability, we introduce the Forced Deferral Attack (FDA), an adversarial image attack that lowers the weak model's confidence and causes cascades to route queries to the strong model. FDA learns a universal border trigger by optimizing a temperature-flattened objective. This objective pushes the weak model's token distribution on triggered inputs toward less concentrated targets constructed from its clean responses. Across datasets, model families, and deferral metrics, FDA consistently increases strong-model routing while outperforming image-perturbation and prompt-injection baselines. These results show that MLLM cascades are vulnerable to attacks that manipulate compute allocation, forcing unintended strong-model usage without directly targeting answer correctness.

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14.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2510.14792

MSPL: Multi-Step Pseudo-Labeling for Open-Vocabulary Object Detection

Authors:

Hojun Choi↗Youngsun Lim↗Jaeyo Shin↗Hyunjung Shim↗

Open-vocabulary object detection (OVD) aims to recognize and localize object categories beyond the training set. Recent approaches leverage vision-language models to generate pseudo-labels using image-text alignment, allowing detectors to generalize to unseen classes without explicit supervision. However, these methods depend heavily on single-step image-text matching, neglecting the intermediate reasoning steps crucial for interpreting semantically complex visual contexts, such as crowding or occlusion. In this paper, we introduce MSPL, a framework that incorporates multi-step visual reasoning into the pseudo-labeling process for OVD. It decomposes complex scene understanding into three interpretable steps-object localization, category recognition, and background grounding-where these intermediate reasoning states serve as rich supervision sources. Extensive experiments on standard OVD evaluation protocols demonstrate that MSPL achieves state-of-the-art performance with superior pseudo-labeling efficiency, outperforming the strong baseline by 9.4 AP50 for novel classes on OV-COCO and improving box and mask APr by 3.2 and 2.2, respectively, on OV-LVIS. Code and models are available at https://github.com/hchoi256/mspl.

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15.

Nature (Science) 2026-06-24 DOI: HASH:4dc02364c0c253ffcf90f9fc65c9f973

Role of methanesulfonic acid in atmospheric particle nucleation and growth

Authors:

Rima Baalbaki↗

Dimethylsulfide (DMS; CH3SCH3) from marine phytoplankton is a major source of atmospheric sulfur 1. Its oxidation products include sulfuric acid (SA; H2SO4) and methanesulfonic acid (MSA; CH3SO3H), which has a higher yield than SA below 10 °C 2. Whereas SA is known to drive the formation of new particles 3, which may subsequently grow and act as cloud condensation nuclei (CCN), the role of MSA remains unclear 4. Here, in experiments performed under atmospheric conditions at the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber, we show that MSA nucleates together with ammonia (NH3) below −10 °C, at rates comparable to SA-NH3. Moreover, MSA and SA nucleate synergistically below −10 °C, forming multi-acid molecular clusters with NH3. Even at ultra-low NH3 levels, MSA drives particle growth at or near the kinetic limit below 9 °C and above 40 % relative humidity (RH). Since MSA and SA generally coexist at similar concentrations in cool marine regions, our findings indicate that nucleation rates may be accelerated up to tenfold and growth rates up to twofold compared with SA-NH3 alone. Our global model simulations indicate that MSA can enhance CCN concentrations, especially in polar regions. We propose that MSA might be an important driver of biogenic particles in cool, pristine marine regions of both the present-day and pre-industrial atmospheres, and yet is unaccounted for in global climate models 5.

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16.

arXiv (CS.CV) 2026-06-25 DOI: arXiv:2606.25975

Tensorion: A Tensor-Aware Generalization of the Muon Optimizer

Authors:

Vladimir Bogachev↗Vladimir Aletov↗Alexander Molozhavenko↗Sergei Kudriashov↗Maxim Rakhuba↗

Common first-order optimizers, such as Adam, implicitly treat each parameter block as an unstructured vector, which disregards the multilinear weight structure present in many modern machine learning models. Recent work has shown that exploiting matrix structure can improve optimization dynamics. A notable example is Muon, which performs steepest descent under the spectral norm constraint. We take the next step and introduce Tensorion, a tensor-aware optimizer that extends Muon's constrained optimization perspective from matrices to higher-order tensors. Tensorion is built around a linear minimization oracle (LMO) over a tensor norm ball. The norm is carefully chosen to balance two objectives: tightly bounding the tensor spectral norm, while still keeping the LMO tractable. This LMO becomes computable because it reduces to operations on adaptively selected unfolding matrices. Notably, when restricted to order-2 tensors (i.e., matrices), Tensorion recovers Muon exactly. Experiments on tensor-based computer vision problems suggest that Tensorion can offer improved convergence behavior and more stable gradient updates compared with Adam-based and existing tensor-aware baselines in the evaluated settings.

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17.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11675

Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

Authors:

Haoyang Zeng↗Yuanxi Fu↗Rongzhen Li↗Yuming Yang↗Xiao Sun↗Jingwang Huang↗Gujie Shao↗Guohui Xiang↗Quan Lu↗Dongfan Ye↗Xuetao Chen↗Jiang Zhong↗…

arXiv:2606.11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis.

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18.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24499

GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras

Authors:

Anil Bayram Gogebakan↗Filippo Marostica↗Alessio Caviglia↗Alessandro Savino↗Stefano Di Carlo↗

Existing automotive event datasets rely on appearance-based annotations from frame pipelines, making them poorly suited for motion-aware event perception. We present a geometry-driven, annotation-free framework that classifies detected objects as static or independently moving by exploiting ego-motion structure directly from the event stream. A Focus of Expansion model with yaw compensation estimates global background motion, while objects are labeled as moving when local motion deviates from this prediction, as quantified by a scale-invariant residual. Temporal stabilization improves robustness across consecutive event windows. The method requires no learning, no manual motion labels, and works with any input bounding boxes. Experiments on MVSEC and the Prophesee 1 Megapixel Automotive Detection dataset demonstrate consistent performance across diverse driving scenarios, with yaw compensation improving results during turns and a simple translational local model offering a favorable accuracy-efficiency trade-off.

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19.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2606.11606

Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation

Authors:

Raajitha Muthyala↗Zhenan Yin↗Alekhya Jilla↗Frank Li↗Theo Dapamede↗Bardia Khosravi↗Mohammadreza Chavoshi↗Judy Gichoya↗Saptarshi Purkayastha↗

Frozen vision-transformer (ViT) foundation-model embeddings increasingly serve as the substrate for downstream chest-radiography (CXR) pipelines, yet where small-scale, low-contrast signal is retained or lost in the frozen forward pass has not been systematically quantified across architectures, pretraining domains, and objectives. We probed five frozen ViTs (RAD-DINO, DINOv2-B/14, DINOv3 ViT-7B, BiomedCLIP, MedSigLIP) and a frozen DINO-pretrained ResNet-50 architectural control across three large CXR cohorts (NIH-CXR14, MIMIC-CXR, Emory-CXR; aggregate pool n=492,724) and ChestX-Det10 (n=3,543; 1,462 small-lesion bounding boxes across Calcification, Nodule, Mass). Each model was evaluated with a small-scale-perturbation panel and a region-aware bounding-box-stratified probe on real lesions, comparing three pooling modes from the same forward pass: classification token (CLS), patch-mean (mean over all final-layer patch tokens), and bounding-box-restricted patch-local. On the perturbation panel, CLS embeddings sat at the chance floor (area under the ROC curve [AUC] 0.500-0.524); patch-mean was indistinguishable from CLS on iso-blur and reticular-fine cells but rose with CLS on larger directional-blur footprints, while disease AUC on globally decided tasks ranged 0.642-0.913. Patch-local probes recovered AUC ~1.0 from the same forward pass (per-model mean improvement +0.412 to +0.488); the ResNet-50 control reproduced the chance floor. On ChestX-Det10, image-level CLS classification showed within-class small-versus-large stratum gaps up to +0.243 AUC; bounding-box-level patch-local pooling on the same forward pass recovered AUC >= 0.899 on every (model x class) cell. Frozen ViT embeddings silently suppress small-scale signal at the global-aggregation step; the signal is recoverable from patch tokens conditional on a region of interest.

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20.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2602.07429

Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers

Authors:

Yuanxu Sun↗Yuezhou Ma↗Haixu Wu↗Guanyang Zeng↗Muye Chen↗Jianmin Wang↗Mingsheng Long↗

arXiv:2602.07429v2 Announce Type: replace-cross Abstract: Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from a representation gap: continuous approaches offer analytical precision but are visually abstract, whereas discrete methods provide intuitive clarity at the expense of geometric precision. To bridge this gap, we introduce Brep2Shape, a novel self-supervised pre-training method designed to align abstract boundary representations with intuitive shape representations. Our method employs a geometry-aware task where the model learns to predict dense spatial points from parametric Bézier control points, enabling the network to better understand physical manifolds derived from abstract coefficients. To enhance this alignment, we propose a Dual Transformer backbone with parallel streams that independently encode surface and curve tokens to capture their distinct geometric properties. Moreover, the topology attention is integrated to model the interdependencies between surfaces and curves, thereby maintaining topological consistency. Experimental results demonstrate that Brep2Shape offers significant scalability, achieving state-of-the-art accuracy and faster convergence across various downstream tasks.Code is available at this repository: https://github.com/thuml/Brep2Shape.

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21.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11698

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

Authors:

Jian-Ping Mei↗Weibin Zhang↗Ao Yao↗Tiantian Zhu↗Jie Xiao↗

arXiv:2606.11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures. The primary technical challenge lies in ensuring watermark robustness against various post-processing attacks on the watermarked model. Model extraction attacks emerge as the most severe threat, where adversaries exploit prediction outputs to train surrogate models that illegally replicate the original model's functionality. In this work, we propose a rehearsal-based watermark embedding framework to enhance the robustness of model watermarks against model extraction attacks. By simulating the extraction process, our method leverages the loss of a simulated stolen model on a trigger set as a training signal to fine-tune the watermark knowledge within the target model. This fine-tuning step encourages the watermark to be embedded in a way that boosts transferability, thereby increasing its chances of persisting and remaining detectable in stolen models. Comprehensive experiments conducted under diverse settings demonstrate that the proposed method significantly improves the robustness of model watermarks against both model extraction and subsequent watermark removal attacks.

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22.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.19849

ViCoStream: Streaming VideoLLMs Can Run Beyond 100 FPS with Stage-Wise Coordinated Inference

Authors:

Yang Tan↗Junlong Tong↗Linan Yue↗Hao Wu↗Pengfei Fang↗Xiaoyu Shen↗

Streaming VideoLLMs must continuously process incoming video while maintaining low query latency, making both video-ingestion throughput and query-time responsiveness critical for real-time deployment. Existing methods largely focus on accelerating individual modules, such as visual encoding, token pruning, or KV-cache compression, but provide limited insight into whether the resulting system can sustain real-time streaming performance. We formulate streaming VideoLLM inference as a coordinated pipeline spanning visual preprocessing, visual encoding, token dropping, and LLM prefilling/decoding. Building on this formulation, we propose ViCoStream (Video Coordinated Streaming), a stage-wise coordinated streaming framework that combines chunk-wise execution, CUDA-stream overlap, visual token control, bounded visual attention, and query-side retrieval to bound per-chunk computation and memory costs. We further provide a systematic study of bottleneck migration, revealing how chunk size, token retention, attention locality, and retrieval scope shape the throughput-accuracy trade-off. Experiments with Qwen2.5-VL-3B/7B-Instruct across multiple streaming benchmarks show that ViCoStream achieves 134 FPS video throughput and less than 50 ms TTFT on a single A100 GPU while maintaining accuracy close to full-history baselines.

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23.

arXiv (quant-ph) 2026-06-19 DOI: arXiv:2606.20281

Arrival times of an atomic Bose-Einstein condensate

Authors:

Pascal Naidon↗Lucas Happ↗Denis Boiron↗

arXiv:2606.20281v1 Announce Type: cross Abstract: The times of flight of an atomic Bose-Einstein condensate are theoretically investigated in the experimentally unexplored regime corresponding to detection close to the trap of the condensate. In this regime, there is no consensus on how to calculate the distribution of times of arrival onto the detector. For non-interacting particles, distinct theoretical predictions have been made in the past. This work analyses how these predictions are modified for an interacting Bose-Einstein condensate. For this purpose, a time-dependent Gross-Pitaevskii equation is solved analytically and numerically.

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24.

arXiv (CS.CV) 2026-06-25 DOI: arXiv:2606.25770

Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning

Authors:

Muhammed Furkan Dasdelen↗Fatih Ozlugedik↗Anastasia Litinetskaya↗Nassir Navab↗Carsten Marr↗Ario Sadafi↗

Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient augmentation approach that generates realistic patients directly in embedding space. Using Gaussian Mixture Models as a probabilistic clustering approach on pooled instance embeddings from all patients, our method learns disease-specific "recipes"-statistical distributions of instances across unsupervised clusters. New patients are then generated by sampling embeddings from clusters based on learned recipes. Unlike existing methods that require examples from all categories, our method can generate patients offline by re-mixing pooled embeddings. Generated patients are further selected based on uncertainty quantification to improve MIL performance. We evaluate our method across three clinically relevant scarcity scenarios: (i) cross-dataset transfer, where an entirely missing "healthy" class is generated using statistics from an external cohort; (ii) low-data regimes, where class sizes are extremely limited; and (iii) small-cohort non-image tasks, including single-cell RNA-seq and flow cytometry. Across all experiments, our method improves performance over baseline, often outperforming other bag-mixing strategies. Notably, in the missing-class scenario, a performance comparable to full-dataset training is achieved, demonstrating its potential for rare disease diagnostic and privacy-preserving patient augmentation. The code is available at https://github.com/marrlab/RECIPE

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25.

medRxiv (Medicine) 2026-06-22 DOI: HASH:3b3a7311e1efc0bdb07a21c978ab0bea

A blinded, counterbalanced rater design for evaluating AI-assisted summarisation of tertiary clinical genomics reports: methodology of the QNOMX-VHIR-CPSP-001 Phase 1 study

Authors:

Creeden↗Olivecrona↗Soriano↗

Background. Tertiary clinical genomics reports condense layered molecular findings into documents that treating oncologists must read, translate, and act upon; manual summarisation of these reports is time-consuming and variable. Tools that assist summarisation and translation into local languages are emerging, yet the field lacks an agreed methodology for evaluating such tools before any downstream clinical use. The appropriate first endpoint is fidelity of the generated summary to its source report, assessed by qualified human raters under blinded scoring, not downstream variant classification. Methods. QNOMX-VHIR-CPSP-001 Phase 1 is a single-site, non-interventional clinical performance study conducted at Vall d'Hebron Institut de Recerca (VHIR) under ISO 20916:2019 as a Clinical Performance Study Protocol. De-identified tertiary cancer genomics reports from pediatric oncology cases are summarised by the AI-assisted summarisation system under evaluation and, in parallel, by the standard manual workflow. Qualified raters score both summary types against the source genomics report using the Quality Summary Index (QSI), a six-dimension, five-point rubric adapted from the Provider Documentation Summarization Quality Instrument, under a blinded, counterbalanced, two-period crossover with a minimum fourteen-day washout. Two co-primary composite endpoints, content and presentation, are analysed for non-inferiority under a Bayesian hierarchical model, with a frequentist linear mixed model as the convergence check. Inter-rater reliability is reported as Krippendorff's ; a Monte-Carlo power analysis of the fixed clustered design is pre-specified. Discussion. The design isolates summarisation quality from clinical decision-making by scoring both summary types against the same source report under blinding, counterbalancing, and a fourteen-day washout. Conclusion. The QSI rubric, the counterbalanced crossover, and the pre-specified Bayesian primary with frequentist convergence check define a replicable protocol for early-stage evaluation of AI-assisted summarisation in tertiary genomics reporting; observed variance components will inform sample-size determination for Phase 2.

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