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01.
Nature (Science) 2026-06-17

Probing picometre-scale interlayer deformations via hyperbolic polaritons

作者:

The resilience of van der Waals (vdW) materials to large strain fields makes them an ideal platform for tuning electronic, optical and magnetic properties1–4. Although in-plane strain is readily mapped, non-invasive and quantitative characterization of out-of-plane strain remains a formidable challenge, particularly for picometre-scale deformations buried at interfaces. Here we demonstrate a polaritonic optical method that uses the mid-infrared out-of-plane hyperbolic polaritons (oHPs) mode to detect interlayer deformations in prototypical vdW polar insulator–hexagonal boron nitride (hBN). This method uses the softening mechanism of out-of-plane transverse optical (oTO) phonons induced by interlayer strain, enabling highly sensitive detection of picometre-scale deformations. Although these oTO phonon modes are typically spectroscopically ‘dark’, their strain response is activated through the oHPs, achieving an atomic displacement sensitivity of about 10 pm (about 8 × 10−7 times the probing wavelength), enabling ultradeep-subwavelength mechanical interlayer deformation detection. This is experimentally validated in both planar hBN and at the buried interface of quantum dot–hBN nanotube heterostructures. This polariton-based picometrology bridges nanomechanics and photonics, providing a non-destructive lens to visualize hidden stress landscapes with atomic precision. A new polaritonic optical method that uses the mid-infrared out-of-plane hyperbolic polaritons mode is described and experimentally validated to allow the examination of picometre-scale interlayer deformations, providing a bridge between nanomechanics and photonics.

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

Forced Deferral: Manipulating Routing Decisions in Multimodal LLM Cascades

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.

03.
arXiv (quant-ph) 2026-06-19

Phase locking nuclear spins in silicon with spin-orbit coupling

arXiv:2606.20340v1 Announce Type: new Abstract: Because they have such long coherence times, nuclear spins have extraordinary potential for use in quantum information processing devices. However, coherent nuclear spin control generally requires external phase references, such as microwave control fields. Here, we phase-lock a $^{29}$Si nuclear spin ensemble in a silicon quantum dot using only the internal electronic spin-orbit coupling as a phase reference. When driven with the quantum-dot electrons, the nuclear spins align themselves to a phase determined by the electronic spin-orbit coupling and the timing of the drive protocol. This enables us to measure the coherent precession and inhomogeneous dephasing of the nuclear spins. We corroborate our results with detailed numerical simulations of the many-body electron nuclear system. Our work opens new routes for coherently controlling solid-state nuclear spin ensembles.

04.
arXiv (quant-ph) 2026-06-16

Magic transfer in quantum spin chains

arXiv:2606.14855v1 Announce Type: new Abstract: Quantum communication protocols based on spin chains have been extensively studied, yet their ability to transmit nonstabilizer resources has not been systematically addressed. We investigate the transport of quantum magic in spin chains through the natural dynamics of systems initialized in nonstabilizer states, and quantify the transported resource via the stabilizer norm. We analyze three experimentally feasible state-transfer protocols, ranging from noisy to (quasi-)perfect transfer, including one realizable in trapped-ion platforms. We find that the geometry of the injected state strongly influences transport: states in the lower Bloch hemisphere achieve higher transfer quality, whereas states in the upper hemisphere give rise to an efficient magic transport only beyond a threshold value of the parameter controlling the tendency towards perfect transfer. These features are robust across all protocols and identify the Hamiltonian and state properties that favor high-quality transfer. Moreover, we identify a parameter region, relevant to the initial state preparation, in which the transported magic exceeds the initial encoding, indicating that such spin systems can act as magic-amplification channels. Our results establish the conditions for efficient transport of nonstabilizer resources and demonstrate quantum magic as a sensitive probe of quantum transport beyond population dynamics.

05.
medRxiv (Medicine) 2026-06-22

Population-Scale, Genotype-First Characterization of Monogenic Diabetes in 374,973 Multi-Ancestry Individuals from the All of Us Research Program

OBJECTIVE To characterize the prevalence and penetrance of maturity-onset diabetes of the young (MODY) in a multi-ancestry population using a genotype-first design. RESEARCH DESIGN AND METHODS We analyzed whole-genome sequencing and clinical data from 374,973 unrelated All of Us participants (42.0% non-European ancestry). We identified pathogenic or likely pathogenic (P/LP) variants in 10 established MODY genes and assessed carrier prevalence, diabetes penetrance, and glycemic profiles. We evaluated age-dependent diabetes risk by comparing carriers with non-carriers stratified by type 2 diabetes polygenic risk score (T2D PRS). RESULTS We identified 370 carriers of P/LP MODY gene variants (0.099%; 1 in 1,013), with similar carrier prevalence among European- and African-ancestry participants (0.105% in both groups). Diabetes penetrance was incomplete (13.4% by age 40; 43.5% by age 60) and varied by etiology: highest for GCK (56.0% by age 60), intermediate for HNF genes (HNF1A/HNF1B/HNF4A; 45.4%), and lowest for non-GCK/HNF genes (ABCC8/INS/KCNJ11/NEUROD1/PDX1/RFX6; 29.0%). In multivariable Cox models using non-carriers in the middle 80% of the T2D PRS as the reference, non-GCK/HNF gene variant carriers had modestly increased diabetes risk (HR, 1.57), similar to non-carriers in the top 10% of T2D PRS (HR, 1.64). These associations were observed in both European- and non-European-ancestry individuals. HbA1c profiles differed by etiology, with stable mild hyperglycemia in GCK variant carriers and greater variability among HNF and non-GCK/HNF gene variant carriers. CONCLUSIONS MODY gene variants showed incomplete, etiology-dependent penetrance across ancestries. Carriers of P/LP variants in lower-penetrance genes had diabetes risk comparable to that of non-carriers with high polygenic susceptibility.

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

PermaVid: Consistent Video Generation Across Edits via Disentangled Context Memory

Consistent video generation under editing operations requires persistence: when edits modify scene appearance or layout, subsequent generations should remain coherent across time and viewpoints. However, existing memory designs struggle to maintain long-term consistency after such modifications, as stored contexts may become outdated or invalid. To address this, we propose PermaVid, a novel framework built upon a multi-modal context memory that disentangles spatial context into semantic appearance and geometric structure, together with an edit-aware memory update and retrieval strategy that keeps memory evolution aligned with subsequent observations. Specifically, we develop two complementary memory banks: an RGB context memory that captures appearance-aware observations while implicitly encoding geometry, and a depth context memory that preserves geometry-only structure disentangled from semantics. Building on this design, we introduce a memory-guided video generation model that performs multi-modal feature fusion under reference conditions drawn from mixed-modality memory contexts. Experiments demonstrate that our method maintains strong long-term semantic and structural consistency after edits, significantly outperforming state-of-the-art methods.

07.
medRxiv (Medicine) 2026-06-12

Heterogeneity of Treatment Effect of Aspirin and Clinically Significant Bleeding in Older Adults

Aim: The global population of older adults is growing, and older age is linked to higher bleeding risk. Although guidelines discourage aspirin for primary prevention in healthy older adults due to bleeding harms outweighing benefits, many continue taking it without a clear indication. It remains unclear whether all older adults face uniform aspirin-related bleeding risk or if certain subgroups are more vulnerable. Methods: We analyzed data from 19,114 ASPREE trial participants to develop machine learning models using 116 baseline variables. Random forest (RF) and random survival forest (RSF) models predicted 5-year bleeding risk, and participants were stratified into low, intermediate, and high-risk groups based on the 20th and 80th percentiles of predicted risk. We assessed heterogeneity of treatment effect (HTE) by testing treatment-by-risk group interactions on the relative scale using Fine-Gray models, and on the absolute scale using observed 5-year cumulative incidence rates. Results: Over a median follow-up of 4.7 years, 626 major bleeding events occurred. The RF model had moderate discrimination (AUC = 0.65, 95% CI: 0.63-0.67) and good calibration (Brier = 0.032, 95% CI: 0.029-0.034). Statistically significant HTE was observed on the relative scale, with the greatest relative increase in bleeding risk seen in the low-risk group (subdistribution hazard ratio = 2.26, 95% CI: 1.27-4.01). On the absolute scale, low-risk participants experienced higher bleeding with aspirin (absolute risk difference (ARD) = 1.17%, 95% CI: 0.37-1.95), but heterogeneity in ARDs was not statistically significant (Cochran's Q p > 0.45). Similar findings were observed when using the RSF model. Conclusion: Participants at lowest baseline bleeding risk experienced the greatest relative increase in bleeding risk with aspirin therapy. We found statistically significant heterogeneity in treatment effects on the relative but not absolute scale. These findings support an individualized, risk-based approach to aspirin therapy decision-making in older adults.

08.
medRxiv (Medicine) 2026-06-12

Association of circulating endothelial progenitor cell count and functional outcome in patients with acute ischemic stroke due to intracranial large vessel occlusion

Background: Circulating endothelial progenitor cells (cEPCs) contribute to vascular repair following an ischemic stroke. The aim of the study was to evaluate the association between cEPCs and functional outcomes in patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) who received endovascular therapy (EVT). Methods: Prospective study of patients with LVO-AIS who received EVT. Blood samples were obtained within 24 +- 12 hours and on day 7+-1 from stroke onset. cEPCs were detected using flow cytometry (CD34+/VEGFR2+/CD133+). The primary endpoint was a favourable functional outcome (modified Rankin Scale 0-2) at three months of follow-up. Secondary endpoints include baseline to 24 hours/day 7 changes in the National Institutes of Health Stroke Scale (NIHSS) score and collateral circulation (CC) status. Bivariate and multivariable logistic regression analyses were performed. Results: Included were 90 patients (73.2+-12.7 years, 41.1% women) in 42 of whom (46.7%) cEPCs were detected at 24 hours. On day 7, cEPCs were detected in 27 (43.6%) of 62 patients for which this information was available. Atrial fibrillation, prior anticoagulant treatment and stroke onset-to-door time

09.
arXiv (CS.LG) 2026-06-11

Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

arXiv:2606.08493v2 Announce Type: replace-cross Abstract: Tissue graph counterfactuals ask how a cell's expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods targeting specific intervention types or treating cells as i.i.d. In this work, we first formalize tissue graph counterfactuals as a class of spatial interventions that either rewire connections between cells (edge perturbation) or modify the expression of their neighbors (node perturbation). We then introduce Cellina (https://cellina.readthedocs.io) - a framework that uses supervised disentanglement to decompose a cell's intrinsic state from its spatial context, using the latter as a conditioning input for counterfactual predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in colorectal cancer and mouse brain, Cellina outperforms spatially-informed and non-spatial competitors in in-silico graph perturbations, disentanglement, and scalability. Additionally, we show that Cellina reveals biologically distinct cancer subdomains in an unsupervised manner and enables targeted neighbor perturbation simulations.

10.
arXiv (CS.LG) 2026-06-15

Side-Channel Attacks Bypass Protection in 3D Printers

arXiv:2606.13952v1 Announce Type: cross Abstract: Active Motor Noise Cancellation (AMNC) ships in commercial fused deposition modeling (FDM) 3D printers as a hardware countermeasure against acoustic side-channel attacks that target intellectual property (IP). We present the first empirical evaluation of a deployed AMNC countermeasure, using a public dataset of synchronized acoustic and vibration recordings from two AMNC-equipped Bambu Lab printers across 12 object classes. AMNC fully neutralizes the acoustic channel: classification accuracy is indistinguishable from the 8.33% random baseline. The vibration channel, which AMNC does not target, still leaks. With summary statistics the leak is coarse and amplitude-driven (vibration accuracy approximately 31% pooled, 36-47% within-printer), while the waveform shape carries essentially nothing (frequency-only features at chance). A full-sequence temporal model that ingests the ordered evolution of the print raises accuracy to approximately 61%, and an order-shuffling control (approximately 33%) shows that a substantial component is genuinely sequential and tied to print progression. The leak is device-specific: a classifier trained on one printer transfers near chance to the other. We conclude that AMNC is an acoustic-only defense: vibration remains a partial, geometry-correlated side channel it does not address, but one that does not, on this dataset, support full geometric reconstruction; reconstruction-grade attacks would require the magnetic or power channels AMNC also leaves untouched. We release all code.

11.
arXiv (quant-ph) 2026-06-16

Conditions for Unitarity in Timeless Quantum Theory

arXiv:2504.01579v3 Announce Type: replace Abstract: Quantum timeless approaches solve the problem of time by recovering the usual unitary evolution of quantum theory relative to a clock in a stationary quantum Universe. For some Hamiltonians of the Universe, such as those including an interaction term with the clock, the dynamics is substantially altered and can be non-unitary. This work derives necessary and sufficient conditions for the relative dynamics to be unitary and finds the general form of the unitary evolution operator. A physical interpretation of these conditions is given in terms of the clock's rate. Unitary dynamics is associated with rates that are constant in time and independent of the clock's internal structure.

12.
arXiv (CS.LG) 2026-06-11

Bypassing Prompt Guards in Production with Controlled-Release Prompting

arXiv:2510.01529v4 Announce Type: replace Abstract: Ball et al. recently established that prompt filtering for AI alignment faces a fundamental barrier: under standard cryptographic assumptions, no filter running significantly faster than the protected model can universally distinguish adversarial prompts from benign ones. We investigate whether this impossibility result translates to real-world vulnerabilities in deployed large language model (LLM) systems. We answer affirmatively by introducing controlled-release prompting, a practical instantiation of the theoretical framework that exploits the resource asymmetry between lightweight input filters and the main models they protect. Unlike the theoretical construction, our attack does not require model modification: it generates malicious prompts that are indecipherable by any bounded filter yet remain tractable to the target LLM. We find our attack to be successful on four major chat platforms (Google Gemini, DeepSeek Chat, xAI Grok, and Mistral Le Chat) where baseline methods fail. Additionally, we apply our attack to extract copyrighted data from Gemini. Finally, we provide a systematic evaluation of 14 open-weight prompt guard models, revealing that even reasoning-capable filters cannot reliably detect our attack without incurring prohibitive resource overhead.

13.
arXiv (CS.CV) 2026-06-16

No One Knows the State of the Art in Geospatial Foundation Models

Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-observation tasks. Yet the published work about these models does not give reviewers or users enough information to tell which model fits a given task. We argue that nobody knows what the current state of the art is in geospatial foundation models. The methods may be useful, but the GFM literature does not standardize evaluations, training and testing protocols, released weights, or pretraining controls well enough for anyone to compare or rank them. In a 152-paper audit, we find 46 cross-paper disagreements of at least 10 points for the same model, benchmark, and protocol; 94/126 papers with extractable pretraining data use a configuration no other paper uses; and 39% of GFM papers release no model weights. This lack of community standards can be solved. We propose six concrete expectations: named-license weight release, shared core evaluations, copied-versus-rerun baseline annotations, variance reporting, one shared evaluation harness, and data-vs-architecture-vs-algorithm controls. These gaps are a coordination failure, not a fault of any individual lab; the authors of this paper, like many others in the GFM community, have contributed to them. Rather than just critiquing the community, we aim to provide concrete steps toward a shared understanding of how to innovate GFMs.

14.
arXiv (CS.CV) 2026-06-16

PhyloSDF: Phylogenetically-Conditioned Neural Generation of 3D Skull Morphology via Residual Flow Matching

Generating novel, biologically plausible three-dimensional morphological structures is a fundamental challenge in computational evolutionary biology, hampered by extreme data scarcity and the requirement that generated shapes respect phylogenetic relationships among species. In this work, we present PhyloSDF, a phylogenetically-conditioned neural generative model for 3D biological morphology that integrates two innovations: (1) a DeepSDF auto-decoder regularized by a novel Phylogenetic Consistency Loss that structures the latent space to correlate with evolutionary distances (Pearson r=0.993); (2) a Residual Conditional Flow Matching (Residual CFM) architecture that factorizes generation into analytic species-centroid lookup and learned residual prediction, enabling generation from as few as ~4 specimens per species. We evaluate PhyloSDF on 100 micro-CT-scanned skulls of Darwin's Finches and their relatives across 24 species. The model generates novel meshes achieving 88-129% of real intra-species variation at the code level, with all 180 generated meshes verified as non-memorized. Residual CFM surpasses denoising diffusion (which fails entirely at this scale), standard flow matching (which mode-collapses to 3-6% variation), and a Gaussian mixture baseline in both fidelity (Chamfer Distance 0.00181 vs. 0.00190) and morphometric Fr\'{e}chet distance (10,641 vs. 13,322). Leave-one-species-out experiments across 18 species demonstrate phylogenetic extrapolation capability, and smooth latent interpolations produce biologically plausible ancestral skull reconstructions.

15.
medRxiv (Medicine) 2026-06-22

Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records

Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by existing phenotype classifications based on onset site. Electronic health records (EHRs) contain detailed symptom documentation in clinical narratives that may enable data-driven discovery of clinically meaningful patient subgroups. Methods: We developed a natural language processing (NLP) pipeline using MedCAT to extract symptoms from clinical notes of 2,361 people with a confirmed diagnosis of MND at a tertiary neurology center. MND cohort confirmation used three complementary methods: clinic attendance records, text-based diagnosis detection, and NLP extraction with negation detection. Extracted symptoms were filtered to Unified Medical Language System semantic type T184 (Sign or Symptom) with removal of negated concepts. Patients were clustered using latent class analysis on binary symptom profiles. Survival differences were assessed using Kaplan-Meier analysis, log-rank tests, and Cox proportional hazards regression. Results: From the first clinical notes, we identified four clusters of symptoms among 872 patients and 76 symptoms: Motor-Bulbar (n=373), Motor-Tremor (n=154), Sensory-Pain (n=222), and Motor-Respiratory (n=123). When extended to all clinical notes (n=2,065; 184 symptoms), these reorganized into three clusters: Autonomic-Respiratory (n=472), Nocturnal-Respiratory (n=338), and Classic Motor (n=1,255). Survival differences were significant across all clusters in both the first notes and all notes analyses (log-rank p < 0.001). Conclusions: NLP-based symptom extraction from EHRs identifies clinically meaningful MND subgroups that extend beyond traditional onset-site classifications. Autonomic-respiratory symptom burden is associated with poorer survival while a newly identified Sensory-Pain subtype with a better prognosis. These data-driven phenotypes may improve prognostication and inform targeted supportive care.

16.
arXiv (math.PR) 2026-06-19

Hermite trace polynomials and chaos decompositions for the Hermitian Brownian motion

arXiv:2207.13180v4 Announce Type: replace Abstract: For a non-zero parameter $q$, we define Hermite trace polynomials, which are multivariate polynomials indexed by permutations. We prove several combinatorial properties for them, such as expansions and product formulas. The linear functional determined by these trace polynomials is a state for $q = \frac{1}{N}$ for $N$ a non-zero integer. For such $q$, Hermite trace polynomials of different degrees are orthogonal. The product formulas extend to the closure with respect to the state. The state can be identified with the expectation induced by the $N \times N$ Hermitian Brownian motion. Hermite trace polynomials are martingales for this Brownian motion, while the elements in the closure can be interpreted as stochastic integrals with respect to it. Using the grading on the algebra, we prove several chaos decompositions for such integrals, as well as analyze corresponding creation and annihilation operators. In the univariate, pure trace polynomial case, trace Hermite polynomials can be identified with the Hermite polynomials of matrix argument.

17.
arXiv (CS.LG) 2026-06-19

Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

arXiv:2606.20015v1 Announce Type: new Abstract: Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins. Existing SciML approaches struggle to accurately capture these localized responses. To address this challenge, this study proposes an adaptive-trunk DeepONet for localized structural response prediction in large-scale bridge systems. The framework dynamically constructs a load-dependent learning domain using a KNN strategy, allowing the network to focus on structural influence zones. The trunk network is further enhanced using distance-aware features that encode the geometric relationship between the load and structural nodes. A physics-based full-field reconstruction is incorporated through a stiffness-informed Schur complement formulation, enabling predictions at adaptive nodes to be extended to the entire structural domain. To enable scalable training, response data are generated using a reduced-order equivalent shell model that preserves the dominant global behavior while significantly reducing computational cost. The proposed framework is validated on both a benchmark bridge model and the real-world Mussafah Bridge. Results show that the method achieves FEM-level accuracy with relative errors below 5%, while reducing the total response evaluation time (including full-field reconstruction) by approximately 60x; excluding the post-processing reconstruction step, the AD-DeepONet inference is up to four orders of magnitude faster than FEM. In addition, the framework enables rapid generation of full-field responses, influence lines, and influence surfaces under arbitrary vehicular loading configurations, demonstrating strong potential for large-scale bridge analysis and digital twin applications.

18.
arXiv (CS.CV) 2026-06-19

HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining

Embodied foundation models are expected to benefit from data scaling like large language models, but face a much tighter data bottleneck. Teleoperated real-robot trajectories remain the dominant pretraining source due to their precise action supervision and embodiment alignment, yet their scalability is limited by high collection cost, acquisition difficulty, and low behavioral and environmental diversity. These limitations have sparked interest in egocentric human video as a scalable, substantially lower-cost, and more diverse alternative for embodied model pretraining. However, its effectiveness compared to teleoperated real-robot data remains underexplored. To address this question, we conduct a systematic study comparing egocentric human video and teleoperated real-robot trajectories as pretraining data sources for embodied foundation models, under fixed post-training and validation protocols. Surprisingly, we find that egocentric data, when processed through a carefully designed filtering and labeling pipeline, is not merely a viable substitute for model pretraining but can lead to superior performance. With the same amount of pretraining data, models pretrained on egocentric data achieve a 24% lower validation loss on real-robot action prediction, as well as 52.5% and 90% higher success rates on in-distribution and out-of-distribution real-robot task execution, respectively. This finding verifies a scalable paradigm for embodied foundation models: pretrain on egocentric human video to learn diverse world representations, then adapt with a small amount of labeled real-robot data for action-space alignment. We hope this study encourages broader exploration of egocentric data and offers guidance for data quality assessment before costly robot data collection.

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

FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty

Vision-language models (VLMs) are playing an increasingly important role across multiple domains. In many applications, such as robotics, it is crucial to quantify the uncertainty in the output of these models. } We develop FUSE, a probabilistic framework for capturing two complementary sources of uncertainty in vision-language modeling: (i) aleatoric embedding-level uncertainty derived from input data vision-language ambiguity, and (ii) epistemic model-level uncertainty estimated from the semantic response diversity of VLMs. Our approach formulates a Bayesian fusion mechanism that analytically combines these uncertainty sources to produce a scalar measure of uncertainty. This measure can be used to reliably predict the model's output correctness for downstream applications. We demonstrate that our method outperforms baselines and achieves SOTA uncertainty calibration.

20.
arXiv (CS.AI) 2026-06-16

Unifying Post-hoc Explanations of Knowledge Graph Completions

arXiv:2507.22951v2 Announce Type: replace Abstract: Knowledge Graphs organize information as entity-relation-entity triples, enabling machine learning models to predict plausible missing triples in a task known as Knowledge Graph Completion (KGC). Post-hoc explainability for KGC addresses the problem of identifying which triples most influence the predictions of machine learning models. Currently, the field lacks formalization and consistent evaluations, hindering reproducibility and cross-study comparisons. This paper argues for a unified taxonomy for post-hoc explainability in KGC. First, we propose a characterization of post-hoc explanations via multi-objective optimization that unifies existing post-hoc explainability algorithms in KGC and the explanations they produce, balancing explanation effectiveness and conciseness. Next, we examine improved evaluation protocols based on popular metrics, such as Mean Reciprocal Rank and Hits@k, through illustrative experiments. Finally, we stress the importance of interpretability as the ability of explanations to address queries meaningful to end users. By unifying methods and discussing evaluation standards, this work puts forward a case for more reproducible and impactful research in KGC explainability.

21.
bioRxiv (Bioinfo) 2026-06-18

A unified smoothing framework for protein domain bigram model

Biomolecular sequences can be represented as strings over an alphabet, an analogy that has motivated many applications of computational linguistic techniques to biological problems. However, such methods must be adapted to the characteristic scale and organization of biomolecular data. Here, we consider the problem of bigram smoothing for multidomain protein architectures, where domain bigram frequency data is extremely sparse and differs from textual data in alphabet size, string length distribution, the relationship between bigram and unigram frequencies, tandem repeat lengths, and the distribution of domain adjacencies. Moreover, some domain combinations are unobserved because they are biologically incompatible, others because the data are incomplete. A smoothing method that distinguishes these two cases is required. We propose a unified smoothing framework based on interpolation that can be tuned to accommodate different bigram data characteristics. Within this framework, we design specific model variants suited to protein domain bigram data: these assign low adjusted counts to pairs that are likely incompatible, while making appropriate adjustments for undersampled pairs. We demonstrate empirically that this approach distinguishes the two cases while preserving the characteristic signatures of multidomain data.

22.
arXiv (CS.AI) 2026-06-19

GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks

arXiv:2606.19566v1 Announce Type: cross Abstract: Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks. While machine learning methods, including graph neural networks, can localize which bus is compromised, significant challenges remain in data sharing and model training. For example, privacy regulations grant EVCS owners the right to delete their training data from a deployed model, yet retraining from scratch on every request is computationally prohibitive. To address this, we study graph unlearning (GU) for EVCS cyberattack localization, formulated as a feature-level unlearning problem on a graph-level multi-label classification task. Specifically, we propose gradient difference-based graph unlearning (GDGU), which removes the influence of the requested deletion data through a first-order parameter correction. The correction is computed from the gradient difference between the original training data and a modified dataset in which only the charging power features at the requested EVCS buses are unlearned. Then, a batch-normalization recalibration and a brief recovery fine-tuning step are applied to restore localization utility. We benchmark GDGU against two second-order GU baselines on the IEEE 34-bus, 123-bus, and 8500-node distribution networks across three graph neural network backbones and cumulative unlearning scenarios. GDGU matches the strongest baseline on localization utility and reaches forgetting fidelity close to full-retraining, while unlearning 10 to 12 times faster than retraining from scratch and using far less memory than the second-order GU baselines.

23.
arXiv (CS.LG) 2026-06-12

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

arXiv:2606.13133v1 Announce Type: cross Abstract: Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly matches theoretical lower bounds, making its generalization highly compelling. We address an open question raised in the work of Antoniadis et al., concerning the extension of this approach to other important problems outside the class of selection problems, such as scheduling. We develop a learning-augmented algorithm for the makespan minimization problem on unrelated machines, denoted by $R\|C_{\max}$. By using predictions of heavy job assignments, we achieve a polynomial-time $(1+\varepsilon)$-approximation for accurate predictions that smoothly degrades to a worst-case 2-approximation as the error increases. We conclude our work with an empirical analysis of our method.

24.
arXiv (CS.CV) 2026-06-19

OncoReg: Medical Image Registration for Oncological Challenges

In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue by enabling researchers to develop and validate image registration methods through a two-phase framework that ensures patient privacy while fostering the development of more generalisable AI models. Phase one involves working with a publicly available dataset, while phase two focuses on training models on a private dataset within secure hospital networks. OncoReg builds upon the foundation established by the Learn2Reg Challenge by incorporating the registration of interventional cone-beam computed tomography with standard planning fan-beam CT images in radiotherapy. Accurate image registration is crucial in oncology, particularly for dynamic treatment adjustments in image-guided radiotherapy, where precise alignment is necessary to minimise radiation exposure to healthy tissues while effectively targeting tumours. This work details the methodology and data behind the OncoReg Challenge and provides a comprehensive analysis of the competition entries and results. Findings reveal that feature extraction plays a pivotal role in this registration task. A new method emerging from this challenge demonstrated its versatility, while established approaches continue to perform comparably to newer techniques. Both deep learning and classical approaches still play significant roles in image registration, with the combination of methods, particularly in feature extraction, proving most effective.

25.
arXiv (CS.AI) 2026-06-16

LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

arXiv:2606.15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoals to enable dynamics-aware robot control. LaWAM achieves state-of-the-art or competitive success rates (SRs) across LIBERO (98.6% SR), RoboTwin (91.22% SR), and real-world manipulation tasks while retaining low-latency inference. LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.