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01.
arXiv (quant-ph) 2026-06-15

Spin-orbit coupling by design in quantum state engineering of atomically defined quantum dots

arXiv:2606.14487v1 Announce Type: cross Abstract: Tuning spin-orbit coupling is essential in controlling both spin and charge in confined semiconductor nanostructures, yet it is rarely a truly controllable parameter. Here, we show control over the spin-orbit Hamiltonian in quantum dots and the resulting quantum states by tailoring the confinement potential with atomic-scale precision. Using scanning tunnelling microscopy and spectroscopy, we pattern individual Cs ions into designer quantum dot structures on the surface of indium antimonide, in which electrons from a two-dimensional electron gas are confined with chosen in-plane electric-field gradients. We then quantify the atomic level structure, both spatially resolving the orbital character of the electronic states and their magnetic-field evolution. We demonstrate that the level structure, including the induced zero-field splitting, can be tailored by the designed geometry of the local electric fields. These effects can be described using a Hamiltonian that allows consistent treatment of the confinement-induced spin-orbit coupling beyond the conventional Bychkov-Rashba description. This Hamiltonian is derived from a multiband k.p model and takes the energy dependence of the relevant physical parameters into account. Such precise control of spin-orbit coupling in semiconductor quantum dots is relevant to quantum and spintronic technologies.

02.
arXiv (CS.CV) 2026-06-15

FBSDiff++: Improved Frequency Band Substitution of Diffusion Features for Efficient and Highly Controllable Text-Driven Image-to-Image Translation

With large-scale text-to-image (T2I) diffusion models achieving significant advancements in open-domain image creation, increasing attention has been focused on their natural extension to the realm of text-driven image-to-image (I2I) translation, where a source image acts as visual guidance to the generated image in addition to the textual guidance provided by the text prompt. We propose FBSDiff, a novel framework adapting off-the-shelf T2I diffusion model into the I2I paradigm from a fresh frequency-domain perspective. Through dynamic frequency band substitution of diffusion features, FBSDiff realizes versatile and highly controllable text-driven I2I in a plug-and-play manner (without need for model training, fine-tuning, or online optimization), allowing appearance-guided, layout-guided, and contour-guided I2I translation by progressively substituting low-frequency band, mid-frequency band, and high-frequency band of latent diffusion features, respectively. In addition, FBSDiff flexibly enables continuous control over I2I correlation intensity simply by tuning the bandwidth of the substituted frequency band. To further promote image translation efficiency, flexibility, and functionality, we propose FBSDiff++ which improves upon FBSDiff mainly in three aspects: (1) accelerate inference speed by a large margin (8.9$\times$ speedup in inference) with refined model architecture; (2) improve the Frequency Band Substitution module to allow for input source images of arbitrary resolution and aspect ratio; (3) extend model functionality to enable localized image manipulation and style-specific content creation with only subtle adjustments to the core method. Extensive qualitative and quantitative experiments verify superiority of FBSDiff++ in I2I translation visual quality, efficiency, versatility, and controllability compared to related advanced approaches.

03.
arXiv (CS.CV) 2026-06-25

Cross-View Variance Correlation in Path-Traced Stereo:A Hidden Shortcut in Synthetic Training Data

Authors:

Path-traced synthetic stereo data underlie a large fraction of modern disparity-estimation training pipelines. We report a previously unrecognised property of such data: while the Monte Carlo (MC) noise streams of the two cameras are statistically independent, the underlying variance fields – deterministic per-pixel functions of the rendering integrand – are highly correlated once aligned by the ground-truth disparity warp. Across 20 scenes rendered with Mitsuba~3, the warped Pearson correlation reaches $\rho{=}0.754{\pm}0.016$ across 20 scenes at $\mathrm{SPP}{=}512$, and on a representative scene remains essentially invariant ($\rho{=}0.778{\pm}0.001$) over a $16\times$ range of samples per pixel. The effect is strongest in Lambertian regions ($\rho{\approx}0.78$) and substantially weaker in glass ($\rho{\approx}0.30$), as predicted by an integrand decomposition into view-independent and view-dependent components. A residual-shuffle intervention that breaks the cross-view alignment while preserving the clean image degrades the GT cost margin by $33\%$ on non-glass and the variance-based winner-take-all accuracy on glass by $4.3\times$, confirming the structure functions as a matching cue. This signal is unique to MC-rendered data and constitutes a candidate sim-to-real shortcut whose impact on trained networks remains to be quantified.

04.
arXiv (CS.AI) 2026-06-12

CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts

arXiv:2606.13024v1 Announce Type: cross Abstract: Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-all" paradigm, struggling to capture distribution shifts and dynamic regime changes inherent in real-world time series. This often leads to entangled representations and spurious causal graphs. In this paper, we propose CausalMoE, a billion-scale multimodal Granger causal foundation model that explicitly models patch-level heterogeneity. CausalMoE introduces a Pattern-Routed Mixture of Heterogeneous Experts, which dynamically identifies latent temporal patterns and routes patches to specialized domain experts, effectively decoupling regime-specific mechanisms from shared dynamics. To ensure interpretable graph recovery, we design a Causality-Aware Self-Attention mechanism operating across variables, yielding sparse Granger causal graphs via proximal optimization. Furthermore, CausalMoE is the first to integrate LLMs and VLMs to align numerical signals with textual and visual priors, regularizing causal estimation in complex scenarios. Extensive experiments demonstrate that CausalMoE establishes a new state-of-the-art on fully supervised benchmarks, while effectively generalizing to few-shot settings where traditional methods fail.

05.
arXiv (CS.AI) 2026-06-24

Grad Detect: Gradient-Based Hallucination Detection in LLMs

arXiv:2606.24790v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applications. We present Grad Detect, a gradient-based approach for predicting hallucinations by analyzing layer-wise gradient patterns from a single forward-backward pass during inference. Our method shows that the internal gradient structure of a model carries rich information about the correctness of its output. This information is not accessible through output-level signals alone. We evaluate Grad Detect on several Q&A benchmarks across both hallucination detection and model abstention prediction, where it consistently outperforms confidence-based and sampling-based baselines. Through comprehensive layer ablation studies across all eleven models from four architectural families, we find that the final five layers concentrate over 97% of the discriminative gradient signal, enabling efficient deployment with minimal performance loss. Grad Detect provides a unified framework for predicting multiple dimensions of LLM reliability, offering strong predictive performance alongside interpretable insights into where and how model failures originate.

06.
arXiv (CS.LG) 2026-06-25

RotRNN: Modelling Long Sequences with Rotations

arXiv:2407.07239v3 Announce Type: replace Abstract: Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not well understood and they come with a number of drawbacks, most notably their complex initialisation and normalisation schemes. In this work, we address some of these issues by proposing RotRNN – a linear recurrent model which utilises the convenient properties of rotation matrices. We show that RotRNN provides a simple and efficient model with a robust normalisation procedure, and a practical implementation that remains faithful to its theoretical derivation. RotRNN also achieves competitive performance to state-of-the-art linear recurrent models on several long sequence modelling datasets.

07.
medRxiv (Medicine) 2026-06-24

Beyond Nodal Status: Interactions Between Molecular Subtype, Tumor Burden, and Survival in 12,225 Patients with Breast Cancer

Background Lymph node status and molecular subtype are among the most established prognostic factors in breast cancer. However, the extent to which their prognostic effects vary across different tumor size categories and clinical subgroups remains incompletely understood. We investigated the interplay between nodal status, molecular subtype, and tumor size in a large real world breast cancer cohort and developed a prognostic nomogram for individualized survival prediction. Methods A total of 12,225 women with invasive breast cancer from the Shiraz Breast Cancer Registry were analyzed. Patients were stratified according to tumor size, lymph node status, and molecular subtype. Overall survival (OS) and disease free survival (DFS) were evaluated using Kaplan Meier analyses and subgroup comparisons. Logistic regression was performed to identify predictors of lymph node involvement, while Cox regression was used to determine independent prognostic factors. A nomogram was subsequently developed and internally validated for prediction of 3-year and 5-year OS. Results Of 12,225 patients, 41.7% had lymph node positive disease. Across nearly all tumor size categories and molecular subtypes, nodal involvement was associated with significantly worse OS and DFS. Notably, the survival disadvantage associated with nodal positivity was more pronounced among patients with larger tumors and among those with HER2 positive and triple negative breast cancer (TNBC). Although TNBC demonstrated the lowest rate of lymph node involvement among molecular subtypes (adjusted OR 0.54, 95% CI 0.46-0.63), it appeared to show one of the largest survival gaps between node positive and node negative disease. In the overall cohort, survival outcomes generally ranked from best to worst as Luminal A, Luminal B, HER2 positive, and TNBC. However, survival differences among molecular subtypes were not consistently observed across all tumor size and nodal status subgroups. When significant differences were present, Luminal A and Luminal B tumors consistently showed superior outcomes compared with HER2 positive and TNBC tumors. Multivariable analysis identified lymph node status, tumor size, molecular subtype, lymphovascular invasion, tumor necrosis, type of surgery, radiotherapy, hormone therapy, and adjuvant chemotherapy as independent prognostic factors. A nomogram integrating clinicopathological and treatment variables demonstrated good predictive performance, with time dependent AUCs of 0.749 and 0.751 for 3 year and 5 year OS, respectively, and showed good calibration. Conclusions The prognostic impact of lymph node status is not uniform across breast cancer subgroups and appears particularly pronounced in larger tumors and biologically aggressive subtypes. Despite a lower likelihood of nodal involvement, TNBC showed substantial outcome deterioration when nodal metastasis was present. These findings highlight the importance of jointly considering nodal status, molecular subtype, and tumor burden in prognostic assessment.

08.
arXiv (CS.AI) 2026-06-16

Towards End-to-End Automation of AI Research

arXiv:2606.15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI. While the community has made significant progress in automating individual components of the scientific process, a system that autonomously navigates the entire research lifecycle – from conception to publication – has remained out of reach. Here, we present the strongest demonstration to date toward automating the entire process end-to-end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyzes data, writes the entire scientific manuscript and performs its own peer review. Its ideas, execution, and presentation are of sufficient quality to produce a manuscript generated by an AI system that passes the first round of peer review at a major machine learning conference workshop. The workshop has an acceptance rate of 70 percent. Our system leverages modern foundation models within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold to conduct research on a specific topic, and a template-free, open-ended mode that leverages agentic search for wider scientific exploration. Both settings produce diverse ideas and automatically test, report on, and evaluate them. This achievement demonstrates AI's growing capacity for scientific contribution and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be significant risks, including taxing overwhelmed review systems and adding noise to scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery.

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

Momentum LMS Theory beyond Stationarity: Stability, Tracking, and Regret

arXiv:2602.11995v2 Announce Type: replace Abstract: In large-scale data processing scenarios, data often arrive in sequential streams generated by complex systems that exhibit drifting distributions and time-varying system parameters. This nonstationarity challenges theoretical analysis, as it violates classical assumptions of i.i.d. (independent and identically distributed) samples, necessitating algorithms capable of real-time updates without expensive retraining. An effective approach should process each sample in a single pass, while maintaining computational and memory complexities independent of the data stream length. Motivated by these challenges, this paper investigates the Momentum Least Mean Squares (MLMS) algorithm as an adaptive identification tool, leveraging its computational simplicity and online processing capabilities. Theoretically, we derive tracking performance and regret bounds for the MLMS in time-varying stochastic linear systems under various practical conditions. Unlike classical LMS, whose stability can be characterized by first-order random vector difference equations, MLMS introduces an additional dynamical state due to momentum, leading to second-order time-varying random vector difference equations whose stability analysis hinges on more complicated products of random matrices, which poses a substantially challenging problem to resolve. Experiments on synthetic and real-world data streams demonstrate that MLMS achieves rapid adaptation and robust tracking, in agreement with our theoretical results especially in nonstationary settings, highlighting its promise for modern streaming and online learning applications.

10.
arXiv (CS.LG) 2026-06-17

Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds

arXiv:2606.18218v1 Announce Type: cross Abstract: We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained service resources. Arrivals may be dependent, time-varying, and adapted to the past; the standing load condition is uniform interior slack, meaning the conditional mean arrival vector stays in a fixed contraction of the capacity region. We show that this slack reshapes the finite-time peak law for drift-minimizing scheduling policies such as MaxWeight. The square-root envelope that is sharp without slack persists only up to a geometry-dependent threshold; beyond that threshold, the running maximum grows only logarithmically with the horizon, both with high probability and in expectation. The mechanism is self-normalization: in the current queue direction, the projected fluctuation scale is normalized by the stabilizing drift scale. This removes capacity geometry from the logarithmic coefficient, while geometry remains in the threshold. Matching lower bounds show that both the logarithmic term and a geometric threshold are unavoidable. When finite-time state-space collapse is available, the threshold can be sharpened using local bottleneck geometry. For generalized input-queued switches, we obtain finite-time peak bounds with tight logarithmic coefficients. Simulations illustrate the two-phase envelope, local geometric refinements, and variance-sensitive improvements predicted by the theory.

11.
arXiv (CS.CL) 2026-06-19

A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization

In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022. We choose to follow the popular hierarchical approach, i.e. condensing each document followed by aggregation and summarization. We propose a novel yet simple strategy to shorten documents that is driven by the golden summary, thus ensuring high correlation between stages of the hierarchical approach. Our method achieves a ROUGE2-F1 score of 0.2468 on the VLSP's public test set, and can produce fluent and concise summaries. Additionally, we utilize external sources for extra data, which greatly enhances the quantity of data for Vietnamese multi-document summarization. The additional data is made available for the community.

12.
PLOS Medicine 2026-05-29

Characterization of the VHH-Fc construct rimteravimab in healthy adults and patients hospitalized for mild-to-moderate COVID-19: Two Phase 1 randomized clinical trials

Authors:

by Ellen Jansen, Viki Bockstal, Florence Herschke, Per Olsson Gisleskog, Manuela Rinaldi, Angélique Boerboom, Salah Hadi, Natalia Gaibu, Michel Moutschen, Dominique Tersago Background Variable Heavy domain of Heavy chains (VHH) are innovative tools to target unique epitopes, yet few have been developed as heavy chain-only antibodies for clinical use. Rimteravimab (referred to here as XVR011) is a humanized antibody developed for the treatment of mild-to-moderate coronavirus disease 2019 (COVID-19), consisting of two identical VHHs targeting the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, with a human immunoglobulin (Ig) G1 fragment constant of antibody (Fc), silenced for Fc effector functions. We conducted two Phase 1 studies in healthy volunteers or hospitalized COVID-19 patients to evaluate its safety, tolerability, pharmacokinetics and immunogenicity. Methods and findings A randomized, double-blinded, single-center, placebo-controlled, single ascending dose study was performed in healthy volunteers (Phase 1a, EXEVIR0102, EudraCT 2021-003707-17), in parallel to an open-label, multi-center, single ascending dose study in patients hospitalized for mild to moderate COVID-19 (Phase 1b, EXEVIR0101, EudraCT 2020-005299-36, NCT04884295). Participants received a single intravenous infusion of 250, 500 or 1,000 mg of XVR011. The primary objective for both trials was the safety and tolerability of XVR011. Pharmacokinetics were evaluated as a secondary objective in Phase 1a and as an exploratory objective in Phase 1b. Efficacy (evaluated as respiratory parameters and COVID-19 clinical status) and antiviral activity in patients were evaluated as a secondary objective in Phase 1b. Immunogenicity was evaluated as an exploratory objective. Part 2 of the EXEVIR0101 study (initially a phase 1b/2 study) was not conducted due to the loss of XVR011 potency against SARS-CoV-2 Omicron BA.2. Demographics, safety, efficacy, and immunogenicity were analyzed using descriptive statistics, while pharmacokinetics were analyzed with noncompartmental pharmacokinetics (PK) modeling.In the Phase 1a study, there were no infusion-related reactions, serious treatment-emergent adverse events (TEAEs) or TEAEs grade ≥3. 22/30 volunteers (73.3%) reported 53 TEAEs (49 Grade 1, 4 Grade 2) with none being related to XVR011. The most common TEAE was headache (n = 8, 26.7%) in various treatment groups. In the Phase 1b study, 27 hospitalized patients were enrolled, and followed up to 30 days. Seven patients (25.9%) reported a total of 15 TEAEs, the majority (80%) being mild to moderate (Grade 1–2). There were no treatment-related serious TEAEs. All TEAEs resolved by the end of the study. Peak exposure (maximal concentration, Cmax) and systemic exposure (area under the curve, AUC0-t, and AUC0-inf) for XVR011 increased dose-proportionally. Geomean half-life ranged from 15.4 to 17.0 days in Phase 1a, while individual half-life ranged from 11.4 to 15.6 days in Phase 1b. SARS-CoV-2 viral load, as detected in nasopharyngeal samples by reverse transcription and quantitative polymerase chain reaction (RT-qPCR), decreased similarly in all cohorts compared to baseline. No treatment-induced anti-drug antibodies (ADA) were detected in Phase 1a. In Phase 1b, higher XVR011 concentrations increased the likelihood of ADA formation, without impacting pharmacokinetics and pharmacodynamics. No obvious dose-response in COVID-19 clinical status or respiratory parameters was observed.Technological limitations included study size, absence of placebo for the Phase 1b, absence of repeated dosing, evolving SARS-CoV-2 variants and standard-of-care. Conclusions XVR011 displayed a favourable safety, tolerability, pharmacokinetics, and immunogenicity profile, both in healthy volunteers and in patients hospitalized for mild to moderate COVID-19. These data pave the way for the design and clinical development of VHH-Fc constructs.

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

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606.15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models. In this paper, we propose Structured Nonparametric Variational Inference (SN-VI), a novel framework for modeling complex dependencies among latent variables in posterior approximation, leveraging multivariate spline techniques. Unlike traditional methods that rely on the mean-field assumption, SN-VI preserves intricate latent variable dependencies, providing a flexible and accurate approximation of posteriors with arbitrary shapes. We establish rigorous theoretical guarantees, including the derivation of the lower bound for the variational objective and proof of asymptotic consistency in posterior estimation. To facilitate practical implementation, we develop an algorithm that automatically identifies dependent latent variables and their underlying dependence structure, without requiring manual specification. Simulation studies validate the effectiveness of SN-VI in approximating posterior distributions with bounded support and complex dependencies. The proposed method has been successfully applied to high-dimensional structured data, including computer vision datasets and spatial transcriptomics. In these applications, SN-VI demonstrates improved generative model performance and effectively uncovers coupled biological signals through the learned dependency structure.

14.
arXiv (CS.AI) 2026-06-15

VikingMem: A Memory Base Management System for Stateful LLM-based Applications

arXiv:2605.29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions. Existing memory approaches often rely on simplistic extraction methods that lead to incomplete memories or use rigid, single-purpose memory extraction prompts tailored to a single use case, such as chatbots. Consequently, they lack generalizability and perform poorly across diverse downstream tasks. To bridge this gap, we introduce the Memory Base, a novel data management paradigm for managing the persistent state of long-term interactions. It is characterized by three core principles: selective extraction of high-value memories from raw information streams; inherent statefulness and evolution, where memory content is progressively summarized, corrected, and temporally weighted to prioritize recent interactions; and a generalizable abstraction paradigm designed for robust transferability across diverse applications, including education, recommendation, and agent memory. Building on this foundation, we present VikingMem, an end-to-end Memory Base Management System implemented on the VikingDB vector engine. VikingMem materializes this paradigm through interconnected event and entity abstractions. It features event-centric memory extraction to selectively handle complex information streams, while entities are dynamically updated by events to achieve stateful evolution. Using temporal compression via a topic-wise timeline and time-weighted recall, the system progressively produces high-level summary memories, prioritizes recent items, and compresses and fades older ones. Extensive evaluations on long-term memory benchmarks demonstrate that VikingMem outperformes baselines by up to 30% in memory retrieval effectiveness while maintaining the low latency essential for interactive applications.

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

CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees

arXiv:2606.12840v1 Announce Type: new Abstract: Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing well-performing regression trees. While optimal methods based on dynamic programming and branch-and-bound exist, they are computationally prohibitive for general linear regression trees, despite often achieving substantially better performance than greedy approaches. Recent work has shown that specialized lookahead strategies can dramatically improve runtime while maintaining near-optimal performance, primarily in classification settings. In this work, we develop a novel algorithm for near-optimal, sparse, piecewise linear regression trees that combines a lookahead-style search strategy with efficient rank-one Cholesky updates of the Gram matrix. We demonstrate, both theoretically and empirically, that our method achieves a favorable trade-off between computational efficiency, predictive accuracy, and sparsity, and scales significantly better than the current state of the art.

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

Co-Scraper: query-aware DOM Pruning and Reusable Scraper Synthesis for Lightweight Web Data Extraction

arXiv:2606.14821v1 Announce Type: cross Abstract: The abundant and heterogeneous nature of web content necessitates automated information extraction, and generating scrapers that can be reused across similar web pages offers an effective solution for scalable data extraction. In this work, we propose Co-Scraper, a two-stage framework capable of handling the hierarchical complexity of long HTML documents. By integrating a query-aware DOM pruning mechanism with stable extraction strategy induction, Co-Scraper can effectively transforms web content into executable programmatic wrappers using a fine-tuned Qwen3-8B model. On the test set of SWDE, Co-Scraper achieves state-of-the-art performance with an F1 score of 94.78% and a reuse success rate of 90.39%. This framework significantly enhances the accuracy and resilience of data extraction, providing a highly efficient approach for web data acquisition tasks.

17.
arXiv (CS.AI) 2026-06-11

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics

arXiv:2606.11657v1 Announce Type: cross Abstract: Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition. This raises a central evaluation and interpretability question: when a foundation-style model can reproduce known continuum dynamics, what internal mechanism supports that behavior, is the internal behaviour consistent with known physics, and how does it relate to where the emulator succeeds or fails? We investigate a cross-domain foundation model for continuum dynamics, Walrus by Polymathic, using mechanistic interpretability guided by physical principles. We apply a sparse autoencoder (SAE) to probe a selected layer, and address the practical challenge of triaging a large feature set (over 20,000) using enstrophy as a physically grounded metric. As a deliberately simple testbed, we focus on shear flow and compare feature recruitment across multiple shear-flow setups, i.e. parameter values in the numerical simulation. Across setups we find evidence of piecewise consistency, with subsets of features recurring in similar roles, but this structure is intermittent and does not map cleanly onto standard physical decompositions. In parallel, direct comparisons between numerical simulation and the emulator reveal systematic output-level discrepancies, including regimes where energy/structures become too diffuse or too localized. We connect parts of these discrepancies to changes in specific SAE feature usage. Our work highlights open questions for scientific foundation models: how to robustly prioritize mechanistically meaningful features, how to separate stable structure from analysis artifacts (including single-layer and SAE limitations), and how to use established benchmarks to decide when "different" internal representations are genuinely informative rather than merely effective.

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

Quantifying and Auditing LLM Evaluation via Positive–Unlabeled Learning

arXiv:2606.19057v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as judges for scalable evaluation, yet such LLM–as–a–Judge systems exhibit systematic biases that are decoupled from semantic quality, most notably verbosity bias. Meanwhile, human supervision is costly and typically selective, yielding reliable positive judgments but leaving most outputs unlabelled and potentially mixed in quality. We formulate LLM evaluation under selective human supervision as a positive–unlabelled learning problem and propose a geometric auditing framework based on Partial Optimal Transport. By aligning a small set of human–verified positives with a reliable subset of unlabelled outputs in a fixed embedding space, our method identifies human–consistent preferences and corrects biased judges without retraining. Experiments demonstrate improved alignment with human preferences, increased robustness to presentation biases, and interpretable confidence estimates, offering a scalable and statistically grounded alternative to existing LLM–as–a–judge pipelines.

19.
arXiv (quant-ph) 2026-06-24

The $\omega$-Effect from a Multimode Squeezed Graviton State

arXiv:2606.24613v1 Announce Type: cross Abstract: The $\omega$-effect in entangled neutral-meson systems provides a sensitive probe of CPT violation induced by quantum-gravitational environments. In open quantum systems, interactions with inaccessible gravitational degrees of freedom can render the reduced meson dynamics non-unitary, causing the CPT operator to become ill-defined, even when the underlying microscopic Hamiltonian is CPT invariant. We present a microscopic derivation of the $\omega$-effect arising from a multimode squeezed gravitational environment generated by an axion cloud around a Kerr black hole. Using the Takagi decomposition of the associated complex symmetric squeezing kernel, the graviton field is expressed in terms of independent squeezed supermodes possessing anomalous correlators. These correlators provide a microscopic quantum counterpart of the stochastic fluctuations that appear in earlier D-particle foam descriptions of the $\omega$-effect, replacing phenomenological variances of flavour-changing D-particle recoil by calculable graviton correlation functions. After tracing over the graviton bath, the anomalous correlators and the weak-interactions-induced mixing combine to generate transitions between the antisymmetric and symmetric two-meson sectors. This results in a small exchange-symmetric admixture, parametrised by $\omega$, in the otherwise antisymmetric EPR state. We obtain an explicit expression for $\omega$ in terms of a sum over Takagi supermodes weighted by their squeezing amplitudes and phases together with the weak-interaction flavour-mixing matrix element. The resulting framework suggests that the $\omega$-effect may be a generic signature of non-classical states of gravitational environments, extending beyond the specific axion-cloud scenario considered here. The observability of the $\omega$-effect from other astrophysical and microscopic black-hole sources is discussed.

20.
arXiv (quant-ph) 2026-06-19

Quantum Batteries as Work Sources for Phase-Locked Parametric Amplification

Authors:

arXiv:2606.20306v1 Announce Type: new Abstract: Quantum batteries have been proposed as locally precharged work sources for superconducting quantum technologies, suggesting a route to reduce continuously supplied microwave drives. Here we ask whether the pump tone of a quantum-limited parametric amplifier can be replaced, or strongly duty-cycled, by a finite bosonic quantum battery. Quantizing the pump of a nondegenerate parametric amplifier exposes a resource distinction hidden in the classical description: stored pump energy can generate signal-idler photons, but pump phase coherence is required to generate a phase-locked amplifier field. In a closed trilinear model, coherent and phase-randomized coherent pumps with the same photon-number distribution produce comparable pair numbers, yet only the coherent pump produces anomalous two-mode coherence and an EPR-squeezed interference dip. Including leakage, we collect the emitted fields into cascaded temporal modes. At matched collector bandwidth, the coherent pump gives \(I_{\min}^{(f)}=0.553\), whereas the phase-randomized pump gives \(I_{\min}^{(f)}=1.94\) at nearly identical collected energy. Weak amplitude squeezing slightly improves the dip by reducing finite-pump number fluctuations while preserving the coherent displacement. Thus battery-powered parametric amplification requires phase-coherent stored energy, possibly assisted by number-noise reduction, rather than stored energy alone.

21.
arXiv (CS.LG) 2026-06-17

Learn from Your Mistakes: Self-Correcting Masked Diffusion Models

arXiv:2602.11590v3 Announce Type: replace Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance. Despite these advantages, MDMs face a fundamental limitation: once tokens are unmasked, they remain fixed, leading to error accumulation and ultimately degrading sample quality. We address this by proposing a framework that trains a model to perform both unmasking and correction. By reusing outputs from the MDM denoising network as inputs for corrector training, we train a model to recover from potential mistakes. During generation we apply additional corrective refinement steps between unmasking ones in order to change decoded tokens and improve outputs. We name our training and sampling method Progressive Self-Correction (ProSeCo) for its unique ability to iteratively refine an entire sequence, including already generated tokens. We conduct extensive experimental validation across multiple conditional and unconditional tasks, demonstrating that \method~yields better quality-efficiency trade-offs (up to ~4x faster sampling) and enables inference-time compute scaling to further increase sample quality beyond standard MDMs (up to ~1.2x improvement on benchmarks).

22.
medRxiv (Medicine) 2026-06-12

Metastatic Patterns and Treatment Characteristics of Triple-Negative Breast Cancer in Nigeria: A Retrospective Cohort Study

Background: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype characterized by the absence of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. It is associated with limited targeted treatment options, early relapse, and a high propensity for visceral metastasis. Data describing metastatic patterns and treatment characteristics of TNBC in Nigeria remain limited. Methods: This retrospective descriptive cohort study included 869 patients with TNBC managed at the Medserve-LUTH Cancer Center, Lagos University Teaching Hospital, Nigeria between June 2019 and June 2024. Demographic, clinicopathologic, metastatic, and treatment-related data were extracted from electronic medical records. Descriptive statistics were used to summarize patient characteristics, metastatic patterns, and treatment profiles. Associations between metastatic disease and selected clinicopathologic and treatment variables were explored using Pearsons chi-square test. Complete-case analysis was applied throughout. Results: The mean age at presentation was 52.09 {+/-} 12.26 years. Most patients were married (79.1%), postmenopausal (64.3%), and of Yoruba ethnicity (56.8%). Advanced disease predominated, with Stage III and Stage IV disease accounting for 42.9% and 35.6% of cases, respectively. Invasive ductal carcinoma was the most common histologic subtype (77.0%), while Grade II tumours constituted 51.3% of graded cases. Surgery was performed in 73.1% of patients, predominantly mastectomy (70.9% of surgical procedures). Chemotherapy was administered to 83.2% of patients, most commonly anthracycline-based regimens (41.8%), while radiotherapy was delivered to 63.5% of patients, with hypofractionated schedules of 42-43 Gy in 15-16 fractions accounting for 47.2% of radiotherapy courses. Metastatic disease was documented in 32.9% of evaluable patients. Lung metastasis was the most frequent site (62.5%), followed by bone (46.3%), regional lymph node invasion (38.5%), liver (23.0%), and brain (22.6%). Tumour grade and histologic subtype were not significantly associated with metastatic disease, whereas radiotherapy exposure demonstrated a significant association with metastatic status ({chi}{superscript 2} = 10.35, p = 0.001). Conclusion: TNBC in this Nigerian cohort was characterized by advanced-stage presentation, invasive ductal predominance, extensive use of multimodality treatment, and substantial visceral metastatic burden. Lung metastasis was the most common metastatic site. These findings provide contemporary real-world data on TNBC in Nigeria and highlight the continuing need for earlier diagnosis, timely referral, and sustained investment in comprehensive cancer care services.

23.
bioRxiv (Bioinfo) 2026-06-17

Beyond phylogeny: Genome-wide DNA sequence patterns suggest DNA physical properties associated with thermal adaptation in extremophile microbes

Authors:

Temperature is a fundamental constraint on biological systems, yet how it is reflected in genome sequence organization remains unclear. Here, we show that genome-wide distributions of short DNA sequences contain a robust signal of thermal adaptation that is largely independent of phylogeny. Using Structural Topic Modelling (STM), a machine-learning approach for identifying groups of co-occurring sequence motifs, we analyze canonical 6-mer and 9-mer frequency profiles of bacterial and archaeal genome proxies (randomly sampled genomic regions) and identify motif families systematically associated with thermophiles and psychrophiles. In bacterial thermophiles, the identified motif families are dominated by highly specific, overrepresented and co-occurring C- and G-stacked hexamers, and a distinct family of CG-periodic hexamers recurring across multiple temperature comparisons. In contrast, bacterial psychrophile-associated motifs are dominated by low-complexity A-, T-, and AT-run hexamers. Thermophilic archaea generally exhibit a distinct CTAG-centred hexamer family, suggesting that different domains may adapt to similar environmental constraints through different sequence-level solutions. However, this domain-level contrast is not absolute: in a targeted analysis of two thermophilic bacterium–archaeon pairs, we find unusually similar frequencies of all the STM-identified thermophile-associated hexamer families, suggesting that shared high-temperature environments can, in specific cases, partially override phylogenetic divergence. Notably, the identified motif families constitute only a small and highly selective subset of the vast space of possible G+C-rich or A+T-rich sequences. This indicates that thermal adaptation is associated with specific sequence architectures rather than broad shifts in nucleotide composition. Accordingly, the observed signal cannot be explained by overall base composition alone, but instead arises from structured combinations and positional arrangements of nucleotides within short sequence contexts. Related motif families are recovered at both k=6 and k=9, indicating that the signal reflects systematic shifts in genome-wide sequence organization rather than isolated sequence motifs. These patterns are consistent with known sequence-dependent DNA physical properties documented in biochemical and biophysical studies, including differences in base-stacking interactions and conformational flexibility. Together, our results suggest that genome-wide sequence organization reflects sequence-dependent DNA physical properties associated with thermal adaptation, revealing a previously underappreciated physical layer of genomic information beyond phylogenetic history.

24.
arXiv (quant-ph) 2026-06-12

Quantum charge pumping in helical systems: A comparative study of short- and long-range hopping

arXiv:2606.12914v1 Announce Type: cross Abstract: Using the Keldysh non-equilibrium Green's function approach, we investigate charge pumping through a single-stranded helical structure described by a tight-binding model that includes either short-range hopping (SRH) or long-range hopping (LRH). While quantum pumping has been studied in various low-dimensional systems, the detailed behavior of the spectral current and the pumped dc current in helical geometries in the presence of higher-order electron hopping (beyond nearest neighbors) has not yet been systematically explored. Here, we focus on the interplay between helicity and extended hopping ranges, analyzing how they jointly control the energy-resolved and dc pumped currents under time-periodic end potentials. For LRH, the pumped dc current exhibits pronounced plateau-like regions as a function of chemical potential when energy levels are sparsely spaced – consistent with adiabatic transport – whereas SRH yields more parameter-sensitive currents without clear plateaus. The plateau stability is controlled by the drive frequency: at higher frequencies, Floquet side-band mixing destroys the plateaus, leading to oscillatory currents. The phase dependence remains nearly sinusoidal, and the current vanishes at zero phase lag, confirming the necessity of out-of-phase potentials. Crucially, in helical systems, the decay exponent $(\ell_c)$ acts as an effective structural parameter that can tune both the magnitude and sign of the pumped current, offering a geometric knob for controlling quantum pumping. Our findings not only fill a gap in the understanding of spectral and pumped currents in helical systems with extended hopping but also provide tools that can be applied to analyze similar phenomena in other chiral or quasi-one-dimensional systems.

25.
arXiv (quant-ph) 2026-06-12

Relativistic Locality from Electromagnetism to Quantum Field Theory

arXiv:2412.11532v2 Announce Type: replace Abstract: Electromagnetism is the paradigm case of a theory that satisfies relativistic locality. This can be proven by demonstrating that, once the theory's laws are imposed, what is happening within a region fixes what will happen in the contracting light-cone with that region as its base. The Klein-Gordon and Dirac equations meet the same standard. We show that this standard can also be applied to quantum field theory (without collapse), examining two different ways of assigning reduced density matrix states to regions of space. Our preferred method begins from field wave functionals and judges quantum field theory to be local. Another method begins from particle wave functions (states in Fock space) and leads to either non-locality or an inability to assign states to regions, depending on the choice of creation operators. We take this analysis of quantum field theory (without collapse) to show that the many-worlds interpretation of quantum physics is local at the fundamental level. We argue that this fundamental locality is compatible with either local or global accounts of the non-fundamental branching of worlds, countering an objection that has been raised to the Sebens-Carroll derivation of the Born Rule from self-locating uncertainty.