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01.
medRxiv (Medicine) 2026-06-24

Screen-Free Haptic Breathwork with HRV-Adaptive Control, Pilot Outcomes and System Design

Vayu is a mobile breathwork system comprising an iOS companion app and Apple Watch application that delivers slow, resonant breathing using screen-free haptic cues, HRV-adaptive pacing, and reflective journaling grounded in Patanjali's five states of mind. The watchOS component provides tactile phase guidance and real-time biometric sensing (heart rate, HRV), while the iOS interface supports analytics and personalized recommendations. In a 4-6-week naturalistic pilot involving 199 adults (ages 22-65) across Canada, the United States, and India, participants engaged in daily 5-10-minute sessions guided by on-wrist haptics. Average adherence was 4.1 +/- 2.3 sessions per week, with 71% of active users maintaining at least 3 sessions per week. By week four, perceived stress (PSS-10) decreased by 2.5 points, resting heart rate declined by 7.4 bpm, and HRV increased by a median of 28.6% relative to baseline, accompanied by mood improvements. No adverse events were reported. HRV metrics are derived from Apple Watch PPG-based proxies and interpreted as relative trends. These findings suggest Vayu is effective and well-tolerated, demonstrating strong engagement and early efficacy signals.

02.
bioRxiv (Bioinfo) 2026-06-11

DLDN-Bench: A Benchmark Framework for Deep Learning de Novo Peptide Sequencing in Proteomics

De novo peptide sequencing is an essential approach for analyzing mass spectrometry data because it enables the identification of novel peptides without relying on protein sequence databases. Recent advances in deep learning have substantially improved the performance of de novo sequencing methods, but the rapid emergence of new models has led to heterogeneous evaluation practices and limited comparability. To address this, we introduce DLDN-Bench, a benchmark framework including a set of benchmark datasets derived from human muscle biopsy mass spectrometry data retrieved from PRIDE and annotated through consensus across multiple widely used database search engines. Using these datasets, we systematically benchmark recent deep learning-based de novo sequencing tools alongside traditional approaches. Performance is assessed using established metrics, including precision and coverage relative to a pseudo-ground truth defined by cross-engine agreement. To demonstrate the utility of DLDN-Bench, we benchmark four recent deep learning models and make all results publicly available. This benchmark framework provides a standardized basis for comparing state-of-the-art methods and offers an extensible resource for evaluating future tools in de novo peptide sequencing.

03.
medRxiv (Medicine) 2026-06-15

Fanconi Anemia as a Window into Premalignant Field Cancerization of the Oral Mucosa

Head and neck squamous cell carcinoma (HNSCC) evolves through stepwise clonal expansion within genetically altered mucosa fields, yet actionable biomarkers remain undefined. Leveraging Fanconi anemia (FA), a cancer predisposition syndrome with extreme HNSCC risk due to defective DNA interstrand crosslink repair, we profiled premalignant changes in the oral cavity using noninvasive brush biopsies. Consistent with our prior demonstration of genomic instability in FA-associated SCCs, we detected pathogenic TP53 variants in 26% and copy number alterations in 60.5% in clinically normal-appearing oral mucosa of individuals with FA. These subclinical clonal expansions define candidate biomarkers of early clonal evolution amenable to serial sampling for risk stratification and prevention studies. Since FA-associated SCCs share genomic features with sporadic HNSCC, these findings may extend to the broader population. We also identify somatic reversion of a pathogenic FANCB variant, providing evidence of genomic self-correction and suggesting a potential avenue for gene-based cancer prevention in FA.

04.
arXiv (CS.LG) 2026-06-17

Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks

arXiv:2605.00725v2 Announce Type: replace Abstract: Topological neural networks have emerged as effective tools for modeling higher-order relational structures beyond pairwise graphs, including hypergraphs, simplicial complexes, and cell complexes. However, existing Weisfeiler-Leman type expressivity analyses are typically developed on different structural domains and rely on domain-specific neighborhood systems, making their expressive powers difficult to compare within a common formalism. In this paper, we introduce the Combinatorial Complex Weisfeiler-Leman (CCWL) framework, a unified expressive power refinement defined on combinatorial complexes. By exploiting the ability of combinatorial complexes to represent both set-type relations and part-whole hierarchies, CCWL performs topological color refinement through four structural neighborhoods: boundary, co-boundary, lower adjacency, and upper adjacency. We show that, under specified lifting maps, CCWL can simulate several domain-specific WL-type refinements, thereby providing a common theoretical baseline for analyzing topological message passing. We further study the neighborhood sufficiency problem and prove that, under explicit coverage conditions, a reduced refinement using only lower- and upper-adjacent bridge information preserves the distinguishing power of the full four-neighborhood CCWL refinement. Guided by this theoretical result, we instantiate the reduced refinement as the Combinatorial Complex Isomorphism Network (CCIN). Experiments on synthetic and real-world benchmarks demonstrate that CCIN achieves competitive performance against representative graph and topological neural network baselines. Ablation studies and resource-efficiency analyses further support the effectiveness of the proposed lower/upper-neighborhood design.

05.
arXiv (CS.LG) 2026-06-16

The Data Manifold under the Microscope

arXiv:2606.15760v1 Announce Type: new Abstract: A significant gap exists between theory and practice in deep learning. Generalization and approximation error bounds are often derived for simplified models or are too loose to be informative. Many rely on the manifold hypothesis and on geometric regularity such as intrinsic dimension, curvature, and reach. Progress requires insight into data-manifold geometry and suitable benchmarks, yet existing options are polarized: analytic manifolds with known geometry but limited applicability, or real-world datasets where geometry is only coarsely estimable. We introduce a benchmarking framework for studying data geometry. We repurpose and extend dSprites and COIL-20 with additional transformation dimensions and dense, axis-aligned sampling, and pair them with finite-difference estimators that recover curvature, reach, and volume at near-ground-truth accuracy in a regime where general-purpose estimators are unreliable or difficult to deploy. The framework is intended as a controlled testbed, useful as a calibration environment for geometric estimators and a sandbox for probing theoretical assumptions. To illustrate its use, we present two application studies, namely assessing the scaling behavior of the bounds of Genovese et al. and Fefferman et al., and tracking the layer-wise geometry of a $\beta$-VAE, highlighting the behavior of current bounds and the value of controlled benchmarks for guiding and validating future theory. A reference implementation is available at https://github.com/koulakis/manifold-microscope.

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

Triangle Splatting SLAM

We present a dense RGB-D SLAM system using differentiable triangles as the 3D map representation. While 3D Gaussian Splatting has emerged as the leading method for novel-view synthesis, triangles remain the standard primitive for traditional rendering hardware, game engines, and downstream tasks requiring explicit geometry such as simulation, collision, and editing. Recent offline methods have demonstrated that an unstructured 'triangle soup' can be optimised into a photorealistic mesh via Delaunay triangulation across a set of posed images. Building upon this insight, we present the first dense SLAM system to employ Triangle Splatting to perform both tracking and mapping through online differentiable rendering of a triangle soup. The map can be converted into a connected mesh on-the-fly via restricted Delaunay triangulation, enabling new online capabilities such as mesh deformation and collision checking. On Replica and TUM-RGBD, our system outperforms baselines on 3D geometry, matches the camera-tracking accuracy, and enables online mesh-based scene editing.

07.
arXiv (math.PR) 2026-06-25

Selection principles for quasi-stationary distributions and reinforcement processes

arXiv:2606.25857v1 Announce Type: new Abstract: Let \(P\) be a sub-Markov matrix on a finite set \(S\), representing the transition probabilities of a Markov chain on \(S\) absorbed at a cemetery point \(\partial\notin S\). We consider a reinforced process \((X_n,\mu_n)\) defined as follows: \((X_n)\) behaves like a chain with kernel \(P\) until it dies, and when it dies at time \(n\), it is instantaneously ``resurrected'' at a point sampled according to its weighted past occupation measure \[ \mu_n = \frac1{W_n} \left( w_0\mu_0+\sum_{k=1}^n w_k\delta_{X_k} \right), \qquad W_n=\sum_{k=0}^n w_k, \] where the positive weights $w_k$ satisfy certain technical assumptions, a typical example being given by $w_k = k^q$, with $q\geq -1$. When \(P\) is irreducible, the behaviour of \((\mu_n)\) is well understood [AFP], [bansaye2022non]: it converges almost surely toward the unique quasi-stationary distribution (QSD) of \(P\). The purpose of this paper is to investigate the general situation where \(P\) is not irreducible. Under generic assumptions on \(P\), there are finitely many QSDs. We prove that the asymptotic selection depends on the summability of the inverse cumulative weights \(1/W_n\). If \[ \sum_{n\geq0}\frac1{W_n}=\infty, \] then \((\mu_n)\) almost surely converges toward the QSD associated with the largest Perron value. If instead \[ \sum_{n\geq0}\frac1{W_n}0\).

08.
arXiv (quant-ph) 2026-06-15

Modeling light-matter coupled systems with neural quantum states

arXiv:2606.14352v1 Announce Type: cross Abstract: Recent advances in cold atom manipulation enable the study of many-body systems where short-range interactions between neighboring atoms coexist with long-range interactions mediated by photons. Such a combination of interactions makes a theoretical approach challenging beyond mean-field methods. In this work, we develop a neural quantum state based approach to study these systems numerically. We introduce a neural-network architecture capable of handling hybrid Hilbert spaces with large local bosonic dimensions in strongly interacting spin-photon systems. We benchmark this approach on a model of a two-dimensional lattice of Rydberg atoms coupled to a photon mode. The superradiant ground states found in the large spin-photon coupling regime allow us to demonstrate the efficiency of the method in the presence of high photon occupation. Furthermore, the ability to capture spin-spin and spin-photon correlations leads us to observe quantitative deviations in the ground state phase boundaries with respect to mean-field theory. The method extends to other systems with a similar hybrid Hilbert space structure, such as spin-phonon systems, and provides a scalable framework for investigating their ground state properties.

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

Environment-Grounded Automated Prompt Optimization for LLM Game Agents

LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns. We propose a behavior analyzer to attribute episode outcomes to specific prompt components, and a mutator to propose targeted revisions to the prompt, before validating them through environment rollouts. We evaluate on all five BabyAI tasks in the BALROG benchmark, comparing our pipeline against BALROG's RobustCoTAgent under both plain and guided prompt initializations. Optimization improves performance consistently across tasks and conditions, without requiring updates to the model weights. On PutNext, a multi-step coordination task where the RobustCoTAgent achieves 0% success, our framework reaches up to 72.5% success rate using the same underlying LLM with optimized prompts. These results suggest that a multi-agent framework, combined with automatic prompt optimization, enhances LLMs without the need for fine-tuning or extensive human supervision.

10.
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.

11.
medRxiv (Medicine) 2026-06-22

Reliable quantification of renal function from frozen blood samples

BACKGROUND: Differences in renal function may affect Alzheimer disease (AD) blood biomarker levels independent of AD pathology. Although renal function was unaccounted for in foundational AD blood biomarker studies, there is potential to address this through quantification of estimated glomerular filtration rate (eGFR) from frozen serum and plasma samples. However, the validity of eGFR evaluation from long-term frozen blood samples is unknown. METHODS: Adults aged 50-85 with at least 2 vascular risk factors were recruited from vascular surgery or cardiology clinics in Tucson, Arizona from 2022-2025. Individuals with creatinine assessments in point-of-care whole blood (POC-WB) and frozen serum and plasma samples using the iSTAT (Abbott) were included. eGFR was calculated using the 2021 CKD-EPI creatinine equation without race. Agreement between POC-WB and frozen blood samples was assessed using Cohen's kappa with linear weights. RESULTS: 134 participants (mean [SD] age: 72.6 [7.5] years, 39.6% female, 23.1% chronic kidney disease) had POC-WB eGFR available. Frozen serum and plasma samples had strong agreement with POC-WB for eGFR (Kw= 0.90-0.95, P

12.
bioRxiv (Bioinfo) 2026-06-08

TRACEY: an updated resource for SNARE protein domain annotation with improved HMMs and expanded sequence coverage

Motivation: SNARE proteins catalyse membrane fusion across the eukaryotic endomembrane system, from synaptic vesicle exocytosis to intracellular trafficking, endosomal and vacuolar transport, and autophagy, and their accurate domain annotation depends on the quality of profile models and the sequence diversity behind them. The original SNARE domain classification predates the recent expansion of eukaryotic sequence data, leaving its HMM profiles and subgroup coverage unable to resolve divergent and lineage-specific paralogs. Results: We present an updated release of TRACEY built on a resynchronized, non-redundant collection of 18,915 curated SNARE proteins spanning 1,188 species, together with a consolidated set of 83 HMM profiles, including 43 models for newly defined subgroups, reconstructed through an iterative, mixture-model-driven procedure. In direct comparison with the legacy models, at least ~75% of sequences in every overlapping group scored better with the new HMMs, indicating systematic gains in domain detection. A redesigned web interface adds multiparameter querying, FASTA download, and direct scanning of user-submitted sequences against the curated profiles. Availability and implementation: TRACEY is freely available at https://tracey.unil.ch.

13.
arXiv (CS.CV) 2026-06-12

Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning

Multimodal agents, which integrate a controller e.g., a vision language model) with external tools, have demonstrated remarkable capabilities in tackling complex multimodal tasks. Existing approaches for training these agents, both supervised fine-tuning and reinforcement learning, depend on extensive human-annotated task-answer pairs and tool trajectories. However, for complex multimodal tasks, such annotations are prohibitively expensive or impractical to obtain. In this paper, we propose an iterative tool usage exploration method for multimodal agents without any pre-collected data, namely SPORT, via step-wise preference optimization to refine the trajectories of tool usage. Our method enables multimodal agents to autonomously discover effective tool usage strategies through self-exploration and optimization, eliminating the bottleneck of human annotation. SPORT has four iterative components: task synthesis, step sampling, step verification, and preference tuning. We first synthesize multimodal tasks using language models. Then, we introduce a novel trajectory exploration scheme, where step sampling and step verification are executed alternately to solve synthesized tasks. In step sampling, the agent tries different tools and obtains corresponding results. In step verification, we employ a verifier to provide AI feedback to construct step-wise preference data. The data is subsequently used to update the controller for tool usage through preference tuning, producing a SPORT agent. By interacting with real environments, the SPORT agent gradually evolves into a more refined and capable system. Evaluation in the GTA and GAIA benchmarks shows that the SPORT agent achieves 6.41% and 3.64% improvements, underscoring the generalization and effectiveness introduced by our method. The project page is https://SPORT-Agents.github.io.

14.
bioRxiv (Bioinfo) 2026-06-23

Multi-Scale Machine Learning for Antibody-Antigen Binding Affinity Prediction Using Deep Mutational Scanning and Structural Features

Predicting how mutations alter antibody-antigen binding affinity is essential for antibody engineering and vaccine design, yet current methods generalize poorly to unseen complexes. We present a multi-scale machine learning framework integrating 93 descriptors across four modalities: physicochemical, structural, ESM-2 protein language model, and solvent-accessible surface area (SASA)/{Delta}{Delta}G_fold features. Under leave-one-complex-out deep mutational scanning (LOCO-DMS) cross-validation on AbAgym (36,541 mutations, 68 experiments, 13 pathogens), gradient boosting achieved MCC = 0.206; a confidence-stratified ensemble reached MCC = 0.374 (83.5% accuracy, 25.5% coverage). No single modality exceeds the majority baseline alone; only multi-scale fusion succeeds. Boltzmann ceiling analysis shows 45.9% of mutations are near-neutral (|{Delta}{Delta}G| < k_BT), bounding theoretical maximum MCC at 0.473; our method achieves 79.1% of this limit. Five deep learning architectures benchmarked under LOCO-DMS showed self-attention matching gradient boosting (MCC = 0.200). Cross-pathogen transfer failed systematically (mean 46.7%), confirming universal binding predictors remain an open challenge.

15.
arXiv (quant-ph) 2026-06-16

High-fidelity two-qubit gates in a 7-qubit register for quantum networks

arXiv:2606.14847v1 Announce Type: new Abstract: Quantum networks based on optically active solid-state spins may enable quantum technologies including long-range quantum communication and distributed quantum computing. Network nodes containing multiple high-fidelity qubits can facilitate large-scale fault-tolerant operation. However, the stringent error thresholds remain out of reach for multi-qubit registers. In this work, we demonstrate high-fidelity two-qubit gates in a 7-qubit register, based on nuclear spins coupled to a nitrogen-vacancy (NV) center in diamond. We analyze crosstalk in highly connected spin systems, develop an efficient optimization procedure, and characterize the gates using gate set tomography. The two-qubit gate fidelities (best: 99.61(5)%, average: 99.18(2)%) demonstrate a multi-qubit register at the threshold for distributed quantum computation. Finally, as an example application, we perform a variational quantum eigensolver (VQE) simulation of the ground-state energy of H2 and LiH molecules. These results demonstrate one of the key prerequisites for scalable quantum networks based on solid-state spins.

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

Reliable Neural-Codec Text-to-Speech by ASR Self-Verification and Distillation: Near-Zero Catastrophic Failures Across Models and Codecs

arXiv:2606.18323v1 Announce Type: cross Abstract: Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content. We show this failure mode is cheap to remove. Under a single format-robust metric (a catastrophic-failure rate via an ASR round-trip), best-of-N ASR self-verification drives failures to near-zero: no observed failures remain by N=2 on a standard corpus (LibriSpeech) and by N=4 on a hard prompt set. This is not an artifact of one model: the reduction replicates across four open codec-TTS systems and three neural codecs (XCodec2, SNAC, Mimi), reaching the near-zero floor by N=2 on three of the four. We then make the fix free at inference time by distilling the self-verified behaviour into the model, which recovers much of the robustness in single-shot decoding, closing ~52-58% of the failure mass on hard inputs at no test-time cost. The distillation gain concentrates where it is needed (hard inputs); on already-reliable prose there is no headroom and no detectable change. A controlled comparison adds a clean negative: offline direct preference optimization (DPO/IPO) does not beat plain supervised distillation, and an online iterative variant is promising but not statistically separable at our evaluation size. We report honestly the one model that resists (a larger Llasa where scale did not obviously help) and a rare-word capability ceiling that no self-distillation method overcomes

17.
arXiv (CS.LG) 2026-06-25

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions. In many practical contexts, these programs require obtaining solutions rapidly as new information becomes available. These problems can be represented through scenario approximations to be solved by off-the-shelf optimization solvers, which achieve high decision quality offline but typically run in seconds to minutes per instance, falling short of the sub-second responses that peak periods of planning require. This paper develops a learning-based optimization proxy: a scenario-embedded neural network trained offline on solver-generated labels, paired online with a decoder that enforces feasibility, replacing the per-epoch solve with a single forward pass. The framework is specialized to omnichannel order fulfillment, where each arriving order requires a sub-second assignment of products to distribution centers and carrier services under stochastic delivery times and future demand. A two-stage contextual stochastic program is introduced to formulate this problem, and its contextual sample average approximation (C-SAA) supplies the offline labels, while a composite training loss combines label imitation, a constraint-violation penalty, and self-supervised cost alignment. In a calibrated simulator built from JD.com transactional records, a detailed computational study is provided. The proxy reduces decision latency by roughly 2800x relative to the online finite-sample C-SAA reference and improves over it by 3.3% in realized fulfillment cost. Relative to established fulfillment policies, the proxy lowers total realized cost by at least 10.7% and roughly halves the late-delivery rate.

18.
medRxiv (Medicine) 2026-06-16

Prevalence and Correlates of Ideal Cardiovascular Health among Ugandan Adolescents: A Cross-Sectional Study

Introduction: Cardiovascular disease (CVD) risk factors often emerge during adolescence and track into adulthood, yet data on cardiovascular health (CVH) in sub-Saharan Africa remain limited. We assessed the prevalence and correlates of ideal CVH among Ugandan adolescents. Methods: We analysed baseline data of adolescents enrolled in a cluster-randomised controlled trial being conducted in urban (Kampala) and rural (Jinja) districts of Uganda. In this study, Ideal CVH was defined as meeting "ideal" status of 5-7 of the American Heart Association's Life's Simple 7 metrics. Random-effects logistic regression was used to identify factors associated with ideal CVH, accounting for village-level clustering. Results: We recruited 1316 participants with a mean age of 13.2 years, of whom 58.1% were female. Overall, the prevalence of ideal CVH was 66.8% (95% CI: 64.2% - 69.3%). The prevalence was higher in Jinja (74.4%, 95%CI: 70.9% - 77.7%) than Kampala (59.6%, 95%CI: 55.8%-63.2%) and the difference was evident (p

19.
bioRxiv (Bioinfo) 2026-06-15

VrySure: A Multi-Task AI Scientific Fraud Detection Platform for Identifying Manipulated and AI-Generated Biomedical Research Images

Integrity of scientific data is critical in biomedical research, where images often serve as primary evidence for experimental observations and conclusions. Advances in image-editing technologies and generative artificial intelligence (AI) have increased the accessibility and realism of visual manipulation, making detection through manual review increasingly challenging. To empower our laboratory researchers to continuously monitor and uphold scientific rigor and data integrity, and serve the global scientific community, we developed VrySure, an easy-to-deploy, AI-driven multi-task platform for automated image-integrity screening in biomedical research. VrySure integrates four detection modules: cross-image transformation detection, within-image copy-move detection, splicing detection in blot and gel images, and AI-generated image detection. The system identifies potentially manipulated images and, when possible, localizes suspicious regions using bounding-box outputs to support downstream verification. To support development and evaluation, we constructed task-specific datasets by combining public biomedical image resources, curated manipulated examples, and synthetic images generated by multiple generative AI systems. We evaluated VrySure using region-level F1 score, recall, precision, false negative rate (FNR), and false discovery rate (FDR) across multiple manipulation categories and compared its performance with two commonly used commercial image-integrity screening platforms under a predefined benchmark protocol. Under the tested conditions, VrySure achieved a higher F1 score and recall, lower FNR, and maintained a low FDR for within-image copy-move detection, splicing detection, and AI-generated image detection, while showing comparable performance in transformation detection. Beyond automated screening, VrySure is designed to support source-data comparison and evidence-based assessment in scientific integrity investigations. By integrating multiple detection capabilities into a unified and scalable workflow, VrySure provides a practical framework to improve the efficiency and consistency of image-integrity screening in biomedical research.

20.
medRxiv (Medicine) 2026-06-17

The interaction between chronic hepatitis B (CHB) and Metabolic dysfunction-associated steatotic liver disease (MASLD) in a diverse central London population

Introduction: The overlap between chronic hepatitis B (CHB) and metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging global health challenge. We investigated the impact of MASLD and metabolic comorbidity in a diverse London viral hepatitis clinic. Methods: This retrospective cross-sectional study (May 2018-Feb 2024) included adults with CHB having controlled attenuation parameter (CAP) measurements. MASLD was defined as CAP >264 dB/m plus [&ge;]1 cardiometabolic factor (CMF). We used univariable and multivariable models to examine MASLD's relationship with liver stiffness and hepatitis B viral load (HBV VL). Results: Among 323 individuals (67% male, median age 36), most were from Black (35%) or non-white British/Irish (29%) backgrounds. Overall, 64% had [&ge;]1 CMF, and 20% had MASLD. The CHB/MASLD group was significantly older (median 43 vs 35 years, p

21.
arXiv (CS.AI) 2026-06-18

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning

arXiv:2601.21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joint framework reshapes the loss landscape by reducing the largest Hessian eigenvalue and reducing its max eigenvalue, thereby significantly enhancing robustness to quantization noise. HeRo-Q requires no architectural modifications, incurs negligible computational overhead, and integrates seamlessly into existing PTQ pipelines. Experiments on Llama and Qwen models show that HeRo Q consistently outperforms state of the art methods including GPTQ, AWQ, and SpinQuant not only achieving superior performance under standard W4A8 settings, but also excelling in the highly challenging W3A16 ultra low bit regime, where it boosts GSM8K accuracy on Llama3 8B to 70.15\% and effectively avoids the logical collapse commonly seen in aggressive quantization.

22.
arXiv (CS.CV) 2026-06-11

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

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.

23.
arXiv (CS.LG) 2026-06-25

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

arXiv:2601.23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy, and policy updates must be conservative. In this paper, we revisit actor policy representation as a first-class design choice for on-policy RL. We study discretized categorical actors, which represent each action dimension as a distribution over discrete bins and induce a policy objective analogous to classification cross-entropy loss. Building on architectural advances from supervised learning, we further pair discretized categorical actors with regularized networks, yielding RN-D. Across diverse continuous-control benchmarks, we show that simply replacing the standard Gaussian actor with our proposed actor substantially improves performance, achieving state-of-the-art results within on-policy RL. We release our code at https://github.com/alwaysbyx/RND-RL.

24.
arXiv (CS.AI) 2026-06-15

No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements

arXiv:2606.14357v1 Announce Type: cross Abstract: Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories. Such repositories contain valuable signals: tests, incidents, migrations, edge cases, product judgment, and operational history. These signals are entangled with framework churn, naming drift, generated-source ambiguity, dependency rituals, CI dialects, weak proof routes, and human-oriented review customs. We propose agent-first canonical code, a proof-carrying substrate that rewrites routine product software into canonical behavior profiles, typed change algebra, proof lanes, constrained edit grammars, semantic patch cells, runtime negative memory, and proof-carrying change objects. The core hypothesis is that quotienting software by behavior equivalence under a declared oracle can collapse equivalent encodings into governed representatives with explicit evidence and proof obligations. The endpoint is amortized cost per verified correct change, including source, context, reasoning, tools, verification, security, provenance, review, failed loops, defects, and foundry cost under a common oracle. Reported reduction bands are hypotheses, not measured frontier results. The proposed limit is a No-Accident Horizon: removable accident decreases until residual novelty, evidence, governance, risk, and future optionality dominate. For supported routine-product distributions, this gives a defensible planning target near 100-fold all-in cost reduction, not a guarantee for all software. Preliminary QLoRA experiments on Qwen2.5-Coder-14B show that 64,088 canonical trajectories are learnable and suppress tested forbidden-language markers, but do not establish behavior preservation, scaling economics, or verified-change cost. The contribution is a falsifiable program centered on minimum functional description length and verified-change cost.

25.
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.