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

CPS4: Class Prompt driven Semi-Supervised Spine Segmentation with Class-specific Consistency Constraint

Vision Language Model (VLM) has great potential to enhance the quality of pseudo labels in semi-supervised spine segmentation by leveraging textual class prompts to generate segmentation map, but no one has studied it yet. Although promising, it lacks explicit constraints to ensure consistency between spine class prompts and spine unit region, resulting in unsatisfactory performance in multi-class segmentation map generation. In this paper, we propose CPS4, the first text-guided semi-supervised spine segmentation network using class prompts to enhance the quality of spine pseudo labels. Specifically, CPS4 is implemented through two training stages. (i) Class-specific consistency constrained VLM pretraining stage: we propose token- and pixel-level attention loss to optimize the consistency between class prompts and spine units, forcing the textual class prompt to be closely coupled with the target spine unit in the semantic space. (ii) Class Prompt driven semi-supervised spine segmentation stage: using the pretrained vision-text encoder, we derive each class-specific binary segmentation map for the unlabeled spine image and integrate them into an unified multi-class segmentation map, improving the quality of the spine pseudo label generated by the semi-supervised spine segmentation network. Experimental results show that our CPS4 achieves superior spine segmentation performance with Dice of 80.44%, only using 5% labeled data on the public spine segmentation dataset, surpassing popular semi-supervised learning and VLM methods. Our code will be available.

03.
arXiv (CS.LG) 2026-06-16

Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation

arXiv:2606.14763v1 Announce Type: cross Abstract: Real-time autonomous navigation in dynamic, unknown environments remains a fundamental challenge for mobile robotics. We propose a map-free framework that tightly integrates reactive rolling-horizon planning with nonlinear Model Predictive Control (MPC). At each control cycle, a LiDAR-based Gaussian occupancy representation is constructed and used to generate collision-free trajectories via A* search, which are then tracked by a CasADi/IPOPT MPC formulation incorporating a smooth sigmoid obstacle barrier. To improve robustness to parameter sensitivity, we adopt an offline Bayesian optimization scheme based on Tree-structured Parzen Estimators (TPE), which identifies near-optimal controller parameters with respect to a composite navigation objective. In addition, a Gaussian Process surrogate is used to analyze parameter sensitivity and provide insight into the optimization landscape. The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo, followed by deployment on the physical robot. Experimental results show that parameters tuned in simulation transfer effectively to hardware, maintaining comparable performance without additional tuning. The full system achieves up to a 90.0\% navigation success rate when deployed, along with a 38.9\% average improvement in the evaluation metrics across simulated environments.

04.
arXiv (CS.LG) 2026-06-15

Learning Variable-Length Tokenization for Generative Recommendation

arXiv:2605.17779v2 Announce Type: replace Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs). A fundamental yet unexplored design choice is that existing methods employ fixed-length tokenization for all items, implicitly assuming uniform encoding capacity regardless of item characteristics. Through systematic experiments across four datasets, we discover the Popularity-Length Paradox: popular items achieve optimal performance with short IDs, while tail items require substantially longer codes to capture discriminative semantics. This reveals a critical mismatch where popular items benefit from abundant collaborative signals and require minimal semantic detail, whereas tail items must rely on fine-grained content features due to sparse interaction data. To address this, we propose VarLenRec, a framework for learning variable-length tokenization. We develop Popularity-Weighted Information Budget Allocation (PIBA), an information-theoretic framework proving that optimal ID length should scale as a negative power of popularity. Directly implementing variable-length allocation faces two technical challenges: standard Euclidean residual quantization lacks geometric capacity to support diverse code lengths without distortion, and discrete length decisions are non-differentiable. We address these through Hyperbolic Residual Quantization, which leverages the exponential volume growth of the Poincaré ball to naturally stratify encoding capacity, and a Soft Length Controller, which enables differentiable length prediction via continuous layer retention probabilities regularized by PIBA-derived priors. Extensive experiments demonstrate that VarLenRec achieves significant improvements over state-of-the-art methods in recommendation accuracy and training/inference efficiency, revealing the importance of adaptive encoding capacity in generative recommendation.

05.
arXiv (CS.LG) 2026-06-15

Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction

arXiv:2606.14159v1 Announce Type: new Abstract: Protein-ligand binding affinity (PLA) prediction is critical in drug discovery. Despite the notable advancements in machine learning-based approaches, existing methods struggle to jointly characterize local geometric organization and globally coordinated cross-molecular interactions, limiting their ability to model complex binding mechanisms. Here, we propose RicciBind, a geometric representation framework that integrates curvature-guided hierarchical structure learning with optimal transport (OT)-based cross-domain alignment to model molecular interactions. Specifically, RicciBind leverages Ricci curvature to capture local interaction tightness within molecular structures, enhancing structural awareness and organizing atomic interactions into curvature-aware hierarchical representations. An OT-based cluster matching mechanism then aligns protein and ligand clusters across heterogeneous domains under geometric constraints, enabling globally consistent correspondences and revealing higher-order interaction patterns beyond local neighborhoods. By coupling curvature-guided structure encoding with OT-driven cross-domain alignment, RicciBind effectively models complex interaction semantics and substantially improves both the accuracy and interpretability of binding affinity prediction. Extensive experiments demonstrate that RicciBind achieved superior predictive performance and generalization across PLA benchmarks and virtual screening tasks. Ablation studies further confirmed the essential role of Ricci curvature in enhancing molecular interaction representations.

06.
medRxiv (Medicine) 2026-06-16

Validating an Early Pregnancy HbA1c as the Screening Test for Gestational Diabetes Mellitus: Findings from PRISMA Pakistan Cohort

Background: Early identification of gestational diabetes mellitus (GDM) is critical to improving maternal and neonatal outcomes, particularly in resource-constrained settings where universal oral glucose tolerance testing (OGTT) is burdensome. We assessed whether early-pregnancy HbA1c alone or combined with common risk factors can predict GDM and reduce the burden of OGTT requirements in a peri-urban cohort in Karachi, Pakistan. Methods: We conducted a secondary analysis of the Pregnancy Risk Infant Surveillance and Measurement Alliance (PRISMA) Pakistan cohort. Women enrolled before 20 weeks' gestation with available early-pregnancy HbA1c and a 2-hour 75g OGTT at 24 to 28 weeks were included. We externally validated GDM prediction models originally developed in the STRiDE-India cohort. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). We assessed four models: HbA1c alone (Model 1a); age, BMI, and family history of diabetes mellitus (FH DM) (Model 1b); HbA1c combined with age, BMI, and FH DM (Model 2); and an extended model, i.e., Model 2 combined with socioeconomic status, gestational age, parity, systolic and diastolic blood pressure (Model 3). A dual-threshold approach was applied to assess rule-in and rule-out performance. Results: Among 2,489 women, GDM incidence was 7.5% (n=186). Models with a broader set of predictors demonstrated higher AUC values, with Model 2 achieving an AUC of 0.61 (95% CI: 0.57, 0.66). Including additional factors (Model 3) did not further improve predictive ability (AUC: 0.62; 95% CI: 0.58, 0.66). In addition, at predefined thresholds, Model 2 achieved sensitivity of 73.7% (rule-out) and specificity of 83.5% (rule-in), with the potential to reduce OGTT requirements (58.5%). Conclusions: Early-pregnancy risk stratification using HbA1c combined with simple clinical predictors offers a pragmatic approach to streamline GDM screening among high-risk pregnant women. A dual-threshold strategy using Model 2 could reduce reliance on universal OGTT while prioritizing high-risk women for confirmatory testing.

07.
bioRxiv (Bioinfo) 2026-06-16

Evidence for recombination in dengue virus genomes

Recombination is a key driver of RNA virus evolution, yet its extent and evolutionary implications in dengue virus (DENV) remain incompletely understood. We conducted a comprehensive, genome-wide recombination screen across 6,905 complete DENV genomes representing all four serotypes, 82 countries, and eight decades of sampling (1944-2023) retrieved from the Bacterial and Viral Bioinformatics Resource Center. Using seven complementary recombination detection methods implemented in RDP5, we identified 66 recombination events across 53 unique recombinant sequences, of which 29 are newly described. Events included intra-genotypic (n = 18), inter-genotypic (n = 32), and inter-serotypic (n = 16) exchanges spanning 14 genotypes and four continents, with no meaningful serotype-level enrichment (Cramer's V = 0.054). Recombination was concentrated in non-structural genes, most frequently NS3 (19 events), NS5 (17), and NS2 (12), while the capsid gene contained no recombination events, consistent with strong functional constraint. Single-nucleotide polymorphism analyses confirmed low divergence between recombinants and their inferred parents in both recombinant and non-recombinant regions. Phylogenomic analysis of 6,642 sequences revealed that recombinants cluster significantly closer to their major parents (p = 8.9 x 10-6 ) and that their removal does not significantly alter tree topology (p = 0.898), suggesting that the short length of recombinant regions limits phylogenetic conflict. We also introduce RECOSIM, an unsupervised machine-learning tool for recombination detection that achieved higher precision than RDP5 on both simulated (93.4% vs. 80.0%) and empirical (98.1% vs. 39.3%) datasets. Collectively, these results establish recombination as a widespread, pan-serotypic phenomenon in DENV with implications for genomic surveillance, vaccine evaluation, and evolutionary inference.

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

LLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Science

Qualitative coding is central to social science, but expert annotation is difficult to scale. LLMs offer a possible extension, yet require careful validation when the target construct is interpretive, theoretically loaded, and only indirectly expressed. We study this problem in a difficult case: detecting whether authors treat Bayesian models as descriptions of mental and neural mechanisms (realism) or as useful mathematical tools (instrumentalism). Our method combines a theory-driven codebook, expert-coded reference annotations, a diagnostic-gated prompt-optimization search yielding a shared zero-shot prompt for three frontier LLMs (GPT-5.1, Claude Sonnet 4.6, Gemini 3 Pro Preview), and multi-rater reliability analysis. The final prompt achieved a held-out combined reliability score of 0.76 (harmonic mean of ICC = 0.79 and $\alpha$ = 0.74), with all diagnostics satisfied. Deployed on 6,858 quotes from 210 articles, the three LLMs reached substantial quote-level agreement (ICC = 0.80; $\alpha$ = 0.76; combined = 0.78) and near-perfect article-level rank stability ($r$ = 0.96-0.97 across rater pairs). The corpus was predominantly weakly realist, but article-level stances were rarely uniform: only 1.4% of articles used a single band, while 59.5% spanned four or more. Low-level perception/motor articles scored 8.8 Realism points higher than high-level cognition articles ($p < .001$, $d = 0.60$), quantifying a long-held qualitative intuition. We present this as an expert-led case study; the framework is intended to generalize to similar theoretically demanding tasks, not to all qualitative analysis.

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

Reload-Mamba: Hierarchical Anti-Dilution State-Space Modeling for Multi-Class Semantic Segmentation

Mamba-based state space models offer linear-time long-range modeling for high-resolution dense prediction, but sequential state-space propagation can attenuate boundary-sensitive and detail-sensitive responses that are critical in multi-class semantic segmentation. We propose Reload-Mamba, a semantic segmentation framework that addresses this propagation-induced response dilution through three segmentation-specific designs: (i) a boundary-supervised local detail prior that is explicitly trained with ground-truth boundary masks to identify regions requiring response restoration; (ii) a class-uncertainty-aware Reload Gate that incorporates per-pixel class entropy from a pre-reload auxiliary head as an additional gating signal, a formulation that is informative only under multi-class dense prediction; and (iii) a hierarchical multi-level Reload mechanism that applies anti-dilution refinement at three decoder levels and fuses the restored representations top-down. Built upon a ConvNeXt-Tiny encoder with a multi-scale decoder and four-directional Mamba scanning with pixel-wise directional attention, Reload-Mamba achieves 47.9% single-scale (48.9% multi-scale) mIoU on ADE20K and 83.2% single-scale mIoU on Cityscapes. With ResNet-101 + COCO pre-training under the standard DeepLab-style protocol, Reload-Mamba reaches 87.8% mIoU on PASCAL VOC 2012 val. Controlled ablations show that each of the three segmentation-specific designs contributes beyond a direct port of the prior anti-dilution architecture proposed for binarization, cumulatively improving over the direct-port baseline by +2.2 mIoU on ADE20K.

10.
arXiv (CS.CL) 2026-06-18

Trust Region On-Policy Distillation

On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD training becomes unstable when the teacher and student distributions differ substantially, as teacher supervision on student-generated tokens may yield unreliable policy gradients and even cause optimization failure. This work addresses reliable on-policy token-level supervision through credit assignment strategies, and proposes Trust Region On-Policy Distillation, TrOPD. It features the following characteristics: 1) Trust-Region On-Policy Learning: TrOPD performs OPD only in regions where the teacher provides reliable supervision, mitigating the optimization difficulty of the K1 reverse-KL estimator under distribution mismatch. 2) Outlier Estimation: For outlier regions, we explore gradient clipping, masking, and forward-KL estimation to reduce the adverse effects of unreliable supervision. 3) Off-Policy Guidance: The student continues generation from teacher prefixes and uses forward KL to imitate off-policy guidance, encouraging on-policy exploration toward reliable regions. Experiments show that TrOPD consistently outperforms SoTA OPD baselines, including OPD, EOPD, and REOPOLD, across mathematical reasoning, code generation, and general-domain benchmarks.

11.
medRxiv (Medicine) 2026-06-17

Treatment of Multi-Drug-Resistant Tuberculosis with Second-Line All-Oral Drugs in Ghana: Incidence of Adverse Events.

Introduction: The treatment of multidrug-resistant tuberculosis (MDR-TB) remains challenging due to the toxicity of second-line medications and suboptimal treatment outcomes. This study aimed to determine the incidence of adverse events and identify factors associated with these events in patients undergoing treatment for MDR-TB with second-line all-oral drugs in Ghana. Methods: This retrospective cohort study reviewed the medical records of 384 MDR-TB patients treated with second-line all-oral drugs at selected health facilities in Ghana, including the Greater Accra Regional Hospital, Eastern Regional Hospital, and Kumasi South Hospital. Data were extracted using the Kobo Collect tool, capturing patient demographics, baseline clinical and laboratory characteristics, treatment regimens, and adverse events. The study period spanned from 2020 to August 2024. Results: The study included a total of 384 MDR-TB patients, with a mean age of 45 years (SD = 15). The majority of patients were male (65.78%), and most were within the 45-64 years age group (33.85%), followed by those aged 25-44 years (31.25%). Regionally, the highest number of cases were reported from the Greater Accra Region (39.06%), followed by the Eastern Region (31.25%) and Kumasi South Hospital (29.69%). Approximately one in four patients (25%) presented with comorbidities, with HIV being the most common (19.5%). The most frequently reported adverse events were diarrhea (14%), dizziness (13.7%), and vomiting (12.3%). Most of these were mild to moderate in severity and tended to decrease as treatment progressed. Severe adverse events, such as leukopenia and acute kidney injury, were rare, occurring in less than 5% of patients. Over the course of treatment, gastrointestinal adverse events such as vomiting and nausea showed a significant decline, indicating possible patient adaptation or improved clinical management. Results from the multivariate Poisson regression analysis revealed that age and comorbidities were significant predictors of adverse events. Patients aged 65 years and above had a 56% lower risk of developing adverse events compared to younger patients (Adjusted Risk Ratio [aRR] = 0.44, 95% CI: 0.25-0.79, p = 0.005). Conversely, patients with comorbid conditions such as diabetes or hypertension were approximately 2.6 times more likely to experience adverse events compared to those without comorbidities (aRR = 2.65, 95% CI: 1.58-4.43, p < 0.001). The effect of sex was not statistically significant after adjustment (aRR = 1.03, 95% CI: 0.70-1.50, p = 0.86). At the end of the treatment period, 74.9% of patients achieved successful outcomes, including both those who were cured and those who completed treatment without being classified as cured. However, 25.1% had unsuccessful outcomes, which included treatment failure, relapse, or death. Conclusion: In conclusion, adverse events are common in the treatment of MDR-TB with second-line All-Oral drugs, with gastrointestinal adverse events being the most prevalent. These findings highlight the importance of monitoring and managing adverse events to optimize treatment outcomes for MDR-TB patients in Ghana.

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

Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality

This paper addresses the challenge of assessing image quality in ID cards in remote verification systems by applying capture-related quality measures from the Open Face Image Quality (OFIQ) standard to ID card images. Our preprocessing pipeline includes corner detection, perspective normalization, and comprehensive foreground masking to ensure accurate and unbiased quality measure computation. We evaluate the effectiveness of these measures by analyzing their correlation with the performance of three presentation attack detection (PAD) algorithms across four diverse ID card datasets, where two datasets contain bona fide, i.e. pristine, images and two contain printed mock ID cards. Our results suggest that quality assessment based on some OFIQ measures can significantly improve PAD performance.

13.
PLOS Medicine 2026-05-21

Semaglutide-associated risk of nonarteritic anterior ischemic optic neuropathy in patients with type 2 diabetes: A systematic review and meta-analysis of observational studies

by Jędrzej Chrzanowski, Magdalena Walicka, Jacek Burzyński, Małgorzata Zaraś, Arkadiusz Michalak, Wojciech Fendler Background Semaglutide, a glucagon-like peptide-1 receptor agonist, is widely used for the management of type 2 diabetes (T2DM). Recent case reports have raised concerns about a potential association between semaglutide use and the development of nonarteritic anterior ischemic optic neuropathy (NAION), a rare but vision-threatening condition. We aimed to evaluate whether semaglutide use is associated with an increased risk of NAION in patients with T2DM. Methods and findings We conducted a systematic review and meta-analysis of observational studies comparing patients with T2DM aged ≥12 years treated with semaglutide to those receiving other glucose-lowering therapies. We searched PubMed, Scopus, and Web of Science databases from January 2023 to November 2025. Two reviewers independently extracted data on study design, population characteristics, and outcomes. Risk of bias was assessed using the Newcastle–Ottawa Scale, and ROBINS-I v.2. Certainty of the evidence was graded according to the GRADE framework. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using fixed-effects models; sensitivity analyses included crude and subgroup HRs, and overlapping study replacement. Leave-one-out analysis was conducted to assess small-study effects and publication bias. Results were contextualized within other meta-analyses, systematic reviews, consensus statements, and regulatory communications on the topic.Five eligible observational studies met the inclusion criteria, and 7 additional studies were included in the sensitivity analysis. Semaglutide use was associated with a significantly increased hazard of NAION compared with nonsemaglutide glucose-lowering regimens (HR 2.17, 95% CI [1.73, 2.74]; p 

14.
bioRxiv (Bioinfo) 2026-06-21

Antibody-Antigen Affinity Prediction with Chain-Aware Protein Language Modeling

Motivation: Antibody-antigen affinity determines which antibodies advance in therapeutic discovery, repertoire analysis and affinity maturation, but experimental measurements are sparse relative to the scale of sequence libraries. Structure-based predictors can exploit interface geometry when reliable complexes are available, yet early discovery often requires ranking many heavy-light chain pairs against antigens for which no complex structure exists. Existing sequence-based models are scalable, but frequently compress heavy and light chains into a single antibody representation or concatenate antibody and antigen features obscuring the chain-specific and epitope-specific signals that drive binding. Results: We present AbAffinity, a sequence-only chain-aware three-stream architecture that maintains heavy chain, light chain and antigen as distinct streams. It integrates frozen ESM-2 embeddings with heavy-chain CDR-focused pooling, heavy-light self-attention, adaptive fusion gating and gated cross-attention, training only a compact interaction module. On the SAAINT-DB benchmark, AbAffinity achieves strong predictive performance under ten-fold cross-validation and maintains robust accuracy on novel antigens. It consistently outperforms recent sequence-based models across external benchmarks including SAbDab, AB-Bind and SKEMPI 2.0. Ablation studies highlight the contributions of chain-specific representations, CDR-focused pooling and the gated interaction pathway. Integrated Gradients attributions recover known paratope and epitope residues at structurally validated interfaces. AbAffinity provides a lightweight, explainable sequence-first framework for antibody triage and prioritisation when structural information is limited or unavailable.

15.
arXiv (CS.AI) 2026-06-11

When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines

arXiv:2606.11265v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection. Existing studies mainly evaluate poisoning under simplified retrieval settings, overlooking practical RAG pipelines involving document chunking, dense retrieval, reranking, and grounded generation. In this paper, we revisit corpus poisoning under realistic multi-stage retrieval pipelines and show that many existing attacks substantially degrade after reranking despite achieving high retrieval-stage relevance. We identify retrieval granularity mismatch as a key reason for this failure: document-level adversarial signals are often fragmented during chunking, while rerankers favor locally coherent and answer-bearing passages rather than globally optimized semantic similarity. Based on this observation, we propose Chunk-aware and Rerank-Consistent Poisoning (CRCP), a poisoning framework that jointly optimizes retrieval relevance, reranker consistency, and chunk-boundary robustness. CRCP explicitly models chunking transformations during optimization to generate locally self-contained adversarial passages that remain effective under varying chunking configurations. Experiments on standard RAG benchmarks with multiple retrievers and rerankers show that existing poisoning methods are highly sensitive to chunk size and reranking strategies, whereas CRCP achieves substantially higher attack success rates and stronger robustness across realistic retrieval pipelines. Our findings highlight an important realism gap in current RAG security evaluation and suggest that poisoning in modern RAG systems should be studied as a multi-stage retrieval consistency problem rather than a retrieval-only problem.

16.
arXiv (CS.LG) 2026-06-11

On the Stability of Growth in Structural Plasticity

arXiv:2605.15435v2 Announce Type: replace Abstract: Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization. In contrast, a model can also be adapted by editing its structure during training, for example by pruning existing hidden-neuron units or growing new ones. Although growth is appealing for adaptive and continual systems, we show that it is not simply the inverse of pruning. Pruning selects among units that have participated in training from the start, whereas growth inserts new units into an already specialized optimization trajectory. We isolate this insertion problem and show that newborn units are often forward-active but backward-starved: they participate in the forward computation, yet receive much weaker gradient signal than incumbent units. This disadvantage is minor in small MLP benchmarks, but becomes clear in harder image-classification settings with a convolutional trunk. In these settings, \textsc{Grow} can achieve high final accuracy during the structural-editing procedure, while \textsc{Prune} is stronger when performance is averaged over the training trajectory or when the final sparse network is retrained from scratch. Interventions targeting optimizer state, insertion, selection, and trainability show that improving the integration of newborn units can improve adaptive performance, but does not automatically produce better final subnetworks. In continual-learning benchmarks stressing plasticity loss, \textsc{Grow} becomes competitive mainly when new units have enough time to integrate. Together, these results suggest that \textsc{Grow} should be evaluated not only as an architecture-search operator, but as a time-sensitive optimization process whose success depends on insertion stability.

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

Geometric Metrics and LLMs: What They Measure and When They Work

We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free quality signals, but the conditions under which they are reliable remain unclear. We evaluate eight commonly-used metrics: intrinsic-dimensionality estimators, spectral norms, and related quantities across six tester models (0.5-8B) and eight generators on contrasting tasks, separating genuine geometric signal from text-length effects and from what standard text statistics already capture. Three findings emerge. First, some metrics (notably Schatten Norm and MOM) mainly reflect output length, and their apparent discriminative power collapses once length is controlled. Second, geometric metrics add modest but real information beyond text statistics: combined with them, a classifier reaches 78% accuracy on 6-way generator identification versus 69% for text statistics alone. Third, rather than tracking a general notion of text quality, the metrics demonstrate only moderate association between the intrinsic-dimensionality and lexical diversity (RTTR). We give use-case-specific recommendations and identify failure detection as the most promising near-term application.

18.
arXiv (quant-ph) 2026-06-16

Study of the triangular-lattice Hubbard model with constrained-path quantum Monte Carlo

arXiv:2603.14808v2 Announce Type: replace-cross Abstract: We benchmark constrained-path Monte Carlo (CPMC) on the triangular-lattice Hubbard model for several fillings and $U$ values and show that symmetry-adapted trial wave functions substantially improve quantitative accuracy. Away from half-filling, simple free-electron-based trials that preserve the ground state symmetry yield energy deviations $\lesssim 1\%$ from exact diagonalization and density matrix renormalization group results. At half-filling, strong frustration in the intermediate to large $U$ regimes necessitates symmetry-projected trials to reach comparable accuracy, where both free-electron and symmetry-broken Hartree-Fock trials incur substantial constraint bias. Since the computational cost of CPMC with symmetry projection scales polynomially with system size, our results motivate its use as a practical route for studying competing ground states in strongly correlated, frustrated systems.

19.
arXiv (quant-ph) 2026-06-11

Exploring Variational Entanglement Hamiltonians

arXiv:2505.10530v3 Announce Type: replace Abstract: Recent advances in analog and digital quantum-simulation platforms have enabled exploration of the spectrum of entanglement Hamiltonians via variational algorithms. In this work we analyze the convergence properties of the variationally obtained solutions and compare them to numerically exact calculations in quantum critical systems. We demonstrate that interpreting the cost functional as an integral permits the deployment of iterative quadrature schemes, thereby reducing the required number of measurements by more than an order of magnitude even in the presence of noise. We further show that a modified ansatz captures deviations from the Bisognano-Wichmann form in lattice models, improves convergence, improves trainability and provides a cost-function-level diagnostic for quantum phase transitions. Finally, we establish that a low cost value does not by itself guarantee convergence in trace distance. Nevertheless, it faithfully reproduces degeneracies and spectral gaps, which are essential for applications to topological phases.

20.
arXiv (CS.AI) 2026-06-15

Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)

arXiv:2606.14309v1 Announce Type: cross Abstract: Property graphs may be constrained by schemas that inform both query engines and human users about the shape of valid data, enforcing a contract between data provider and consumer. Composable property-graph queries transform input graphs into output graphs. Then, the question arises of which schema can be expected after one (or several) transformation steps. We investigate how schema constraints can be inferred given an input schema and a transforming query. Specifically, we propose a reasoning procedure that, given an input schema in ProGS and a query in G-CORE infers an output schema. Since graph updates will happen frequently, our inference procedure does not rely on graph instances, such that the computed output schema applies to all graphs originating from any input graph complying with the input schema. Related work has addressed this problem for SPARQL CONSTRUCT queries, encoding it in Description Logics (DLs) so that the output schema is entailed by axioms inferred from input schema and queries. Property graphs and their queries, however, complicate the matter, as property graphs feature label and property annotations as well as first-class edges. Thus, reification has to be used in one way or another, though available DLs lack the means to encode such features directly. We approach this novel challenge via a family of mappings for i) property graphs reified in RDF, aligned with ii) a mapping from ProGS to SHACL and iii) a mapping from G-CORE to SPARQL CONSTRUCT queries. In this manner, schema inference for property graphs becomes manageable, as we break apart the problem through the extra mapping layer and utilize efficient DL reasoners. We develop the metatheory regarding the soundness of inferred schema constraints and the semantic equivalence of mapped schemas and queries.

21.
arXiv (CS.CL) 2026-06-12

Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants

Conversational shopping assistants now serve hundreds of millions of customers, yet no existing benchmark jointly evaluates the open-ended multi-turn reasoning, domain expertise, and criterion-level quality that real shopping conversations demand. Shopping reasoning is unique among language model applications. Unlike factual question answering or verifiable code generation, it requires balancing subjective preferences, budget constraints, and cross-product trade-offs across multi-turn dialogue, capabilities absent from previous e-commerce and general-purpose benchmarks. We introduce the Shopping Reasoning Bench, an expert-authored benchmark of 525 missions (232 single-turn, 293 multi-turn) with 10863 importance-weighted binary rubrics authored by retail domain experts. These criteria are organized under a taxonomy of five reasoning categories and fifteen subcategories covering diverse demands such as preference refinement, trade-off analysis, and compatibility assessment. An evaluation of nine models across three families (GPT, Claude, Gemini) shows that pass rates reach only 57–77% overall. On multi-turn missions, all models score 13–29 points lower on optional above-and-beyond criteria than on required ones, and performance degrades 4–18 points as conversations progress. These gaps show that current models handle basic shopping assistance but fall short of expert-level advice, making Shopping Reasoning Bench a challenging testbed for future shopping assistant development.

22.
Nature (Science) 2026-06-17

These ‘master’ proteins protect us from deadly mutations — and could inspire new drugs

Authors:

Biology has clever ways to mask the effects of potentially harmful gene mutations. Scientists are investigating how this ‘buffering’ works — and how to exploit it. Biology has clever ways to mask the effects of potentially harmful gene mutations. Scientists are investigating how this ‘buffering’ works — and how to exploit it.

23.
arXiv (CS.CL) 2026-06-19

IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources

Persian pretrained language models (PLMs) are still limited by the scarcity of large-scale, high-quality pretraining corpora and by insufficient evaluation beyond standard classification and NER tasks. We present IHUBERT, a monolingual Persian PLM trained from scratch with the RoBERTa-base encoder (125M parameters) on a 45 GB curated subset of the Sepahr-Danesh collection (about 7-8B tokens). To improve corpus quality and reduce redundancy, we employ a multi-stage preprocessing pipeline that includes normalization, exact and near-duplicate removal, anonymization, and vector-database-based semantic deduplication for distribution balancing control across domains and registers. We additionally train a 139k-vocabulary BPE tokenizer on the full pretraining corpus to better capture Persian morphology and orthographic variation. IHUBERT is evaluated on seven Persian NLU benchmarks covering NER, sentiment analysis, topic classification, NLI, extractive question answering, and relation extraction, using task-standard metrics (entity-level F1, Macro-F1, EM/F1). IHUBERT achieves its strongest gains on extractive QA, ranking first on both PQuAD (F1 88.3542) and ParsiNLU-RC (F1 49.0987), and attains the best result on FarsTail (Macro-F1 0.8350). On NER and topic classification, it remains competitive (e.g., 0.8308 F1 on ParsTwiNER; 0.7953 Macro-F1 on DigiMag), while relation extraction remains the main remaining gap (0.6684 Macro-F1 on PERLEX). A controlled tokenizer ablation on the IHUBERT pretraining corpus shows that BPE yields slightly lower subword fragmentation than WordPiece at matched vocabulary size, supporting our tokenization design. Overall, IHUBERT advances Persian language modeling through semantically curated large-scale pretraining and broad evaluation across both classification and comprehension-oriented tasks.

24.
PLOS Computational Biology 2026-06-05

StPedf: Cell trajectory inference of spatial transcriptomics via spatial proximity embedding and spatial density-adaptive fusion

Authors:

by Yuan Zhang, Ziyan Sun, Zhixin Shi, Mengdi Nan, Yuhan Fu, Qing Ren, Jie Gao Spatial transcriptomics is transforming our multidimensional understanding of cellular spatial organization and its functional mechanisms in processes such as development and disease by systematically resolving the spatial heterogeneity of gene expression within tissues. To delve deeper into the dynamic processes underlying spatial expression patterns, spatial trajectory inference integrates genetic and spatial information to reconstruct the spatial developmental trajectories of cells within tissues. This approach reveals the patterns of differentiation and dynamic changes as cellular states evolve continuously along spatial axes. However, existing methods often struggle to uniformly model the complex, nonlinear interactions between high-dimensional gene expression and spatial coordinates. Here, we introduce StPedf, whose core lies in employing a neural network with a masking mechanism to capture complex nonlinear interactions between high-dimensional genes and spatial positions. It further leverages spatial proximity information as a guiding cue, dynamically and adaptively adjusting the embedding of gene and spatial information and the weighting of spatial proximity information based on spatial density. This enables trajectory inference guided by spatial information. This enables optimal transport to derive intercellular transition matrices, reconstruct cellular differentiation trajectories, and construct pseudo-spatiotemporal maps. StPedf demonstrates superior performance over existing methods on five structurally distinct simulated datasets. Using StPedf, we successfully mapped distinct lineages in the spatial trajectories of telencephalon regeneration in the Ambystoma mexicanum, multiple malignant lineages expanding within primary tumors, and developmental spatial trajectories and pseudo-spatiotemporal maps in human dorsolateral prefrontal cortex (DLPFC). StPedf significantly enhances the accuracy and interpretability of spatial trajectory inference, providing critical technical support for revealing the dynamic patterns of cellular fate transitions within tissue microenvironments.

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bioRxiv (Bioinfo) 2026-06-10

Promera: a unified model for biomolecular structure prediction, filtering, and design

Generative models have become staple tools for modeling and designing biomolecular structures. However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders—an essential use case—remains insufficient; whereas design methods have focused more on unconstrained binder generation rather than capabilities enabled by controllable design. We introduce Promera, a unified generative model that combines all-atom structure prediction with improved filtering and controllable design. We find that Promera's confidence metrics are more accurate for filtering binders from non-binders for both miniproteins and nanobodies, while its co-folding performance surpasses popular open-source models (OpenFold3-p2, Boltz-2) on therapeutically relevant categories. As a design model, Promera generates binders by predicting masked protein sequences with optional epitope, paratope, and template constraints. Remarkably, our nanobody designs match the in silico success rates from backprop-based techniques (mBER) when evaluated under co-folding confidence filters. We further provide two in silico demonstrations of the the versatile capabilities of our design method: epitope targeting of the Andes hantavirus glycoprotein with VHHs and active state stabilization of the beta-2 andrenergic GPCR. We conclude by proposing a scaling law for co-folding models, suggesting a path for further performance improvement.