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decision-aware analysis by combining benchmark data with controlled synthetic scenarios.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"3:Mapping between input features and predictive models, including both classical machine learning methods and neural network-based approaches within the digital twin framework. 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Neural network-based extensions can further in-"},{"citing_arxiv_id":"2605.10722","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints","primary_cat":"cs.LG","submitted_at":"2026-05-11T15:30:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"From intuition to AI: Evolution of small molecule represen- tations in drug discovery.Briefings in Bioinformatics25, bbad422 (2024). URL https://doi.org/10.1093/bib/bbad422. [16] Dablander, M., Hanser, T., Lambiotte, R. & Morris, G. M. Exploring QSAR models for activity-cliff prediction.Journal of Cheminformatics15, 47 (2023). URL https://doi.org/10.1186/s13321-023-00708-w. [17] Jiang, D.et al.Could graph neural networks learn better molecular representa- tion for drug discovery? A comparison study of descriptor-based and graph-based models.Journal of Cheminformatics13, 12 (2021). URL https://doi.org/10. 1186/s13321-020-00479-8. [18] Xia, J.et al. Understanding the Limitations of Deep Models for Molecular prop- erty prediction: Insights and Solutions."},{"citing_arxiv_id":"2605.10616","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image","primary_cat":"cs.LG","submitted_at":"2026-05-11T14:12:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"which contextualize the representations of unstructured modalities can push the boundaries of MMTL, and we believe that MulTaBench would be instrumental for developing true Multimodal TFMs. 2 Related Work Tabular Foundation Models.The landscape of tabular learning shifted with Prior-data Fitted Networks (PFNs) [69], which pretrain transformers over synthetic tabular datasets with in-context learning (ICL) [9]. The TabPFN family [40, 41, 34, 27] pioneered this direction. Multiple subsequent works [75, 76, 62, 103, 86, 102, 6] advanced the paradigm with improvements spanning synthetic data diversity, real-world data pretraining, and architectural scalability. 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Random walks on graphs provide efficient way of extracting and encoding graph-related information. erature especially for social networks like Face- book or Twitter."},{"citing_arxiv_id":"2605.00618","ref_index":12,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Is Textual Similarity Invariant under Machine Translation? 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We report our primary results for κ= 1 , which corresponds to the invariance margin being equal to the typical within-reference-class heterogeneity - so translation is deemed invariant when its induced perturbation is no larger than that arising from natural disagreement among reference-class model"},{"citing_arxiv_id":"2605.00538","ref_index":70,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images","primary_cat":"cs.CV","submitted_at":"2026-05-01T09:34:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Vesselpose predicts voxel-wise direction vectors to extend the TEASAR algorithm for topologically accurate vascular graph reconstruction from 3D images.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00363","ref_index":271,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space","primary_cat":"math.ST","submitted_at":"2026-05-01T02:54:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27775","ref_index":49,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-30T12:12:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"minimization with early stopping (patience = 15 rounds) evaluated against the internal test partition [47]. (4) Two feed-forward Neural Network architectures were evaluated with ReLU activations and the Adam optimizer (up to 10,000 iterations) [48]. The unconstrained baseline (64-64) provides a standard non-linear mapping reference. The constrained architecture (64-8-64) introduces a narrow 8-neuron intermediate layer [49]. Because Pmax, Wtot, and hmax reflect the same loading history at different integration levels, constraining the representation to 8 latent dimensions is designed to force compression of collinear load-scaling information, retaining only features that distinguish intrinsic material strength from depth-dependent geometric artifacts. How the latent activations of this layer are analyzed by"},{"citing_arxiv_id":"2604.25304","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"RCProb: Probabilistic Rule Extraction for Efficient Simplification of Tree Ensembles","primary_cat":"cs.LG","submitted_at":"2026-04-28T07:12:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RCProb uses Dirichlet-smoothed class priors and Beta-smoothed condition likelihoods in a Naive Bayes formulation to extract rules from tree ensembles approximately 22 times faster than RuleCOSI+ while maintaining competitive accuracy and producing more compact rule sets on 33 benchmark datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25196","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model","primary_cat":"cs.LG","submitted_at":"2026-04-28T04:05:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24516","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"StarCLR: Contrastive Learning Representation for Astronomical Light Curves","primary_cat":"astro-ph.SR","submitted_at":"2026-04-27T14:18:49+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"StarCLR pretrains on TESS light curves via contrastive learning on overlapping subsequences and improves variable star classification F1 scores over scratch-trained models when fine-tuned on TESS, ZTF, and Gaia.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"corresponds to the size of the key and query vectors, typically defined asd k =H/k, whereHis the hidden dimension of the input andkis the number of attention heads. The scaling factor √dk alleviates issues of vanishing or exploding gradients, thereby improving training stability. The outputs of thekattention heads are concatenated and linearly transformed to produce the final attention output: MultiHead(Q, K, V) = Concatenate(head 1,head 2,· · ·,head k)W O (7) whereW O is a learnable projection matrix for integrating the head outputs. This design enables the model to simultaneously capture information from multiple feature subspaces, thereby strengthening its capability for sequence modeling and representation learning. 3.4.Pretrain loss The contrastive learning strategy adopted in this work follows Yue et al."},{"citing_arxiv_id":"2604.23824","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Resource-Lean Lexicon Induction for German Dialects","primary_cat":"cs.CL","submitted_at":"2026-04-26T18:09:56+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Random forests on string similarity features outperform LLMs for German dialect lexicon induction and boost dialect information retrieval by up to 50% in recall.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22328","ref_index":26,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting","primary_cat":"cs.LG","submitted_at":"2026-04-24T08:00:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"engineering often outweighs architectural complexity, with gradient boosting and quantile regression emerging as strong probabilistic baselines. In line with these findings, Gradient Boosted Trees like XGBoost dominate recent energy forecasting competitions [25, 4, 5], while random forest remains a robust, stable, and simple baseline widely adopted in practical applications due to its minimal tuning requirements and interpretability [26]. Large-scale empirical comparisons further report that such compact, task-specifically tuned tree ensembles remain highly competitive with task-specific deep learning architectures, as the limited historical data typical of individual energy forecasting tasks favor small, low-variance models with strong inductive biases and hand-crafted features over larger,"},{"citing_arxiv_id":"2604.22084","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Generating Synthetic Malware Samples Using Generative AI","primary_cat":"cs.LG","submitted_at":"2026-04-23T21:33:05+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Opcode-sequence generative models produce synthetic malware data that raises minor-class classification accuracy by up to 60% and overall detection to 96%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21042","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Interpretable Quantile Regression by Optimal Decision Trees","primary_cat":"cs.LG","submitted_at":"2026-04-22T19:40:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A novel algorithm learns sets of optimal quantile regression trees to predict full conditional distributions interpretably and efficiently.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19690","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Is the `Known' Enough? An Integrated Machine Learning Framework for Eclipsing Binary Classification and Parameter Estimation Based on Well-Characterized Systems","primary_cat":"astro-ph.SR","submitted_at":"2026-04-21T17:12:27+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An ensemble ML framework achieves 90.7% morphology classification accuracy and R² values of 0.77–0.92 for key parameters on held-out test data, with external validation against OGLE and Kepler catalogs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18910","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Predicting Redshift in Seyfert Galaxies Using Machine Learning","primary_cat":"astro-ph.GA","submitted_at":"2026-04-20T23:27:10+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"We restrict the sample to spectroscopically observed objects withclass = GALAXYandsubClass = AGN, thereby adopting the internal SDSS classification scheme to identify galaxies whose emission-line ratios are consistent with AGN activity. This clas- sification is assigned by the SDSS spectroscopic pipeline based on the criterion of Bolton et al. (2012): log \u0012 [OIII] Hβ \u0013 >0.7−1.2×log \u0012 [NII] Hα \u0013 −0.4,(1) which separates AGN-dominated systems from star-forming galaxies. The resulting parent sample consists of 23,797 objects. This choice is motivated by the need for a uniform and re- producible sample definition. Automated classifications ensure consistency across the dataset and reduce selection biases intro- duced by heterogeneous line-ratio criteria."},{"citing_arxiv_id":"2604.18579","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The T16 Planet Hunt: 10,000 New Planet Candidates from TESS Cycle 1 and the Confirmation of a Hot Jupiter Around TIC 183374187","primary_cat":"astro-ph.EP","submitted_at":"2026-04-20T17:59:06+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A transit search on TESS Cycle 1 full-frame images produced 10,091 new planet candidates down to T=16 mag, more than doubling the known TESS total, with one hot Jupiter confirmed by radial velocity.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Plugging in our best fit values we get a tidal circulariza- tion timescale oft e ∼0.5 Gyrs. The preference for slight eccentricity is marginal and could thus be an artifact of our fitting procedure. Equation 13 in Hara et al. (2019) allows the quantification of this so-called Lucy-Sweeney bias (Lucy & Sweeney 1971) if the true eccentricity is assumed to be near zero: b= r π 4−π σe (10) whereσ e is the measured uncertainty on the eccentric- ity. We measured a uncertainty ofσ e = 0.15, resulting in a bias ofb≈0.287. This result indicates that our fit is entirely compatible with a circular orbit. In ad- dition, the presence of potential additional planets in the system could drive eccentricity. If real, however, the 17 Figure 9.Period distribution of our candidates."},{"citing_arxiv_id":"2604.18083","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations","primary_cat":"cs.LG","submitted_at":"2026-04-20T10:59:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17622","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction","primary_cat":"cs.LG","submitted_at":"2026-04-19T21:21:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"STRIKE improves credit default prediction AUC-ROC by training independent models on feature groups and aggregating their outputs via a meta-learner, outperforming tree baselines and conventional stacking on three real datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13392","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold","primary_cat":"cs.AI","submitted_at":"2026-04-15T01:43:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ReSS uses decision-tree scaffolds to fine-tune LLMs for faithful tabular reasoning, reporting up to 10% gains over baselines on medical and financial data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12109","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Identifying Changing-Look AGN Transitions in Light Curve Data with the Zwicky Transient Facility","primary_cat":"astro-ph.GA","submitted_at":"2026-04-13T22:32:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A criterion of |Δg| > 0.4 mag and |Δ(g-r)| > 0.2 mag detects photometric CL-AGN transitions in 9.6% of known hosts with 1.6% false positive rate from simulations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"and on what timescales. 6.3.4.Propagation of a heating/cooling front N. P. Ross et al. (2018) proposed a scenario in which changing-look quasars are triggered by the propagation of a heating or cooling front through the accretion disk. The timescale for this to occur is given by tfront ∼20 yrs \u0012 h/R 0.05 \u0013−1\u0010 α 0.03 \u0011−1\u0012 MBH 108M⊙ \u0013\u0012 R 150rs \u00133/2 , (7) 16 where the viscosity and scale height of the disk mod- erate the speed at which the front can propagate (D. Stern et al. 2018, equation 7). N. P. Ross et al. (2018) proposed that changes at the innermost stable circular orbit, or ISCO (wherer ISCO ≡6r s) could deflate the in- ner disk, sending a cooling front outward. At later times a heating front would propagate inward, re-inflating the"},{"citing_arxiv_id":"2604.10293","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification","primary_cat":"eess.SP","submitted_at":"2026-04-11T17:17:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08021","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"SynQL: A Controllable and Scalable Rule-Based Framework for SQL Workload Synthesis for Performance Benchmarking","primary_cat":"cs.DB","submitted_at":"2026-04-09T09:20:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SynQL synthesizes diverse, execution-ready SQL workloads by deterministically traversing foreign-key graphs to populate ASTs, yielding high topological entropy and cost-model training data with R² ≥ 0.79 on held-out sets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05225","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R","primary_cat":"stat.CO","submitted_at":"2026-04-06T22:41:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.16378","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning","primary_cat":"cs.CL","submitted_at":"2026-03-24T20:21:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"RCT couples an LLM and Random Forest via RL feedback so each augments the other's features and rewards, producing consistent gains on three medical datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.03274","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Financial Dynamics and Interconnected Risk of Liquid Restaking","primary_cat":"q-fin.GN","submitted_at":"2026-03-23T10:58:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Renzo liquid restaking revenue is primarily predicted by EigenLayer value locked, token yield, and multi-blockchain expansion, with current bridge risks not imposing systemic threats to the restaking ecosystem.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.04925","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Detecting RAG Advertisements Across Advertising Styles","primary_cat":"cs.IR","submitted_at":"2026-03-05T08:16:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Entity recognition models detect ads in RAG responses effectively and stay robust when advertisers switch styles, while lightweight models like random forests and SVMs become brittle under the same changes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.21876","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard","primary_cat":"stat.AP","submitted_at":"2026-02-25T13:00:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.01548","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS","primary_cat":"astro-ph.GA","submitted_at":"2026-02-02T02:28:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.01119","ref_index":63,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings","primary_cat":"cs.LG","submitted_at":"2026-01-03T08:43:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Machine learning models trained on Bangladeshi community data achieve 89-90% balanced accuracy for early CKD detection using few accessible features, outperforming traditional screening tools and generalizing across external datasets from India, UAE, and Bangladesh.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.00857","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Harvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks","primary_cat":"cs.LG","submitted_at":"2025-12-30T01:04:22+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"AEF embeddings perform competitively with RS models for local agricultural tasks but show limited spatial transferability, time sensitivity, and interpretability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.23060","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Finding Quasars behind the Galactic Plane. IV. Candidate Selection from Chandra with Random Forest","primary_cat":"astro-ph.GA","submitted_at":"2025-12-28T20:04:55+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A Random Forest classifier on Chandra, Gaia, and CatWISE data identifies 1060 new quasar candidates behind the Galactic plane, with two spectroscopically confirmed at z~1.1-1.3.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.02132","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models","primary_cat":"cs.CL","submitted_at":"2025-06-02T18:01:56+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"We define selectivity at layerℓ as the difference between real and control accuracies: Selℓ =Acc real ℓ −Acc control ℓ (3) Higher values mean the classifier is extracting true linguistic information rather than just memorizing. Linear separability gap.To compare how much linguistic signal each probe type extracts, we com- pute the difference in selectivity: Gapℓ =Sel nonlin ℓ −Sel linear ℓ (4) Negative gap values indicate that additional (MLP) probe capacity captures spurious correlations rather than linguistic structure. 3 Experiments Using the methodology introduced in Section §2, we describe the components of our experimental setup: the datasets, model suite, and procedure for extracting token-level representations. 3.1 Datasets For our analysis of lexical identity and inflec-"}],"limit":100,"offset":0}}