XGBoost-Forget enables efficient machine unlearning for XGBoost models on tabular network intrusion datasets. It maintains model performance while achieving faster unlearning compared to full retraining, addressing a gap in unlearning research for tabular data in network intrusion detection.
A new taxonomy connects Alzheimer's and dementia caregiver mental health needs with technology interventions. It identifies gaps in support for issues like relational strain and compassion fatigue, and offers a shared framework for designing person-centered, clinically grounded technologies.
Dango is a 1.8B-parameter LLM designed to study Japanese-to-English second language acquisition. It uses a filtering method to minimize English contamination in monolingual pretraining, preserving realistic L1 exposure. Fine-tuned on LLM-generated lessons, Dango produces human-like L2 outputs, outperforming unfiltered and standard multilingual models.
LOCUS provides machine-readable access to nearly all publicly available U.S. municipal and county ordinance codes, covering 9,239 cities and counties. It includes a county-harmonized access layer for 2,309 of 3,144 U.S. counties, serving the majority of the population. The corpus, built with OCR and metadata for reproducibility, enables large-scale analysis of local law, including dimensions like opacity and paternalism, using ModernBERT-based models.
This paper introduces StruBI, an algorithm that identifies hidden confounding and selection biases by analyzing causal mechanism shifts across environments. It formalizes a mutual information-based criterion to detect structural biases and demonstrates superior performance in recovering biased variables on synthetic and real-world data.
A meta-feedback semi-supervised learning framework enables scaling of automatic emergency braking using massive unlabeled fleet data. The stabilized approach reduces pseudo-label errors and suppresses risk hallucinations, achieving a 100:1 positive-to-false activation ratio and 35% more accident-free driving mileage compared to a rule-only baseline in real-world deployment.
A domain-shift aware neural network is proposed for estimating unbalance masses in rotating shafts under varying operating conditions. The model uses maximum mean discrepancy to align feature representations across different operational domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications where domain discrepancies are unknown or unaccounted for.
TransitNet, a compact attention-augmented deep learning framework, achieves 95.2% accuracy in low-SNR transit blind searches, outperforming TLS and BLS in ROC-AUC and PR-AP values. It recovers 93.0% of injected Earth- and sub-Earth-size transits, with 97.4% of injected transits fully covered by estimated transit windows, and successfully recovers all 34 confirmed Kepler planets with a mean midpoint error of 1.24 hours.
A new framework enables zero-shot active feature acquisition by leveraging LLMs to elicit only discriminative statistics like unary deviations and pairwise co-variations. Using maximum-entropy closure, it resolves ambiguity in feature selection and outperforms LLMs itself, especially on challenging IBD patient cases where diagnostic uncertainty is high.
A controlled benchmark finds no significant performance gain from quantum generative models in brain MRI augmentation. Synthetic samples produced by quantum and classical GANs are statistically indistinguishable, with both showing mode collapse and off-distribution samples, especially at low data fractions. The study concludes that quantum augmentation does not outperform classical methods and acts more as regularization than data expansion.
A new method introduces adaptive betting with kernel statistics to test conditional independence, reducing Type I error inflation due to estimation error. It outperforms existing sequential Model-X approaches in both synthetic and real-world fairness tasks, maintaining high power while being more robust to distributional estimation errors.
DIPHINE is the first neural estimator that uses score-based diffusion models to jointly estimate all mutual information terms required by Integrated Information Decomposition ($Φ$ID) from a single amortized network. It recovers the sixteen non-overlapping information atoms via Möbius inversion and provides a theoretical analysis showing synergy-to-synergy estimation is the most challenging, with accurate results on synthetic benchmarks and real-world physiological data.
Sumi is a 7B-parameter uniform diffusion language model pretrained from scratch on 1.5T tokens. It competes with autoregressive models on knowledge, reasoning, and coding tasks but underperforms on commonsense benchmarks, likely due to its education-heavy data mixture. The model weights, checkpoints, and full training recipe are publicly released.
Moat is a dynamic analysis approach that secures ML model execution by monitoring host system interactions during well-defined model lifecycle phases. Re-Moat, its reference implementation, detects all evaluated attack classes with a near-zero false-positive rate across 77,974 real-world models and multiple frameworks, outperforming existing static model-scanning solutions.
This paper analyzes discontinuities in Sparse Mixture-of-Experts models, classifying them by order and showing that lower-order discontinuities dominate in volume. It proves that random input paths almost surely first hit an order-1 discontinuity with finite-time probability bounds and derives occupation-time bounds for each order. A simple smoothing mechanism is proposed that enhances model continuity and performance with minimal computational overhead.
A new framework uses positive-unlabeled learning and Partial Optimal Transport to audit LLM evaluation biases. It aligns human-verified positive outputs with unlabelled model responses in embedding space, identifying consistent human preferences and correcting verbosity bias without retraining. Experiments show improved human alignment, robustness to presentation biases, and interpretable confidence estimates.
A study uses a Contextual Markov Decision Process to optimize follow-up intervals for Type 2 Diabetes patients based on EHR data from 22,154 patients. The model identifies two clinical contexts—low and high risk—and recommends adaptive intervals: 1 month for unmeasured lab values, up to 3 months for elevated values or hospitalizations, and 6–12 months for stable control, with shorter intervals for high-risk patients. The CMDP policies reduced expected cumulative costs by 34.8% in high-comorbidity and 6.4% in low-comorbidity contexts compared to a fixed interval policy.
A study using SHAP and SSHAP techniques analyzes electricity price drivers across 39 European bidding zones. It finds solar energy has a disproportionate impact on prices, gas remains a dominant factor, and interconnections highlight regional interdependence. The research also builds a synthetic EU-wide market to examine a fully integrated, single-price scenario.
Giskard enables confidential and Byzantine-robust decentralized machine learning aggregation by organizing parties into tree-based committees of size O(log n). It uses BGW-style MPC and a committee-adapted binary search to compute an approximate median, reducing per-party communication complexity asymptotically while maintaining model utility under up to n/4 Byzantine parties.
OrthoReg introduces orthogonal regularization to prevent neural components from relearning symbolic structures in hybrid dynamical systems. By directly penalizing overlap between symbolic and neural parts, it enables a complementary decomposition where symbolic models capture expressible physics and neural models handle remaining dynamics. On benchmarks with partial library mismatch, OrthoReg improves symbolic recovery and out-of-distribution performance.