A new module called Self-Adaptive Scale-handling (AS) addresses scale heterogeneity in time series forecasting. It uses Scale Calibrating and Scaling Selection to adaptively adjust scaling factors, preserving semantic discriminability and reducing inverse-scaling errors. Experiments on fund sales data show improved performance when integrated into existing forecasting models.
A hierarchical system using a pretrained LLM to select RL skill policies outperforms flat RL in a 2v2 King of the Hill environment. It matches hand-crafted behavior tree performance in win rate and is perceived as more human-like by 60% of users, highlighting effective coordination and adaptability without manual rule design.
An adaptive-trunk DeepONet framework predicts localized structural responses in long-span bridges with high accuracy. By using distance-aware features and a stiffness-informed Schur complement, it achieves FEM-level accuracy with less than 5% error, reducing total response evaluation time by 60x and inference speed by up to four orders of magnitude compared to finite element methods.
PU-UNet introduces stable product-unit residual blocks into U-Net for medical image segmentation, enabling explicit multiplicative feature interactions without numerical instability. It achieves high Dice scores on ISIC 2018, Kvasir-SEG, and BUSI, outperforms a Residual U-Net baseline in Dice and IoU, and eliminates false-positive rates on normal BUSI cases.
A new study combines 3D MRI and PET data using advanced fusion strategies including GMU and gated self-attention, along with a sparsely gated MoE classifier. Results show GMU achieves 80.46% accuracy on NC vs. MCI and 95.47% on NC vs. AD, with gated self-attention reaching 82.08% on MCI vs. AD. Ablations confirm the MoE significantly improves performance, highlighting the importance of input-adaptive multimodal modeling for accurate Alzheimer's diagnosis.
The AI Economist Agent uses RAG, knowledge graphs, and LLMs to generate economic narratives grounded in theory and data. It enables model-based analysis, evidence retrieval, and report generation, ensuring economic coherence and traceability through explicit model computations.
A comparative study evaluates four neural architectures—MLP, ResNet, U-Net, and FNO—as autoregressive predictors of internal battery states using the Doyle-Fuller-Newman model. The U-Net achieves a mean final-step nRMSE of 3% across all state variables and provides a 5.38x speed-up over numerical solvers, demonstrating the importance of spatial inductive bias in surrogate performance.
PaAno+ introduces a lightweight model that uses multiscale convolution and cross-variable attention to improve time series anomaly detection. It achieves state-of-the-art accuracy on both univariate and multivariate tasks, with superior performance in VUS-PR and other metrics, while maintaining efficient computation for real-time deployment on resource-limited devices.
The paper introduces optimal coarse correlated equilibria for continuous-time mean field games. It presents a linear programming formulation, proves existence, and develops a no-regret learning algorithm with explicit convergence rates, supported by numerical examples.
A study evaluates EEG Foundation Models for event-based burst-suppression detection in ICU EEG without patient-specific calibration. REVE-base achieved the highest event-based F1-score of 0.868 and reduced burst-per-minute error by 52.1% compared to EEGNet. Ablation experiments show full fine-tuning outperforms other strategies, and pretrained REVE-base surpasses random initialization by 0.723 F1 points at 25% labeled data.
This paper identifies a dual collapse in latent reasoning: gradient attenuation and representational drift. It proposes Trajectory and Space Supervision, showing that generative reconstruction preserves information capacity better than geometric compression. The Unified Latent Probe measures mutual information between latent trajectories and reasoning steps, revealing an information-performance binding in reasoning accuracy.
MakeupMirror, a diffusion-based makeup transfer model, achieves +60% improvement in facial recognition similarity and -50% reduction in skin tone difference compared to Stable-Makeup. It preserves facial features and skin tone with expert acceptance of 94% across identity criteria, operating at 0.7s latency through a Levenberg-Marquardt Langevin sampler.
A sensorimotor world model (SMWM) is introduced that learns compact, action-aligned latent representations from offline trajectories. It uses inverse dynamics regularization to prevent representation collapse and enable stable, interpretable world models without requiring frozen encoders or complex regularizers. SMWM achieves competitive planning performance in 2D and 3D control tasks.
A new ensemble method for finite-horizon MDPs uses quantile-based estimates to achieve minimax optimal regret bounds. It eliminates reliance on count-based uncertainty and provides theoretical justification for ensemble-based exploration in reinforcement learning.
Pose6DAug enables robot data augmentation by swapping objects in successful episodes while preserving physically valid 6D pose trajectories. It operates in 3D using a mesh anchored by temporally coherent poses, ensuring multi-view consistency and physical plausibility. Fine-tuning a VLA policy on this augmented data improves novel object success rates by 16.5% over state-of-the-art baselines.
Benchmarks using fixed-shape checks miss real bugs in LLM-generated GPU kernels. A controlled corpus of 24 kernels, including 9 buggy variants with transcription errors, reveals that an op-schema-aware oracle detects all failures and passes all correct controls, with identical results across five GPU architectures.
A new system uses subject-aware prompting to adapt tutoring strategies based on student performance and discipline. A/B testing with 656 student conversations shows the model reduces interactions by 3 turns and increases learning strategy conversion from 19.1% to 28.1% with a stochastic router.
MedRLs enables long-context clinical reasoning by recursively inspecting patient data across text, images, sensors, and guidelines. It integrates specialized agents and a Clinical Evidence Graph Memory to connect observations with evidence and referral criteria, supporting sensor-triggered reasoning and uncertainty-gated clinician review.
MELT and SALT are multimodal contrastive learning models that use unpaired geospatial data to improve location embeddings. Both achieve performance equal to the best two-modality baseline across four tasks, but adding more modalities does not consistently boost results, indicating the location encoder's design is the primary performance limit. MELT offers more stable training and is better suited for future model scaling.
A machine learning pipeline using multi-modal fetal MRI data predicts gestational age at birth with an R2 of 0.13 and a mean absolute error of 2.74 weeks. It achieves 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity, with cervical length and placental T2* statistics as key features. This work presents a proof of concept for predicting preterm birth using MRI and machine learning.