Proposes graph-bound execution-state capsules for low-latency, small-batch on-device AI, enabling byte-exact snapshot and restore with sub-millisecond GPU performance.

Liang Su

Proposes Piecewise Polynomial Interpolation-based Gradient Descent (PPI-GD) achieving oracle complexity of O((p/ε)^{d/(2ℓ)}) for data dimension d=O(log^{0.49}(n)), outperforming classical GD/SGD.

Dongmin Lee, William Lu, Anuran Makur

Proposes STARE, a surprisal-guided advantage reweighting method, stabilizing policy entropy and improving accuracy by 4%-8% on models from 1.5B to 32B.

Haipeng Luo, Qingfeng Sun, Songli Wu et al.

Proposes a zero-shot active feature acquisition framework using LLM-derived discriminative statistics and MaxEnt closure, significantly improving IBD diagnosis accuracy.

Binyamin Perets, Natalie Mendelson, Shiran Vainberg et al.

Proposes LoopWM, a parameter-shared transformer with iterative latent refinement, achieving 100× parameter efficiency and stable long-horizon environment prediction.

Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang et al.

Introduces Kolmogorov PDE-based diffusion policies with dimension-independent convergence, improving long-horizon control in robotics and manufacturing.

Lekan Molu

This paper analyzes the sparsity and geometric structure of on-policy distillation (OPD), revealing small, coordinate-sparse updates that are spectrally concentrated and deviate from source principal directions.

Guo Yu, Wenlin Liu, Yulan Hu et al.

Proposes Bebop with TV loss and rejection sampling to stabilize MTP acceptance rate, achieving up to 95% and 1.8× RL training acceleration.

Yucheng Li, Huiqiang Jiang, Yang Xu et al.

This study compares xLSTM, Mamba-2, and Gated DeltaNet architectures, demonstrating xLSTM's superior performance in complex sequence tasks due to its robust state tracking and memory accumulation.

Anamaria-Roberta Hartl, Levente Zólyomi, David Stap et al.

Proposes Latent World Recovery (LWR), a robust multimodal learning framework that aligns modality-specific embeddings in a shared latent space, handling missing modalities without imputation.

Hui Wang, Tianyu Ren, Joseph Butler et al.

This paper offers a machine learning perspective on uncertainty in dynamical systems, distinguishing aleatoric and epistemic uncertainties, and analyzing their propagation across tasks.

Yusuf Sale, Christopher Bülte, Felix Czaja et al.

EEVEE framework employs a router-conditioned prompt set with co-evolution to enhance LLM robustness across heterogeneous task streams, improving scores by 10.38-24.32 points.

Weixian Xu, Shilong Liu, Mengdi Wang

Unified analysis of GP-UCB and DEC in RKHS bandits via MAIR framework, revealing their fundamental differences and advantages.

Yunbei Xu

COGENT integrates Graph Neural Networks with Neural ODEs for continuous long-term physical forecasting on irregular meshes, outperforming traditional autoregressive models.

Zesheng Liu, Maryam Rahnemoonfar

Proposes an efficient online algorithm for drifting halfspaces under Massart noise, achieving an error bound of η + ˜O(Δ^{1/3}/γ), nearly matching theoretical limits.

Mingchen Ma, Guyang Cao, Jelena Diakonikolas et al.

This paper introduces flexible sequence kernels based on evolutionary substitution matrices, leveraging Gaussian processes for data-efficient protein property prediction, outperforming embedding-based methods.

Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani et al.

DRPO introduces smooth advantage-weighted quadratic regularization to improve stability and efficiency in LLM RL training, replacing hard masks with continuous gradient weights.

Jiarui Yao, Xiangxin Zhou, Penghui Qi et al.

Introducing Topological Neural Operators (TNO), a framework leveraging cell complexes and discrete exterior calculus to improve PDE modeling on complex geometries, achieving over 20% accuracy gains.

Lennart Bastian, Samuel Leventhal, Mustafa Hajij et al.

Proposes an fully automated time-series forecasting framework combining high-frequency dataset TimeTrack with dynamic local telemetry, using NAS to generate accurate models, effectively addressing cold-start issues.

Abd Elghani Meliani, Arora Sagar, Adlen Ksentini et al.

Proposes CoMetaPNS, integrating continual Bayesian GMM with set-conditioned generative models for personalized cardiac electrophysiology simulation, achieving superior accuracy and anti-forgetting.

Ryan Missel, Xiajun Jiang, Linwei Wang