PEHT integrates LoRA-enhanced Transformer with urban mobility and congestion data, achieving state-of-the-art network traffic prediction with over 90% parameter reduction.

Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast

MMAO: bio-inspired self-adaptive optimizer using endogenous resource loops for continuous and discrete problems.

Jinliang Xu, Liping Ma

MultiHashFormer employs a multi-hash signature mechanism supporting causal language modeling, outperforming standard Transformers across 100M-3B parameters, with zero-parameter multilingual vocabulary expansion.

Huiyin Xue, Atsuki Yamaguchi, Nikolaos Aletras

RiVER leverages score-based optimization with instance ranking and winner-heavy rewards, improving LLMs on algorithmic tasks by 8.9%-9.4%.

Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang et al.

RouterVLA leverages smoke tests as supervision via outcome-disjoint cross-fitting, boosting heterogeneous VLA policy selection success from 46.86% to 61.49%.

Xingyu Ren, Chugang Yi, Ge Ma et al.

Error-conditioned neural solver (ENS) uses PDE residuals as direct inputs, achieving 10× accuracy improvement and avoiding costly optimizers.

Haina Jiang, Liam Wang, Peng-Chen Chen et al.

SAM2Matting leverages pre-trained VOS trackers (e.g., SAM2, SAM3) with region proposal and multi-scale alpha refinement, achieving state-of-the-art zero-shot video and image matting performance.

Ruiqi Shen, Guangquan Jie, Chang Liu et al.

PEEU framework enables small multimodal models to achieve 30.6% success in web navigation by autonomous exploration and hindsight experience, outperforming larger models.

Tianyi Men, Zhuoran Jin, Pengfei Cao et al.

Introduces two activation regularizers for Top-k sparse autoencoders, enhancing interpretability and robustness without sacrificing reconstruction quality.

Nathanaël Jacquier, Maria Vakalopoulou, Mahdi S. Hosseini

This study pioneers the application of Large Language Models (LLMs) for securities eligibility screening at the German Central Bank, achieving up to 91% document-level precision through a generative information extraction pipeline.

Serhii Hamotskyi, Akash Kumar Gautam, Christian Hänig

This paper establishes the algorithmic equivalence between Blackwell approachability and Gradient Equilibrium (GEQ), unifying online decision frameworks.

Brian W. Lee, Nika Haghtalab, Michael I. Jordan et al.

Ribbon: Influence-function-based scalable approximation for Bayesian uncertainty, improving calibration and efficiency.

Graham Gibson, John Tipton, Kellin Rumsey et al.

Introduces decision-alignment framework for uncertainty quantification (UQ), critiques traditional metrics, and proposes prior-weighted utility (PWU) metrics, validated through experiments showing superior decision utility alignment.

Annika Schneider, Tommy Rochussen, Joshua Stiller et al.

AgentX employs a multi-agent system for autonomous self-iteration in industrial recommender systems, significantly boosting efficiency and business value.

Changxin Lao, Fei Pan, Guozhuang Ma et al.

Proposes Bézier Walk Evolution (BWE), integrating geometry-driven path construction with distance-aware random walks, balancing exploration and exploitation via adaptive curve order.

Jinpeng Wang, Xingguo Xu, Yujing Sun et al.

Introduces Red Queen Gödel Machine (RQGM), a co-evolving framework with learned evaluators, achieving 1.35-1.86× efficiency gains in code, science, and proof tasks under non-stationary utilities.

Alex Iacob, Andrej Jovanović, William F. Shen et al.

SimPhysNet combines physics-informed neural networks with self-supervised contrastive learning to achieve 96.06% accuracy in laser welding penetration prediction using only 200 labeled images.

Sen Li, Xiaoying Liu, Xiaojian Xu et al.

This paper develops a theoretical framework for synthetic augmentation in score-based imbalanced classification, showing limited gains under well-specified models and potential improvements under model misspecification.

Zhengchi Ma, Pengfei Lyu, Anru R. Zhang

This study reveals the asymmetric dynamics of rule survival in pretraining models, with support frequency predicting rule fate and demonstrating irreversibility via editing interventions.

Juliana Li, Diya Sreedhar

This study introduces a low-resource end-to-end Algerian dialect speech-to-speech system using Whisper, achieving 13.74% WER, integrating ASR, NLU, RAG, and TTS.

Dihia Lanasri, Fairouz Taki, Asma Kemmoum