Introduces a multi-solution optimization framework for the Linear Ordering Problem (LOP) based on recent economic data, leveraging advanced metaheuristics to enhance solution diversity and quality.

Fabrizio Fagiolo, Marco Baioletti, Valentino Santucci

Annealed Softmax Greedy achieves near-optimal Bayes regret in many-armed Bayesian bandits.

William Overman, Mohsen Bayati

DiffoR: A unified framework using diffusion models for continuous generative ordinal regression, enhancing performance.

Hongxu Ma, Lin Wang, Chenghou Jin et al.

GUI-C² achieves 46.4% accuracy in GUI grounding via difficulty-aware reinforcement learning.

Junlong Li, Chao Hao, Lap-Pui Chau et al.

PAJAMA distills LLM evaluation into programs, matching 13B model accuracy while cutting API costs by 50×.

Tzu-Heng Huang, Shengqi Qiu, Frederic Sala

LISA combines linear attention and dynamic indexing to boost long-sequence reasoning speed by 50% with 5.6% accuracy gain.

Yu Zhao, Zekun Zhang, Fan Jiang et al.

FLAG optimizes flow policy with latent-augmented guidance, excelling in high-dimensional control tasks.

Sungha Kim, Gawon Lee, Jusuk Lee et al.

COFT employs counterfactual causal intervention with distribution-free marginal guarantees, reducing bias by 30-55% without retraining.

Arya Fayyazi, Mehdi Kamal, Massoud Pedram

This study analyzes effectiveness and RL training efficiency, revealing high sensitivity in evaluation and proposing two acceleration techniques.

Tong Liu, Cheng Qian, Matej Cief et al.

Enhancing selective classification accuracy and cost-efficiency using PairSel algorithms.

Harsh Vardhan, Sunav Choudhary, Natwar Modani et al.

Proposes CFO algorithm using sequential fine-tuning with augmented Lagrangian to balance reward maximization and constraints in molecular design.

Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger et al.

Proposes query-conditioned embodied AI world models to address physical inaccuracies in existing models.

Adam J. Thorpe, Stepan Tretiakov, Cheng-Hsi Hsiao et al.

The study shows padded Transformers are robust in expressivity, influenced by numerical precision and model depth.

Anej Svete, William Merrill, Ryan Cotterell et al.

DisjunctiveNet employs hierarchical convex hull relaxations to enforce hard, input-dependent mixed-integer linear constraints end-to-end, achieving 100% rule satisfaction.

Shraman Pal, Can Li

VideoMLA introduces low-rank latent KV cache, reducing memory by 92.7% for minute-scale video diffusion while maintaining high quality.

Hidir Yesiltepe, Jiazhen Hu, Tuna Han Salih Meral et al.

LLMSurgeon formulates data mixture diagnosis as a label-shift inverse problem, achieving 94.46% accuracy on the LLMSurgeon benchmark.

Yaxin Luo, Jiacheng Cui, Xiaohan Zhao et al.

NeuROK employs a transformer-based encoder-decoder to learn a low-dimensional latent space for 4D object dynamics, trained on large-scale geometric trajectories, bypassing predefined physical models.

Chen Geng, Guangzhao He, Yue Gao et al.

YoCausal employs a two-level causality benchmark using real-world videos and natural reversal, evaluating 13 SOTA video diffusion models' causal understanding via RSI and CCI metrics.

You-Zhe Xie, Yu-Hsuan Li, Jie-Ying Lee et al.

SchGen introduces a semantic-grounded code model for PCB schematic generation, achieving 82% valid circuits with 60.5% functional correctness from natural language prompts.

Qinpei Luo, Ruichun Ma, Xinyu Zhang et al.

Proposed VisAnomReasoner fine-tuned on VisAnomBench achieves 74.30% precision and 72.17% F1 in time-series anomaly detection, surpassing baselines by over 21 and 23 points.

Xiaona Zhou, Muntasir Wahed, Tianjiao Yu et al.