Solve the Loop: Attractor Models for Language and Reasoning
Attractor Models enhance language modeling and reasoning via fixed-point solving, improving training efficiency by 46.6% and accuracy by 19.7%.
Jacob Fein-Ashley, Paria Rashidinejad
Attractor Models enhance language modeling and reasoning via fixed-point solving, improving training efficiency by 46.6% and accuracy by 19.7%.
Jacob Fein-Ashley, Paria Rashidinejad
Multi-stream LLMs unlock language models with parallel streams of thoughts, inputs, and outputs, enhancing efficiency and security.
Guinan Su, Yanwu Yang, Xueyan Li et al.
Proposes HDET method to improve optimization quality and generalization of large models via automatic learning rate exploration.
Hailing Cheng, Tao Huang, Chen Zhu et al.
Efficient learning by implicit exploration in bandit problems with side observations, achieving near-optimal regret guarantees.
Tomas Kocak, Gergely Neu, Michal Valko et al.
Kolmogorov-Arnold Networks achieve universality with a single non-affine function.
Vugar Ismailov
Proposed a Collocation-based Robust Physics-Informed Neural Network (CRVPINN) for simulating pollution propagation under thermal inversion conditions on Spitsbergen.
Leszek Siwik, Maciej Sikora, Natalia Leszczyńska et al.
Budget-efficient scaling law fitting via active experiment selection achieves full dataset performance using only 10% of the budget.
Sijie Li, Shanda Li, Haowei Lin et al.
Using BantuMorph v7, a neural model recovers historical lexical structures in Bantu languages from modern data, confirming 90.9% noun candidates align with Proto-Bantu forms.
Hillary Mutisya, John Mugane
Zero-shot morphological discovery in low-resource Bantu languages via cross-lingual transfer and unsupervised clustering.
Hillary Mutisya, John Mugane
WG-SRC provides operational feature fingerprints for graph datasets using a white-box signal-subspace probe, enhancing node classification accuracy.
Yuchen Xiong, Swee Keong Yeap, Zhen Hong Ban
Evaluates eight Shapley variants' human utility in high-risk settings, revealing misalignment between current metrics and human perception.
Inês Oliveira e Silva, Sérgio Jesus, Iker Perez et al.
Decoding high-dimensional finger motion using Riemannian features and RNNs, TRR achieves 9.79° error on EMG-FK dataset.
Martin Colot, Cédric Simar, Guy Cheron et al.
HubRouter replaces O(n^2) attention with O(nM) routing for efficiency gains.
Abhinaba Basu
SharpAP significantly enhances the transferability of poisoning attacks on recommender systems, showing improved performance across multiple datasets.
Junsong Xie, Yonghui Yang, Pengyang Shao et al.
ReCast framework improves Pass@1 by 36.6% in generative recommendation, optimizing sparse-hit signals.
Peiyan Zhang, Hanmo Liu, Chengxuan Tong et al.
LTBs-KAN enhances KAN efficiency with linear-time B-spline computation.
Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Introduces 'sharpness dimension' to explain improved generalization at the edge of stability.
Mario Tuci, Caner Korkmaz, Umut Şimşekli et al.
Proposes Safe EWC and CF-EWC algorithms for safe continual reinforcement learning in non-stationary environments.
Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro et al.
FASTER method reduces computational cost by early action sample filtering during denoising while maintaining RL performance.
Perry Dong, Alexander Swerdlow, Dorsa Sadigh et al.
Adversarial training enables Vision Transformers to achieve near-zero robust training loss and robust generalization error under moderate perturbation budgets.
Jiaming Zhang, Meng Ding, Shaopeng Fu et al.