arXiv:2510. 22138v3 Announce Type: replace Abstract: We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evaluation scheme on a tensor-network (TN) surrogate: TN-SHAP.
By Farzaneh Heidari, Chao Li, Guillaume Rabusseau
QuadraSHAP is a method for computing ε-exact Shapley values in product games, where coalition values factor across players. It replaces the exponential coalition sum with a one-dimensional polynomial integral, using Gauss–Legendre quadrature to achieve exact values when ε = 0 and provides a computable error bound for ε > 0. The approach supports weighted sums of product games, enabling baseline and empirical interventional attribution for models such as log-link regression, Cox models, odds-scale classifiers, product-kernel machines, and tree-based models, and achieves logarithmic parallel time with efficient GPU evaluation even for hundreds of thousands of features.
By Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn, Krikamol Muandet, Siu Lun Chau
arXiv:2608. 06422v1 Announce Type: new Abstract: Giving an LLM judge more compute does not necessarily make it check more requirements.
By Victor Akinwande, J. Zico Kolter, Aran Nayebi
arXiv:2606. 07560v1 Announce Type: cross Abstract: Function-vector (FV) heads (Todd et al.
By Han-yu Wang
arXiv:2605. 11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents.
By Ling Tang, Jilin Mei, Qian Chen, Qihan Ren, Linfeng Zhang, Quanshi Zhang, Jing Shao, Xia Hu, Dongrui Liu
arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.
By Seungeun Lee, Joao Fonseca, Julia Stoyanovich