arXiv:2608. 11318v1 Announce Type: cross Abstract: Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent.
By Yi Liu
arXiv:2608. 05958v1 Announce Type: new Abstract: The paper addresses several ranking-dependent decision support methods.
By Vitaliy Tsyganok, Sergii Kadenko, Oleh Andriichuk
arXiv:2608. 19748v1 Announce Type: cross Abstract: Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked completion according to a reward model.
By Yarin Bar, Yaniv Romano
arXiv:2608.21187v1 Announce Type: cross
Abstract: Pairwise reciprocal matrices are fundamental to the Analytic Hierarchy Process (AHP), a decision-making model. While the Direct Least Squares (DLS) m...
By Kevin Kam Fung Yuen
The paper introduces Net Utility, a data‑free metric for selecting which singular directions of low‑rank adapters (LoRAs) to keep when merging across tasks. By scoring each direction for task utility and interference, and then globally selecting the highest‑scoring directions under a total budget constraint, the method avoids the uniform‑budget assumption that hampers existing merging techniques. Experiments on vision and language tasks show that Net Utility‑based rank allocation yields about a 2% performance gain over other merging methods.
By Avinash Amballa, Yashas Malur Saidutta, Wenbo Li, Lazar Valkov, Srinivas Chappidi
arXiv:2607. 24063v1 Announce Type: new Abstract: On standard factuality tasks, frontier models now cluster near the top of the scale.
By Keyu Li, Jin Gao, Dequan Wang