arXiv:2608. 15565v1 Announce Type: new Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide.
By Junbo Jacob Lian, Huiling Chen, Hanzhang Qin, Chung-Piaw Teo
RCProb is a probabilistic extension of rule extraction from tree ensembles that improves probability estimates by using smoothed atomic class-conditional evidence and a support‑adaptive mixture for final rule probabilities. Compared to RuleCOSI+, RCProb reduces median paired log‑loss by 71.9% for random forests and 62.5% for gradient boosting, while also decreasing the number of extracted rules by about 38% for both ensemble types. The method shows significant improvements in calibration metrics such as Confidence‑ECE and competitive native probability estimates, with further gains possible through post‑hoc calibration.
By Josue Obregon
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
By Srikumar Krishnamoorthy
arXiv:2609.26839v1 Announce Type: cross
Abstract: Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deploymen...
By Zeming Liu, Hang Lyu, Jingtao Zhang, Yuan Xie
arXiv:2606. 29091v1 Announce Type: cross Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them.
By Tassilo Klein, Johannes Hoffart
HydroJEV is a training‑free, one‑second model that classifies SCADA alarms in water distribution networks into cyberattack, physical fault, normal transient, or faulty sensor. In a benchmark on the C‑Town EPANET network, HydroJEV matched a hand‑written rule tree and outperformed a supervised classifier, especially when few labeled events were available, while being 20‑40 times faster than large language models. When combined with a rule tree gate, it reduced the need for human review by about a third without sacrificing accuracy.
By Tianwei Mu, Shengyan Jiang, Mingzhe Yuan, Qing Luo, Min Xiao, Wenhong Wang, Jun Li, Manhong Huang