Quasar is a new programming language designed to improve large language model (LLM) code actions by separating internal program logic from external tool calls. It allows developers to annotate external calls with effect information and modify internal execution to track these effects, enabling easier implementation of new features. The authors demonstrate Quasar’s utility by adding access control, autoparallelization, and conformal prediction for uncertainty quantification.
By Stephen Mell, Botong Zhang, David Mell, Shuo Li, Ramya Ramalingam, Nathan Yu, Stephan Zdancewic, Osbert Bastani
arXiv:2608.24531v1 Announce Type: cross
Abstract: Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse...
By Wingho Feng, Quanwang Li, Ming Zhong, Jingyu Yang, Chen Wang
arXiv:2608.23382v2 Announce Type: replace
Abstract: Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data t...
By Seokjin Hwang, Yuting Li, Kiwan Maeng
arXiv:2604.06825v2 Announce Type: replace
Abstract: Semi-supervised learning for LiDAR semantic segmentation often suffers from error propagation and confirmation bias caused by noisy pseudo-labels....
By Donghyeon Kwon, Taegyu Park, Suha Kwak
arXiv:2608.24551v1 Announce Type: cross
Abstract: Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate...
By Xitong Zeng, Zhaoge Bi, Yitian Yang, Huaming Chen, Quan Z. Sheng
arXiv:2608.24303v1 Announce Type: new
Abstract: Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one t...
By Mathis Jander, Wouter van Heeswijk, Martijn Mes
arXiv:2510.07289v2 Announce Type: replace
Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...
By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang
arXiv:2608.24568v1 Announce Type: cross
Abstract: Deep neural networks generalize well despite their highly nonconvex, overparameterized loss landscapes, a phenomenon often associated with the geomet...
By Paul Caillon, Christophe Cerisara, Alexandre Allauzen
arXiv:2608.24582v1 Announce Type: cross
Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
By Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook
arXiv:2608. 12231v2 Announce Type: replace Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.
By Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni
arXiv:2608.24650v1 Announce Type: cross
Abstract: System-level simulation is an essential tool for exploring the rapidly expanding design space of LLM serving systems, where real deployments remain c...
By Wonung Kim, Hyunmin Choi, Minsu Kim, Jaehong Cho, Yeongwook Kim, Jongse Park
arXiv:2608.24563v1 Announce Type: new
Abstract: Imaging factor disentanglement in text-to-image generation aims to independently control image acquisition properties such as types of camera lenses, s...
By Sonali Godavarthy, Matthias Neuwirth-Trapp, Tim-Felix Faasch, Maarten Bieshaar, Michael Moeller, Kristof Van Laerhoven, Danda Pani Paudel
arXiv:2603.17170v2 Announce Type: replace-cross
Abstract: AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAu...
By Reshabh K Sharma, Linxi Jiang, Shuo Chen, Zhiqiang Lin
The paper introduces the Sparse-Activation-ReLU (SAR) layer, a single‑step neural operator that promotes activation sparsity without surrogate‑gradient training and is compatible with event‑based computing. In a trunk‑based NOMAD architecture, SAR improves the combined Latency‑Error‑Energy (LEE) metric by over fivefold compared to Variable Spiking Neuron (VSN) and Leaky Integrate‑and‑Fire (LIF) models. Additional techniques such as synthetic knowledge distillation, a ReLU‑based spiking loss, and graph‑neighbor thresholding further reduce LEE and L2 error on the Heat Exchanger dataset, advancing energy‑efficient virtual sensing for edge deployment.
By William Howes, Farid Ahmed, Syed Bahauddin Alam
arXiv:2608.24541v1 Announce Type: new
Abstract: Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding...
By Xinning Yao, Jingjing Wang, Jinghua Yue, Xiaoyan Luo, Fugen Zhou, Bo Liu
arXiv:2608.24364v1 Announce Type: new
Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...
By Sebasti\'an Gonz\'alez, Karen Sanchez, Jos\'e M. Saavedra, Marcelo Pizarro, Bernard Ghanem
arXiv:2608.24053v1 Announce Type: new
Abstract: Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space...
By Junjie Zhou, Ke Mei, Lei Li, Tianyi Wang, Fengyun Rao, Jing Lyu
ESQ‑Bench is an Oracle‑first NL2SQL benchmark that introduces systematic complexity tiers and silent‑divergence evaluation across three enterprise schema levels. It provides six populated schemas (465 tables, 164,682 rows) on Oracle, PostgreSQL, MySQL, and SQL Server, along with 550 gold‑validated question‑query pairs and a four‑metric evaluation harness. The benchmark reveals that state‑of‑the‑art models such as GPT‑4o and Claude Sonnet 4.6 experience significant drops in execution accuracy and high silent‑divergence rates as schema complexity increases, highlighting a gap between closed‑API models and open‑weight baselines on enterprise Oracle schemas.
By Sanjay Mishra, Divya Chukkapalli, Ganesh R. Naik
arXiv:2608.24627v1 Announce Type: new
Abstract: We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank-$k$ matroid on $n$ elements, we give a...
By Zongqi Wan, Zhijie Zhang
arXiv:2608.23864v1 Announce Type: new
Abstract: Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be or...
By Junqiu Yu, Pandeng Li, Yikai Wang, Jiaxing Zhao, Yujie Wei, Kaixun Jiang, Quanhao Li, Hongtao Yu, Zhihang Liu, Zhaohe Liao, Junjie Zhou, Yun Zheng, Yu Liu, Yanwei Fu