AuditFL introduces a contract-backed clearing layer that enables auditable federated learning in open settings by keeping training and aggregation off chain while recording only content identifiers and economic state on chain. Clients reserve update tickets with deposits, publish update identifiers, and claim refunds or rewards, while the task coordinator publishes global checkpoints and finalizes rounds through deterministic settlement rules. Experiments on MNIST with up to 200 clients show that AuditFL preserves the learning trajectory with manageable overhead and remains robust under stress tests, keeping accuracy changes within a few tenths of a percentage point.
By Alexandros Balla, Spyros Lalis
arXiv:2609.27981v1 Announce Type: cross
Abstract: KV-cache eviction is typically evaluated through average quality-memory trade-offs, yet a small average loss can hide requests whose utility degrades...
By Beomgu Kang, SoJin Yun, Hojoon Kim, Hyunseok Seo
The paper introduces HealBench, a benchmark of 265 runtime errors from 18 real-world repositories, and HealGuard, a safety framework that restricts healing code to an analyzable subset of Python and applies static and dynamic taint analysis. Using these tools, the authors evaluate a dedicated healing method and three general coding agents powered by different LLM backbones, achieving a 38.11% resume rate and a 28.68% test‑pass rate, while HealGuard flags 17.4% of successful healings as potentially unsafe. The study demonstrates that current LLM agents can meaningfully repair real repository crashes, but also highlights significant safety concerns that the Guardrail framework can detect, albeit with a high false‑positive rate.
By Gou Tan, Pengfei Chen, Zhensu Sun, Jieke Shi, Junkai Chen, Ting Zhang, Weifeng Sun, Junda He, Shuai Liang, Chuanfu Zhang, Lwin Khin Shar, David Lo
arXiv:2605. 22781v2 Announce Type: replace-cross Abstract: LLM-powered AI agents require high-frequency state exploration (e.
By Yunpeng Dong, Jingkai He, Shiqi Liu, Yuze Hou, Dong Du, Zhonghu Xu, Si Yu, Baochuan Yang, Yubin Xia, Haibo Chen
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is evaluated only on sm...
arXiv:2607. 01646v2 Announce Type: replace Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware stack.
By Haotian Xie, Junlin Chen, Mingkai Zheng, Lishan Yang, Zhao Zhang