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
arXiv:2607. 11149v1 Announce Type: new Abstract: LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including logs, context snapshots, checkpoints, and debug traces.
By Chenglin Yu, Hongquan Gui, Ying Yu, Hongxia Yang, Ming Li
The paper presents RuntimeGuard‑AI, a prototype that links each deterministic AI policy decision to its source code, writes a privacy‑minimizing record at a chosen synchronization point, and returns an Ed25519‑signed receipt indicating whether the write succeeded. After a crash, the system validates the integrity of records, manifests, shard placement, sequence continuity, and replay identity, while an independent attestation path chains committed records into signed Merkle epochs for auditor verification. Performance results on an Apple M4 Pro show high throughput (up to 27,193 requests/s) with low latency when buffering, but throughput drops and latency rises when per‑record data and full synchronization are used, illustrating a clear durability‑latency trade‑off.
By Neeraj Kumar Singh Beshane
arXiv:2607. 09259v1 Announce Type: cross Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability.
By Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli
arXiv:2607. 01646v1 Announce Type: new 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
arXiv:2606. 30306v1 Announce Type: cross Abstract: Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions.
By Tianyu Ding, Aditya Nannapaneni, Bingfan Liu, Ling Zhang
arXiv:2608. 20280v1 Announce Type: cross Abstract: Semantic caches reuse an LLM response when the incoming query embedding lies near a cached query, but proposed eviction policies have rarely been compared under one protocol.
By Yash Kulkarni, Shubham Harkare, Arvind Suresh Yogesh Babu