The paper introduces Open-1B, a language model trained under a new fully auditable regime that ensures every training operation is reproducible on heterogeneous commodity hardware with bitwise certainty. By enforcing a fixed order on sources of nondeterminism—GPU reductions, data batch ordering, and inter/intra-node communication—the authors enable auditors to replay and verify individual training steps on a single machine. The release includes the full pretraining dataset, all intermediate checkpoints, the training codebase, and an audit harness for step-by-step verification.
By John Donaghy, Brian Wilcox, O\u{g}uzhan Ersoy, Shikhar Rastogi, Adam St Arnaud, Alexey Titov, Jordan Greenberg, Ben Fielding, Harry Grieve
arXiv:2607. 06643v1 Announce Type: cross Abstract: Backdoor attacks severely threaten large-scale AI models.
By Issam Seddik, Sami Souihi, Mohamed Tamaazousti, Sara Tucci Piergiovanni
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:2608. 14668v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) solve complex tasks through specialized collaboration, but inter-agent dependencies can propagate hallucinated or malicious outputs into system-level failures.
By Kaixiang Wang, Yidan Lin, Jiong Lou, Jie Li
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
By Hongliang Zhang, Zhongyuan Yu, Fenghua Xu, Teng Hu, Jian Meng, Jiguo Yu
arXiv:2607. 09682v1 Announce Type: new Abstract: AI systems are increasingly used to assist consequential decisions in regulated domains such as auditing, finance, and healthcare.
By Vimal Nakrani