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.
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.
The paper introduces a protocol for auditing and composing reinforcement‑learning policies using discrete behavioral rules, defining auditability through six testable predicates such as trace integrity and rule coverage. Experiments show that overlapping rule sets do not guarantee behavioral agreement, and that rule‑based fusion often fails to outperform value‑based composition, highlighting limitations in current description layers. The authors provide an evidence‑bounded audit framework and outline future directions for more robust skill composition.
arXiv:2607. 08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants.
ESCROW is a post‑deployment maintenance framework for large‑language‑model agents that operate under policy‑bound enterprise workflows. It ensures that any skill revisions proposed by the LLM are only deployed after empirical evaluation, using distributed diagnosis, consensus, per‑category non‑regression guards, cross‑cycle anti‑regression checks, and an accuracy–cost Pareto search to produce a versioned, auditable diff for each change. In a real production financial document‑auditing system, ESCROW achieved the best accuracy–cost trade‑off compared with baseline methods and demonstrated transferability on a public $ au$‑bench.
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.
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.
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
The paper presents a deployed system that scores blockchain addresses using their position in a massive multi‑chain transaction graph instead of relying on sanctions lists. The system operates on a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, employing a shared inductive encoder with per‑chain normalization and two scoring heads. It demonstrates label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis at a very low alert rate, and shows significant lead time over external registry events, while maintaining fast, reproducible serving performance.
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history.
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.
arXiv:2609.37011v1 Announce Type: cross Abstract: Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into...
The paper presents a compliance screening system that evaluates blockchain addresses by their position in a large multi‑chain transaction graph instead of relying on sanctions lists. Using a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, the system employs a shared inductive encoder with per‑chain normalization and two scoring heads, with decision thresholds set as exact quantiles of the score distribution. The authors demonstrate label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis, and report significant lead‑time in flagging external registry events, efficient serving latency, and robustness checks against adversarial behavior.