arXiv:2607. 10305v1 Announce Type: cross Abstract: Byzantine-robust aggregation rules such as multi-Krum assume a central coordinator, and decentralising them is obstructed by the rules themselves: they are globally coupled, non-associative, and discontinuous, so an ulpscale perturbation can flip the selected subset, moving the output by a non-vanishing amount.
arXiv:2607. 16109v1 Announce Type: new Abstract: State machine replication (SMR) and Byzantine fault-tolerant (BFT) consensus guarantee agreement despite a bounded number of arbitrary, colluding faulty participants.
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.
arXiv:2606. 19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem.
The paper introduces Preregistered Belief Revision Contracts (PBRC), a protocol that separates open communication from admissible epistemic change in deliberative multi-agent systems. PBRC fixes evidence triggers, revision operators, priority rules, and fallback policies, requiring that belief changes cite preregistered triggers and validated evidence tokens. The authors prove that PBRC prevents confidence inflation from conformity, preserves auditability, ensures epistemic accountability, and characterizes enforced belief trajectories under token-invariant contracts.
Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors.
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.
This paper introduces a Byzantine‑robust federated retrieval‑augmented generation (RAG) framework that uses aligned calibration and fixed‑membership conformal prediction to ensure that the answer set contains the correct answer with a chosen probability, even when some nodes are compromised. By having all nodes score the same calibration questions and retaining only candidates that could be kept by a plausible group of honest nodes, the method guarantees correctness in finite samples and produces smaller answer sets than simpler approaches. Experiments on medical exam question‑answering tasks with language‑model nodes demonstrate that the method meets the target coverage whenever the number of misbehaving nodes does not exceed the declared bound, while plain averaging often fails.
arXiv:2605. 06738v2 Announce Type: replace-cross Abstract: Autonomous AI agents already transact at production scale -- 69,000 bots, 165 million transactions, $50 million in volume on a single marketplace -- and any party can verify a signed credential without a central service.
arXiv:2606. 17182v1 Announce Type: new Abstract: Multi-agent LLM systems share state through memory stores, vector indices, and tool registries.
arXiv:2606. 03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another.
arXiv:2607. 05397v1 Announce Type: cross Abstract: Agent systems increasingly execute rather than advise.
The paper introduces SWB-DM, a Byzantine‑robust federated learning aggregator that treats each slice of a client update as a one‑dimensional distribution, computes a trimmed Wasserstein barycenter across clients, and uses a medoid‑based gauge‑fixing step to recover coordinate identity. It further incorporates delayed‑momentum caching to decouple robustness from the specific clients sampled each round. Extensive experiments on CIFAR‑10, CIFAR‑100, FEMNIST, and a 500‑client scalability run reveal distinct failure modes of existing defenses and demonstrate that SWB‑DM achieves significant gains, especially when compared under equal round budgets.