arXiv:2605.29139v1 Announce Type: cross Abstract: Federated Conformal RAG (FC-RAG) provides distribution-free coverage for a bandwidth-limited swarm of weak language models, but only at a fixed horizon. We extend it to anytime-valid sequential coverage: validity at every stopping time, preserved under predictable adaptive control (recalibration, per-node bandwidth escalation, distilled-student refresh), at no extra cost in assumptions over fixed-horizon FC-RAG. Naive composition fails because FC-RAG's marginal coverage bound makes the betting e-process a non-supermartingale on adverse calibrat
The proliferation of language models and demand for real-time, reliable AI systems drives research into robust, distributed inference mechanisms.
This development allows large language model swarms to provide continuously valid and adaptive coverage, improving reliability and trustworthiness in dynamic environments.
AI systems using weak language models can now collaboratively achieve robust, anytime-valid performance, enabling new applications in critical and distributed contexts.
- · AI developers
- · Distributed AI systems
- · Edge AI providers
- · Regulated industries adopting AI
- · Centralized AI architectures
- · Systems relying on fixed-horizon validity
Increased real-time reliability for AI applications, especially in resource-constrained or sensitive environments.
Accelerated adoption of federated learning and distributed intelligence paradigms in various sectors.
New standards and regulatory frameworks emerging for continuously valid and adaptive AI systems, impacting compliance and risk management.
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Read at arXiv cs.LG