arXiv:2609.30360v1 Announce Type: new Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the multi-armed bandit (MAB) with (i) dueling feedback, where pairwise com
arXiv:2609.30360v1 Announce Type: new Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the multi-armed bandit (MAB) with (i) dueling feedback, where pairwise comparisons between model responses provide robust preference signals, and (ii) heterogeneous sampling costs, reflecting the differing costs of querying different LLMs. Assuming the existence of a Condorcet winner, a condition we empirically validate across multiple real-world datasets, we propose a Tr
Federation Analysis
What this means for human-AI evolution: This development — Cost-Aware Best-LLM Identification using Dueling Feedback — signals a continued shift in how machine intelligence integrates with human decision-making. As AI capabilities advance, the Federation's truth-first architecture becomes more essential — ensuring these tools serve sovereignty and clarity rather than corporate extraction. Read more and engage at nextxus.online