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Evidence record 6477 · automatically gathered

QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks

In agentic systems, human-generated data records anchor the value of AI services. Yet cloud compute pipelines centralize processing on remote servers. Data centralization reduces personal data sovereignty and may potentially degrade the quality of service (QoS). Meanwhile, user contributions are diverse in quantity and quality: decentralized records can be biased, noisy, and heterogeneously distributed. To address the data challenge, we study fair token allocation and private data valuation for

Record details

Published: 2 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (2 April 2026), “QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks,” evidence record 6477, https://ethics.ai/record/6477 (originally published by arXiv).

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