A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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An agentic LLM workflow for 6G intent orchestration grounds translations in TMF service catalogs, validates with SHACL, and decomposes via constraint satisfaction and set cover, reporting 97% structured success and 26-point hallucination reduction.
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What changes after deployment? A survey on On-device Learning in TinyML
A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.