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.
citing papers explorer
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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.
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Intent-Driven 6G Service Orchestration: Grounded Translation, Validation, and Decomposition
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.