Article

Intent Drift in LLM-Assisted Brain Computer Interface Communication: An In-Silico Benchmark Under Simulated Decoder Corruption

2026-08-27

Abstract excerpt

<h4>Background: </h4> Large language models are increasingly proposed to post-edit decoded text in communication brain-computer interfaces and augmentative communication. A fluent model can substitute a different intent than attempted (intent drift). Whether meaning survives or confidence flags failure is unmeasured. Methods In-silico benchmark of 20 open-weight models post-editing text (4,252,326 labeled generati...

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Literature Corpus work: 412cb921-24e7-59cb-afad-87b2e85d1343
DOI: 10.64898/2026.08.20.26360939

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