Hidden Tape: Addressable Communication Beneath Identical Text

Identical text can conceal different messages in its token representation. Prior work uses this freedom to steer a model’s response through retokenization, or hides pay- loads in generated text whose surface changes with the message. We instead hold one text fixed and ask whether its token order can store a message that a later query reads by address. We introduce Hidden Tape, an addressable communication chan- nel that encodes messages through token order while preserving decoded bytes, to- ken count, and the complete token histogram. A writer commits a token sequence before the query arrives, so one fixed sequence must support every address. We construct matched token cells that realize this channel and demonstrate reliable neural readout and learned writing. Three supervised-calibrated Qwen2.5-0.5B readers recover every bit of all 256 eight-bit messages after being frozen. An MLP writer trained with scalar correctness rewards achieves 99.98% query accuracy in the sixteen-bit setting. Targeted cell interventions verify address-specific readout. Decoding and canonically re-encoding the eight-bit tapes preserves their text while erasing the message, reducing the same readers from 100% to exactly 50% accu- racy. These results establish token order as writable, addressable message state beneath a fixed textual surface and show how the transport interface preserves or removes that state.