Generative Self-Healing Storage and Transmission

Deep space missions face catastrophic data loss from transmission interference and radiationinducedhardware failures. Traditional error correction exhibits hard failure thresholds wheredata becomes unrecoverable once corruption exceeds design limits. We present an LFSR-basedgenerative storage architecture where data recovery operates through mathematical state reconstructionrather than direct bit-level correction. We demonstrate that the recoverability oflinear feedback shift register states, normally considered a cryptanalytic vulnerability, can beintentionally leveraged to construct a state-recoverable storage architecture in which completedata can be regenerated from small, robustly protected fragments rather than corrected at thebit level. By storing data as modular differences against a deterministic LFSR keystream, any31 consecutive known-good symbols enable complete dataset recovery through LFSR state recoveryand keystream regeneration. We demonstrate this across datasets from 100 KB to 10MB (100 trials, 100% success rate, mean recovery time 1.06 ± 0.03 s for 500 KB). In directcomparison with Reed-Solomon codes under identical 14.3% overhead, LFSR recovers 2,296×more data (100% vs 0.04%) when protected segments survive extreme (99.995%) corruption.The method provides three operational modes for space probes: (1) transmission recovery fromsevere data loss, (2) autonomous hardware failure recovery (Voyager-style stuck-bit scenarios),and (3) emergency ground-assisted buffer management (28× local storage reduction) for criticalmission phases. This approach addresses fundamental limitations in current spacecraft data.