When Space Flips a Bit: Recovering Transformer Inference from Radiation-Induced Faults

As transformer models move into space, radiation-induced faults threaten inference reliability by silently corrupting hidden states. Existing defenses offer limited fault coverage, risk suppressing legitimate rare computations, or require hardware modifications that are impractical for commercial off-the-shelf accelerators. We introduce RIFT (Recovery via Inference-time Fault Tolerance), a five-stage pipeline that detects and corrects non-repeating transient corruption without retraining, architectural changes, or a clean reference model. RIFT screens hidden states using spectral, temporal, and cross-layer residuals, then re-executes flagged layers to distinguish corruption from legitimate novelty. It recovers corrupted states through uncertainty-aware spectral correction and gated consistency fusion, and adjusts output distributions according to estimated fault severity. Across language, vision-language, and vision-language-action transformers subjected to single-event upsets, multi-bit upsets, single-event transients, and bus corruption, RIFT approaches the task quality of triple modular redundancy. Under single-event upsets, it achieves WikiText-2 perplexity of 6.21 versus 6.15 and LIBERO task success of 76.8% versus 78.1%, with 5.5% clean-run overhead on LLaMA-3-8B and 95.7–97.1% novelty preservation across the three evaluated models. We further evaluate RIFT on a compact decoder running aboard a satellite in low Earth orbit. Across 400 controlled fault injections, RIFT achieves 91.5% fault detection and 91.7% recovered-token agreement with fault-free execution, with runtime overheads of 8.4% during clean execution and 12.6% under injected faults. This orbital experiment demonstrates recovery from controlled faults on hardware operating in space, while recovery from naturally occurring radiation-induced faults remains unverified. Together, these results support inference-time fault recovery that preserves legitimate model behavior across transformer workloads and extends to orbital computing.