Language model embeddings retain little input information, but autoencoders achieve near-perfect memory; combined causal and retention objectives enable rich, decodable memory formation for efficient encoder-decoder architectures.
DiagnosticIQ benchmarks LLM recommendation of industrial maintenance actions from symbolic rules across 6,690 questions, finding frontier models match human experts but break under structural perturbation due to calibration failures rather than capability gaps.