An Energy Efficient Spiking Detection Layer for Flagging Hallucinated LLM Outputs: A Proof of Concept Study

Large language models are increasingly deployed with a second, separate model or classifier layered on top to catch hallucinated or unreliable outputs before they reach a user or downstream system. This paper presents a proof of concept that converts a trained dense classifier into a rate coded spiking neural network for flagging hallucinated statements, and measures the accuracy and energy trade-off against a dense baseline. The converted spiking classifier reduced a hardware inspired energy proxy by 94.9 percent at 25 timesteps while AUROC held at 0.870 versus 0.786 for the dense baseline, with accuracy dropping from 88.9 percent to 81.5 percent. Results are preliminary, drawn from a small hand built dataset rather than a full benchmark, and the paper is explicit about that scope along with a clear path to scale it up.