ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems

arXiv:2604.11943v2 Announce Type: replace-cross Abstract: An OS kernel that runs LLM inference internally can read logit distributions before any text is generated and act on them as a governance primitive. This paper presents ProbeLogits, a kernel-level operation that performs a single forward pass and reads specific token logits to classify agent actions as safe or dangerous, with zero learned parameters. I evaluate ProbeLogits across three base models (Qwen 2.5-7B, Llama 3 8B, Mistral 7B) on three external benchmarks: HarmBench, XSTest, and ToxicChat. On HarmBench non-copyright (n=300), all three models reach 97-99% block rate with the right verbalizer. On ToxicChat (n=1,000), ProbeLogits achieves F1 parity-or-better against Llama Guard 3 in the same hosted environment: the strongest configuration (Qwen 2.5-7B Safe/Dangerous, alpha=0.0) reaches F1=0.812 with bootstrap 95% CIs disjoint from LG3 (+13.7pp significant); Llama 3 S/D matches LG3 within CI (+0.4pp, parity); Mistral Y/N exceeds by +4.4pp. Latency is approximately 2.5x faster than LG3 in the same hosted environment because the primitive reads a single logit position instead of generating tokens; in the bare-metal native runtime ProbeLogits drops to 65 ms. A key design contribution is the calibration strength alpha, which serves as a deployment-time policy knob rather than a learned hyperparameter. Contextual calibration corrects verbalizer prior asymmetry, with bias magnitude varying by (model, verbalizer) pair. I implement ProbeLogits within Anima OS, a bare-metal x86_64 OS written in approximately 86,000 lines of Rust. Because agent actions must pass through 15 kernel-mediated host functions, ProbeLogits enforcement operates below the WASM sandbox boundary, making it significantly harder to circumvent than application-layer classifiers.