arXiv Computation and Language
VX-One: Typed, Calibrated Decision Heads for Real-Time Perimeter Triage
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
Hugging Face Trending Papers
Oct 21
VX-One: Typed, Calibrated Decision Heads for Real-Time Perimeter Triage - An empirical study of unknown-attack honesty, cascade gray zones, and calibration under drift
Perimeter triage faces a latency/semantics trade-off: signature engines are fast and format-safe but blind to unseen attacks, while generative models are semantic but slow and produce unparseable outp...
arXiv Machine Learning
Oct 22
VX-One: Typed, Calibrated Decision Heads for Real-Time Perimeter Triage - An empirical study of unknown-attack honesty, cascade gray zones, and calibration under drift
VX‑One is a small multi‑head neural network that outputs typed, calibrated decision primitives— a calibrated gate, a closed‑set choice with an explicit unknown option, and an ordinal score—trained in a multi‑task, temperature‑calibrated manner. In experiments on UNSW‑NB15, CIC‑IDS2017, and EMBER2024, VX‑One outperforms traditional models on detection metrics and achieves high recall when combined with a signature engine in a two‑phase cascade, though its calibration degrades under temporal drift and it struggles with multi‑class attribution. The study provides a detailed empirical assessment of where such typed, calibrated decisions aid perimeter triage and where they fall short.
arXiv AI
Sep 24
XLOG: A CUDA-Native Engine for Neurosymbolic Integration
XLOG is a CUDA‑native logic programming engine that fuses neural perception with deterministic Datalog, probabilistic inference, and epistemic world views. It offers multiple reasoning modes that share device data planes, with host‑orchestrated Datalog and exact inference, and resident recursive and Monte Carlo cores that avoid host‑device transfers. The system supports end‑to‑end gradients through GPU knowledge compilation, achieves significant speedups in MNIST‑addition training and join operations, and demonstrates competitive accuracy on several benchmark tasks.
arXiv AI
Oct 5
Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
arXiv:2610.02267v1 Announce Type: new Abstract: Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input...
arXiv Machine Learning
Aug 7
Operating Multi-Node Full Fine-Tuning on NVIDIA B300: A Field Report on Telemetry-Based Triage, Negative Results, and Operational Hardening
arXiv:2608. 05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.
arXiv Machine Learning
Jun 9
AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis
arXiv:2606. 09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA.