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...
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.
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: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:2608. 05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.
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.
We report operational experience full-fine-tuning a 32. 76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among the first published field accounts on this accelerator.
arXiv:2604. 11943v3 Announce Type: replace-cross Abstract: An OS kernel that runs LLM inference internally can read the model's own next-token logit distribution before any text is generated, and act on it as a governance primitive.
The paper demonstrates that Mixture-of-Experts (MoE) models can avoid the need for calibration data when selecting which expert channels to load during decoding. Instead of using a calibrated threshold, a top‑k selection strategy—choosing the k highest‑scoring channels per token—achieves comparable or better performance without calibration. Additionally, the authors propose a reader that uses a ternary sketch and low‑rank mapping to recover skipped channels, achieving 98.1% of full‑model accuracy while reading only 17.8% of expert weights and speeding up inference by up to 2.69× compared to optimized vLLM.
The paper introduces VLA-ULAP, a lightweight local action predictor that interleaves remote vision–language–action (VLA) calls with on‑edge inference. ULAP, with only 7.4 M parameters, predicts action chunks in a single pass using current views, proprioception, and action history, eliminating the need for VLA hidden states or server round‑trips. Experiments on Jetson Orin Nano and simulated benchmarks show that VLA-ULAP can remove 48.8–76.7 % of VLA calls while preserving 95–97.5 % of baseline success, and it outperforms local VLA‑acceleration alternatives in both inference time and energy consumption.
The paper introduces a selection‑safe conformal early‑exit method that calibrates a single split‑conformal quantile using a terminal‑path score derived from prefix maxima. This approach guarantees coverage for any data‑dependent stopping policy that preserves terminal‑set containment, without requiring an α/L correction. Empirical results on eight encoder backbones and CIFAR‑100 show that pathwise selection achieves higher macro coverage (0.901 vs. 0.814) with smaller set sizes and lower latency fractions compared to naive layer‑wise selection, and remains robust to deployment‑time budget changes.
arXiv:2606. 08635v1 Announce Type: new Abstract: Prefill-decode (PD) disaggregation decouples prompt processing from token generation, but it also turns the key-value (KV) cache into a network payload.