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.
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:2610.30090v2 Announce Type: replace-cross Abstract: Perimeter triage faces a latency/semantics trade-off: signature engines are fast and format-safe but blind to unseen attacks, while generativ...
The paper evaluates System One decision models—typed models that output probabilities for branching decisions—against supervised classifiers and generative language models on automated decision gate tasks. Eight checkpoints from six families, including the hosted model Jev, were benchmarked on workflow, intent, and social‑science items, showing that small trained classifiers match or slightly outperform decision models on intent and workflow when labels are available, while decision models outperform zero‑shot classifiers when labels are absent. The study also explores calibration, risk thresholds, and cost‑efficiency trade‑offs, providing condition‑dependent design guidelines for automated decision gates.
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. 15565v1 Announce Type: new Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide.
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.
arXiv:2610. 29052v1 Announce Type: cross Abstract: A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its hidden states, instead of generating and parsing text.
This thesis introduces methods for assessing and enhancing the robustness of large language models (LLMs) against adversarial input variations. It proposes a generative robustness metric, R_stab(f), based on Jensen‑Shannon divergence, and proves bounds for localized attacks. The work also presents adaptive attacks, Trojan detection techniques, defense strategies for multi‑layer systems, and lightweight attestation for Model Context Protocol (MCP) agentic systems, all implemented in the JudgeGuard, TrojanArmor, and MCPSec suites.
arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.
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.
SiLR introduces a structure‑preserving admission and process reward mechanism for large language model (LLM) tool agents. Unlike traditional scalar‑score gates that can trap agents in plateau trajectories, SiLR shadow‑executes each proposal and admits it based on a product order over branch‑level violation states, ensuring safe and recoverable actions. Experiments on Gym‑ANM and CityLearn benchmarks show SiLR consistently recovers all multi‑action episodes and outperforms scalar gates, while also providing a robust reward signal for policy learning.
arXiv:2606. 16999v1 Announce Type: cross Abstract: Frozen small code models ( =45.