Runtime Governance for Production AI Agent Security
A look at the five-plane runtime governance architecture for controlling production AI agent actions and system changes.
Humanoids, autonomy, and embodied AI.
Hub content is updated incrementally.
A look at the five-plane runtime governance architecture for controlling production AI agent actions and system changes.
StatefulDiscovery reframes scientific agent evaluation around evidence-calibrated claims, not just plausible answers.
A practical guide to choosing subtitle-only or multimodal frame analysis for video summary apps, with tradeoffs in quality, cost, latency, and evaluation.
A curated link roundup from recently collected official updates and tech news.
A curated link roundup from recently collected official updates and tech news.
Why adaptive patching in time-series Transformers does not consistently outperform well-tuned uniform baselines.
A curated link roundup from recently collected official updates and tech news.
As AI adoption widens, high-risk capabilities and enterprise deployment diverge into distinct control and monetization layers.
A curated link roundup from recently collected official updates and tech news.
Examines why intervention timing, not just detection, is central to runtime safety in long-running autonomous agents.
A SAR FSCIL approach combining optical guidance and neural collapse to address data scarcity, forgetting, and azimuth sensitivity.
A look at post hoc instance-level bounding box uncertainty for autonomous driving detection and key deployment checks.
A look at probabilistic barrier-certificate verification for RL policies vulnerable to transition perturbations before deployment.
In enterprise document RAG, retrieval granularity often matters more than reasoning. Why structure-aware search helps.
Examines how AI maps discrete tokens into vectors and where continuous representations may fall short in reasoning.
A curated link roundup from recently collected official updates and tech news.
Examines when local AI PCs help with latency, cost, and privacy, and where cloud remains better for scale.
GTBench uses 63 graph theory problems to assess LLMs beyond answer accuracy, focusing on reasoning and proof skills.
A look at using self-improving LLM agents and Pareto evolution to balance risk and realism in driving safety tests.
MUSE asks whether structured execution harnesses can improve multimodal reasoning without retraining the model.
Examines signs that AI infrastructure is shifting from expansion to maintenance, refresh, and upgrade cycles.
A look at StepFinder and why root-cause step attribution matters for cascading failures in LLM multi-agent systems.
Examines a proposed Constitutional AI verification framework for autonomous AI in orbit, with focus on limits and evidence.
Comparing ambient AI clinical drafts with physician-final notes highlights how stigmatizing language may change through editing.