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Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
arXiv:2606.04037v2 Announce Type: replace Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production. We present an ontology-grounded verification framework -- to our knowledge the first to combine three...
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Announce Type: replace Abstract: Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of...
Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production. We propose an ontology-grounded verification framework combining three components: an Agent Operational Envelope formalizing the...
Compact LLM Deployment and World Model Assisted Offloading in Mobile Edge Computing
Announce Type: replace Abstract: This paper investigates compact large language model (LLM) deployment and world-model-assisted inference offloading in mobile edge computing (MEC) networks. We first propose an edge compact LLM deployment (ECLD) framework that jointly applies structured pruning, low-bit quantization, and knowledge distillation to construct edge-deployable LLM variants, and we evaluate these models using four complementary metrics: accessibility, energy consumption,...
India deploys 12 nuclear warheads for first time; where do China and Russia stand?
India deploys 12 nuclear warheads for first time, up from none last year; where do China and Russia stand? NEW DELHI: India has deployed 12 nuclear warheads in peacetime for the first time, signalling a significant shift in its nuclear posture, according to the latest assessment by the Stockholm International Peace Research Institute (SIPRI).In its Yearbook 2026 released on Monday, SIPRI reported that India now possesses a total nuclear arsenal of 190 warheads, up from 180 a year earlier. Of...
Models Know Their Shortcuts: Deployment-Time Shortcut Mitigation
arXiv:2604.12277v2 Announce Type: replace Abstract: Pretrained text encoders are prone to shortcut learning, relying on token-label correlations that fail once the distribution shifts in deployment. Existing shortcut mitigation methods mainly operate at training time and assume access to training data, training dynamics, or shortcut annotations, which are hardly available during deployment, where only the converged model remains. We show that this model alone suffices to mitigate shortcuts...
India's nuclear stance may be changing as warheads move closer to deployment: SIPRI
A reputed global peace think tank has made the big claim that India may have shifted its nuclear posture to a ready-to-strike mode by deploying 12 warheads on delivery vehicles, including possibly one nuclear submarine. This marks a big change because India had so far kept warheads and delivery systems separately. When a nuclear warhead is ‘deployed’, it means the nuclear warhead is actively mounted on missiles or loaded onto bombers and, having been mated, is ready for immediate use.
FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
Announce Type: replace Abstract: The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch prohibitively expensive and deployment increasingly costly. These models are often used as computational monoliths with fixed cost, hindering adaptive deployment across different cost budgets. We argue that nested components, ordered by importance, can be extracted from pretrained models and selectively activated...
Taiwan deploys vessels in response to Chinese operation
Taiwan deploys vessels in response to Chinese operation June 7, 2026Taiwan's coast guard said on Sunday it had deployed vessels in response to a Chinese operation east of the island. Taipei said it detected four Chinese government vessels departing from Xiamen and dispatched more than five coast guard ships for surveillance. National Security Council Secretary-General Joseph Wu posted a video on X where the coast guard is broadcasting to Chinese vessels: "This is not your waters.
FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
Announce Type: replace Abstract: The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch prohibitively expensive and deployment increasingly costly. These models are often used as computational monoliths with fixed cost, hindering adaptive deployment across different cost budgets. We argue that nested components, ordered by importance, can be extracted from pretrained models and selectively activated...