Your Computer System Just Became Your Universal AI Agent Toolkit For Everything! :::tip What if you could talk to your computer the way you talk to a colleague — and it just did the work? ::: Not autocomplete. Not a chatbot that pastes code snippets. Not a suggestion engine that highlights the next three words. A system that takes your intent and orchestrates your entire machine — files, browser, terminal, internet — to deliver the result. That is exactly […]

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PICARD: Data, shields up DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It’s not precaution—it’s strategy. [camera shakes] WORF: HULL BREACHES ON NINE DECKS DATA: Here’s what happened: you told me to raise shields, and I didn’t — Kyle Ferrana, @KyleTrainEmoji Tags: ai-misuse, coding-agents, ai, llms

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arXiv:2604.18595v1 Announce Type: new Abstract: To support mission-critical services in emergency scenarios, wireless networks are required to provide stringent guarantees under massive Ultra-Reliable Low-Latency Communications (mURLLC) constraints. Distributed unmanned aerial vehicle (UAV)-based massive multiple-input multiple-output (MIMO) architectures have recently emerged as a promising solution for rapidly deployable emergency communication systems. However, how to fundamentally characterize and guarantee statistical quality-of-service (QoS) for such systems in the finite blocklength regime remains largely unexplored. To overcome these challenges, in this paper […]

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As engineers, we’re taught to design for reliability. We do design calculations, run simulations, build and test prototypes, and even then we recognize that these are imperfect, so we include safety factors. When it comes to the Industrial Internet of Things (IIoT) though, we rarely give the same level of scrutiny to the components that we rely on. What if we treated our IIoT database the same way we treated the physical things we produce? We build and […]

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We study the problem of learning the law of linear stochastic partial differential equations (SPDEs) with additive Gaussian forcing from spatiotemporal observations. Most existing deep learning approaches either assume access to the driving noise or initial condition, or rely on deterministic surrogate models that fail to capture intrinsic stochasticity. We propose a structured latent-variable formulation that requires only observations of solution realizations and learns the underlying randomly forced dynamics. Our approach combines a spectral Galerkin projection with a […]

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arXiv:2604.08721v1 Announce Type: new Abstract: This paper studies stabilization and its corresponding closed-loop region-of-attraction (ROA) for homogeneous polynomial dynamical systems whose nonlinear term admits an orthogonally decomposable (ODECO) tensor representation. While recent tensor-based results provide explicit solutions and sharp global characterizations for open-loop ODECO systems, closed-loop synthesis and computable ROA estimates are still often dominated by local linearization or Lyapunov/SOS (sum of squares) methods, which can be conservative and computationally demanding. We propose a structure-preserving linear feedback design […]

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Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can cheaply and rapidly adapt to task-specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being […]

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arXiv:2602.06107v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models (LLMs) remains expensive, particularly because the rollout is expensive. Decoupling rollout generation from policy optimization (e.g., leveraging a more efficient model to rollout) could enable substantial efficiency gains, yet doing so introduces a severe distribution mismatch that destabilizes learning. We propose Jackpot, a framework that leverages Optimal Budget Rejection Sampling (OBRS) to directly reduce the discrepancy between the rollout model and the evolving policy. Jackpot integrates […]

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arXiv:2601.17905v1 Announce Type: cross Abstract: Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing novel classes with limited data. We focus on the challenging FSCIL setup where a model receives only a single sample (1-shot) for each novel class and no further training or model alterations are allowed after the base training phase. This makes generalization to novel classes particularly […]

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