Offline safe reinforcement learning (RL) is increasingly important for cyber-physical systems (CPS), where safety violations during training are unacceptable and only pre-collected data are available. Existing offline safe RL methods typically balance reward-safety tradeoffs through constraint relaxation or joint optimization, but they often lack structural mechanisms to prevent safety drift. We propose LexiSafe, a lexicographic offline RL framework designed to preserve safety-aligned behavior. We first develop LexiSafe-SC, a single-cost formulation for standard offline safe RL, and derive safety-violation […]

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arXiv:2603.22315v1 Announce Type: new Abstract: Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer (DT). By casting corridor optimization as offline, return-conditioned sequence modeling, our approach (1) eliminates online environment interaction during policy learning, (2) enables dispatch-level urgency control through a single target-return scalar, and (3) extends to multi-agent settings via a […]

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I assumed the flicker was SAM3 being temperamental. I watched masks snap on and off across consecutive frames, and my first instinct was to reach for the usual bag of excuses: “it’s a hard scene,” “video is noisy,” “the model needs more context.” What actually broke was simpler and more embarrassing: I was letting the model’s outputs live in the wrong coordinate system, and then I tried to stabilize a tracker on top of that. Once I fixed […]

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We present and develop local prospect theory (LPT), a novel framework for understanding consciousness, and, in particular, subjective experience and free will. While predictive processing (PP) theories model the brain as trying to optimize the accuracy of predictions, LPT sees uncertainty as an essential feature of conscious decision-making. This is achieved by creating a “local prospect”—a range of potential developments colored by subjective experience from which an agent can freely choose how to react. Drawing on global workspace […]

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Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical numerical methods and machine learning approaches, through a unified evaluative framework organized around six fundamental computational challenges. Classical methods are assessed for their structure-preserving properties, rigorous convergence theory, and scalable solver design; their persistent limitations in high-dimensional […]

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Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially the dynamic structural modality. Existing dynamic learning methods are often limited to shallow structures across heterogeneous spaces or simple unispaces, making it difficult to capture deep relation-aware geometric features. 2) At the […]

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arXiv:2602.06069v1 Announce Type: new Abstract: The escalating demand for high-fidelity, real-time inference in distributed edge-cloud environments necessitates aggressive model optimization to counteract severe latency and energy constraints. This paper introduces the Hybrid Quantization and Pruning (HQP) framework, a novel, integrated methodology designed to achieve synergistic model acceleration while adhering to strict quality guarantees. We detail a sensitivity-aware structural pruning algorithm that employs a dynamic weight sensitivity metric, derived from a highly efficient approximation of the Fisher Information Matrix […]

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As AI floods software development with code, Qodo is betting the real challenge is making sure it actually works.

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Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of its participants. Existing client evaluation methods predominantly focus on model performance, such as accuracy or loss, which represents only one dimension of a machine learning model’s overall utility. In contrast, this work investigates the critical, yet overlooked, issue of client contributions towards a model’s trustworthiness — specifically, its reliability (tolerance to noisy data), resilience (resistance to adversarial examples), […]

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TL;DR: We ran a factorial study on policy conditioning (appending a “goal” signal to observations). We found that while it barely improves “tracking precision,” it leads to a 23x improvement in tail-risk (CVaR). Crucially, we prove that temporal correlation—not just having the extra data—is the causal driver. The Problem: The “Black Box” of Conditioning In RL, we often append a task descriptor (goal, context vector, or latent) to the agent’s observation. We assume it helps the agent adapt. […]

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