Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-world tasks typically require optimizing multiple objectives simultaneously, necessitating precise tuning of their weights to learn a policy with the desired characteristics. To address this, we propose a two-stage reward curriculum where we decouple task-specific objectives from behavioral terms. In our method, we first train the agent on a simplified task-only reward function to […]

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arXiv:2602.05026v1 Announce Type: new Abstract: We formulate the fundamental laws governing learning dynamics, namely the conservation law and the decrease of total entropy. Within this framework, we introduce an entropy-based lifelong ensemble learning method. We evaluate its effectiveness by constructing an immunization mechanism to defend against transfer-based adversarial attacks on the CIFAR-10 dataset. Compared with a naive ensemble formed by simply averaging models specialized on clean and adversarial samples, the resulting logifold achieves higher accuracy in most test […]

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arXiv:2603.00602v1 Announce Type: new Abstract: Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data, resulting in incomplete feature space characterization and weak decision boundaries. Although synthesizing outliers offers a promising solution, existing approaches rely on fixed, non-adaptive sampling heuristics (e.g., distance- or density-based), limiting their ability to explore informative OOD regions. We propose a Policy-Guided Outlier Synthesis […]

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With Google Opal AI, an experimental tool that assists users in making light “mini-apps” by means of a visual, AI-assisted workflow, Google has quietly made its entrance into the expanding universe of AI-powered app development. Opal places itself in the middle of rush coding, no-code platforms, and AI app creators, growing as a fast and user-friendly way to prototype functioning micro-apps without needing a lot of engineering.  Google Opal AI does not compete with full-stack app builders directly. […]

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I recently started doing software development work to expand and integrate AI systems. Here are some of the biggest surprises I found along the way. AI Developers Love Calling Everything “Fine Tuning” When studying for the AI-102 exam, I thought fine tuning strictly referred to additional training of a model to include additional data (which can be fed into the system in different ways). When actually working on an AI project, what I learned was developers (and architects) sprinkle […]

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Forecasters often choose retraining schedules by convention rather than by an explicit decision rule. This paper gives that decision a posterior-space language. We define learning debt as the divergence between the deployed and continuously updated posteriors, define actionable staleness as the policy-relevant latent state, and derive a one-step Bayes retraining rule under an excess-loss formulation. In an online conjugate simulation using the exact Kullback-Leibler divergence between deployed and shadow normal-inverse-gamma posteriors, a debt-filter beats a default 10-period calendar […]

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arXiv:2602.23464v1 Announce Type: new Abstract: Multi-scalar multiplication (MSM), defined as MSM(P, x) = sum_{i=1}^n x_i P_i, is a dominant computational kernel in discrete-logarithm-based cryptography and often becomes a bottleneck for verifiers and other resource-constrained clients. We present 2G2T, a simple protocol for verifiably outsourcing MSM to an untrusted server. After a one-time keyed setup for fixed bases P = (P1, …, Pn) that produces a public merged-bases vector T and client secret state, the server answers each query […]

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arXiv:2603.22404v1 Announce Type: new Abstract: Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem instances and are willing to pay up to a budget for a verifiable solution. An arbitrageur efficiently allocates inference budget across providers to undercut the market, thus creating a competitive offering with no model-development risk. In this work, we initiate the study of arbitrage in AI model markets, empirically demonstrating the viability of […]

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