arXiv:2504.15243v2 Announce Type: replace-cross Abstract: Constrained optimization with multiple functional inequality constraints has significant applications in machine learning. This paper examines a crucial subset of such problems where both the objective and constraint functions are weakly convex. Existing methods often face limitations, including slow convergence rates or reliance on double-loop algorithmic designs. To overcome these challenges, we introduce a novel single-loop penalty-based stochastic algorithm. Following the classical exact penalty method, our approach employs a {bf hinge-based penalty}, which […]

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arXiv:2602.03900v1 Announce Type: new Abstract: Large Language Models (LLM) can struggle with reasoning ability and planning tasks. Many prompting techniques have been developed to assist with LLM reasoning, notably Chain-of-Thought (CoT); however, these techniques, too, have come under scrutiny as LLMs’ ability to reason at all has come into question. Borrowing from the domain of cognitive and educational science, this paper investigates whether the Task-Method-Knowledge (TMK) framework can improve LLM reasoning capabilities beyond its previously demonstrated success in […]

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Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distribution. This limitation is especially important in contact-rich tasks such as pushing and pick-and-place, where changes in goals, contact conditions, or robot dynamics can drive the system out-of-distribution at inference time. In this paper, we investigate a hybrid controller that combines reinforcement learning with bounded extremum seeking to improve robustness under such conditions. In the […]

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How CMOs win CFO buy-in is no longer just a leadership challenge. It is a financial one that directly impacts budgets, capital allocation, and revenue durability. In today’s capital-constrained markets, CFOs evaluate whether marketing is discretionary spend or a disciplined investment that drives measurable economic return. Many marketing teams still report in impressions and engagement, while finance leaders focus on contribution margin, EBITDA, and cash flow timing. Bridging this measurement gap from activity metrics to financial outcomes, including […]

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Offline meta-reinforcement learning (OMRL) combines the strengths of learning from diverse datasets in offline RL with the adaptability to new tasks of meta-RL, promising safe and efficient knowledge acquisition by RL agents. However, OMRL still suffers extrapolation errors due to out-of-distribution (OOD) actions, compromised by broad task distributions and Markov Decision Process (MDP) ambiguity in meta-RL setups. Existing research indicates that the generalization of the $Q$ network affects the extrapolation error in offline RL. This paper investigates this […]

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arXiv:2603.23607v1 Announce Type: new Abstract: In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end driving that focuses on long-tail driving events. We provide multi-view video data, trajectories, high-level instructions, and detailed reasoning traces, facilitating in-context learning and few-shot generalization. The resulting benchmark for multimodal models, such as VLMs and VLAs, goes beyond safety and comfort metrics by evaluating instruction following and semantic […]

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arXiv:2603.28831v1 Announce Type: new Abstract: Combining different types of agents in uncrewed vehicle (UV) swarms has emerged as an approach to enhance mission resilience and operational capabilities across a wide range of applications. This study offers a systematic framework for grouping different types of swarms based on three main factors: agent nature (behavior and function), hardware structure (physical configuration and sensing capabilities), and operational space (domain of operation). A literature review indicates that strategic heterogeneity significantly improves swarm […]

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ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.

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