Many large-scale platforms and networked control systems have a centralized decision maker interacting with a massive population of agents under strict observability constraints. Motivated by such applications, we study a cooperative Markov game with a global agent and $n$ homogeneous local agents in a communication-constrained regime, where the global agent only observes a subset of $k$ local agent states per time step. We propose an alternating learning framework $(texttt{ALTERNATING-MARL})$, where the global agent performs subsampled mean-field $Q$-learning against […]

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Why 2026 feels different In 2026, low code and no code are not side tools anymore. They are becoming a default path to ship internal workflows (Gartner’s low code forecast), portals, dashboards, and even customer experiences, because the pressure to deliver faster never went away. I see many teams adopt a platform quickly, build a few apps, and then run into the same problems: messy ownership, risky data access, inconsistent quality, and a painful scaling story. This guide […]

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arXiv:2602.01018v1 Announce Type: new Abstract: Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills from long-horizon, multi-task offline data, especially when the data lacks explicit rewards or subtask annotations. In this work, we introduce LOKI, a three-stage end-to-end learning framework designed for offline skill discovery and hierarchical imitation. The framework commences with a two-stage, weakly supervised skill discovery process: Stage one performs coarse, task-aware […]

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arXiv:2602.09039v1 Announce Type: new Abstract: Prescriptive process monitoring seeks to recommend actions that improve process outcomes by analyzing possible continuations of ongoing cases. A key obstacle is the heavy computational cost of large-scale suffix comparisons, which grows rapidly with log size. We propose an efficient retrieval method exploiting the triangle inequality: distances to a set of optimized pivots define bounds that prune redundant comparisons. This substantially reduces runtime and is fully parallelizable. Crucially, pruning is exact: the retrieved […]

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Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in this field focus on the application of diffusion policies while leaving the diffusion critics unexplored. In fact, since policy optimization fundamentally relies on the critic, accurate value estimation is far more important than policy expressiveness. Furthermore, given the stochasticity of most reinforcement learning tasks, it has been confirmed that the critic is more appropriately […]

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In conversations about customer data, one question comes up again and again: if both CDPs and MDMs help create a more complete view of the customer, are they basically doing the same thing? It is an understandable question. After all, both technologies are often positioned around customer unification, identity resolution, and creating better visibility across systems. On the surface, they can sound very similar. But while CDPs and MDMs do overlap in some areas, they are not the […]

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arXiv:2602.00959v1 Announce Type: new Abstract: Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundaries extend. Existing benchmarks are mostly static and provide limited support for systematic knowledge probing. In this paper, we propose an interactive agentic framework to systematically extract and quantify the knowledge of LLMs. Our method includes four adaptive exploration policies to probe knowledge at different granularities. To ensure […]

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