arXiv:2603.12488v1 Announce Type: new Abstract: In many robotic manipulation tasks, the robot repeatedly solves motion-planning problems that differ mainly in the location of the goal object and its associated obstacle, while the surrounding workspace remains fixed. Prior works have shown that leveraging experience and offline computation can accelerate repeated planning queries, but they lack guarantees of covering the continuous task space and require storing large libraries of solutions. In this work, we present COAD, a framework that provides […]

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This paper presents a generative AI frameworkfor producing structured symbolic sequences with fine-grainedexpressive control. The approach introduces a compact tokenrepresentation combined with phrase-aware latent alignment tosupport coherent generation across variable-length segments. Byintegrating sequence-level regularization directly into attention,the model balances structural consistency and diversity withoutrelying on explicit post-processing constraints. Empirical analysisshows that the method maintains stable distributional behavioracross expressive dimensions, highlighting its suitability forcontrollable symbolic generation tasks.

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hey! NeurIPS releases these yearly challenges called MyoChallenges, that focus on human musculoskeletal research using RL. This is the official playlist (by MyoSuite) which has an overview of what to expect: https://youtube.com/playlist?list=PLq492wGha2Iwi8B7OOg5muUmIaqTnSmu8&si=QgAmv9ZvdWc9_tip The challenge would be released around July and I wanted to create a team and learn as much from the past challenges as possible till then! Hit me up if you’re interested!! anyway, thanks! submitted by /u/snailinyourmailpart2 [link] [comments]

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arXiv:2510.12026v3 Announce Type: replace-cross Abstract: Mamba, a recently proposed linear-time sequence model, has attracted significant attention for its computational efficiency and strong empirical performance. However, a rigorous theoretical understanding of its underlying mechanisms remains limited. In this work, we provide a theoretical analysis of Mamba’s in-context learning (ICL) capability by focusing on tasks defined by low-dimensional nonlinear target functions. Specifically, we study in-context learning of a single-index model $y approx g_*(langle boldsymbol{beta}, boldsymbol{x} rangle)$, which depends on only […]

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arXiv:2602.02568v1 Announce Type: new Abstract: We introduce $textbf{Hierarchical Taylor Series-based Continual Learning (HTCL)}$, a framework that couples fast local adaptation with conservative, second-order global consolidation to address the high variance introduced by random task ordering. To address task-order effects, HTCL identifies the best intra-group task sequence and integrates the resulting local updates through a Hessian-regularized Taylor expansion, yielding a consolidation step with theoretical guarantees. The approach naturally extends to an $L$-level hierarchy, enabling multiscale knowledge integration in a […]

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To address the core shortcomings of existing memory reorganization algorithms in distributed change environments, such as trigger lag, performance fluctuations, and memory management imbalances, a Distribution-Aware Continuous Memory Reorganization (DAMCR) algorithm is proposed. This algorithm uses “distribution awareness – dynamic reorganization – performance feedback” as its core logic, employing a four-module architecture. It constructs a distribution change awareness factor by improving KL divergence, achieves adaptive memory unit management based on multi-dimensional value assessment, and combines a buffer mechanism […]

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arXiv:2601.15347v1 Announce Type: new Abstract: The rash development of knowledge graph research has brought big driving force to its application in many areas, including the medicine and healthcare domain. However, we have found that the application of some major information processing techniques on knowledge graph still lags behind. This defect includes the failure to make sufficient use of advanced logic reasoning, advanced artificial intelligence techniques, special-purpose programming languages, modern probabilistic and statistic theories et al. on knowledge graphs […]

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arXiv:2602.16826v1 Announce Type: new Abstract: Theory of mind (ToM) enables AI systems to infer agents’ hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We introduce HiVAE, a hierarchical variational architecture that scales ToM reasoning to realistic spatiotemporal domains. Inspired by the belief-desire-intention structure of human cognition, our three-level VAE hierarchy achieves substantial performance improvements on a 3,185-node campus navigation task. However, we identify a critical limitation: while our hierarchical structure […]

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arXiv:2601.09858v1 Announce Type: new Abstract: Scientific paper generation requires document-level planning and factual grounding, but current large language models, despite their strong local fluency, often fail in global structure, input coverage, and citation consistency. We present a reinforcement learning framework that casts scientific outline construction as a long-horizon planning problem over hierarchical document structures. Our approach models edit evolving outlines through structured actions, enabling the system to incrementally build a complete scientific manuscript. To support effective and stabilize […]

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