Tag
Proposes a Multi-Granularity Reasoning Network (MGRN) that explicitly leverages hierarchical semantic features for natural language inference, outperforming strong baselines on multiple benchmarks.
We propose hierarchical RBF-KAN and RBF-SKAN architectures for multidimensional function approximation and random field learning. The frameworks offer universal approximation properties and partially alleviate the curse of dimensionality, with empirical results showing improved accuracy over existing methods.
CHAM-net introduces a contrastive hierarchical adaptive meta-network that captures site-specific and cross-year dynamics for robust global methane flux prediction, outperforming baseline methods on simulation and observational datasets.
This paper presents CosmicFish-HRM, a compact 82.77M parameter language model with a hierarchical reasoning module that dynamically allocates reasoning compute during inference, learning when to halt based on input complexity.
Introduces BOHM, a zero-cost hierarchical attribution method for compound AI systems that extracts attribution from routing weights, outperforming Shapley-based methods in many real-world deployments.
Maestro is a reinforcement learning-driven framework that dynamically composes ensembles of frozen expert models and skills for multimodal tasks, achieving 70.1% average accuracy with a 4B orchestrator, surpassing GPT-5 and Gemini-2.5-Pro.
Introduced NOML, a custom reinforcement learning algorithm for continuous flight control that uses a hierarchical actor, anchor policy, and mirror learning to prevent oscillation and improve stability. The code is open-sourced on GitHub.
Lighthouse Attention is a selection-based hierarchical attention mechanism that accelerates long-context pretraining by running forward+backward passes ~17× faster at 512K context and delivering 1.4–1.7× end-to-end speedup at 98K context, validated with Llama-3 530M on 50B tokens.