This paper proposes an LLM-enhanced framework for detecting malicious Python packages using a hierarchical heterogeneous graph representation learning approach.

This paper proposes Hierarchical Block-Local Learning (HBLL), a framework for training deep neural networks without full end-to-end backpropagation, achieving $\mathcal{O}(\log N)$ parallel time compl…

The paper proposes a Hierarchical Reinforcement Learning framework with two levels for handling high-level strategic planning and low-level continuous-control using Soft Actor-Critic and entropy-regul…

This paper proposes HDR (Hierarchical Denoising for Visual Reasoning), a framework that integrates hierarchical latents into causal video generation for multi-step reasoning, enabling coarse-to-fine r…

The paper introduces TaskWeave, a hierarchical agentic framework that successfully simulates long-horizon organizational dynamics by treating coordination as a memory-centered problem, demonstrating t…

This paper organizes embodied data sources for multimodal foundation models into a pyramid, focusing on real-robot, UMI-style, egocentric and exocentric, simulation, and general vision-language data.

The paper proposes a generic memory framework for advanced static program analysis, supporting various memory behaviors from different programming languages using Abstract Interpretation theory.

This paper proposes a method for hierarchically parsing long-form audio data into order-consistent Act-Sub-Event parse trees using Hierarchical Activity Grammar.

The paper introduces Semantic Triplet Restoration (STR), a novel protocol that converts complex table structures into atomic semantic triplets, improving table question answering by providing explicit…

This paper unifies the fragmented field of Tree-of-Thoughts (ToT) reasoning by mapping LLM-based search processes onto a formal taxonomy derived from classical heuristic search theory.

This paper introduces MCTS-Guided Group Relative Policy Optimization (M-GRPO) to enhance LLM spatial reasoning by improving the decomposition of complex tasks into optimal sub-tasks.

The paper introduces Hierarchical Adaptive Budgeter (HAB), a framework that improves LLM reasoning efficiency by adaptively allocating computational resources to match the intrinsic complexity of both…

The paper introduces Abstract Worlds Semantics (AWS), a set-theoretic framework that treats worlds as primitive elements to provide a unified and generalized analysis of various belief change models.

This paper generalizes the Phoa principle in synthetic domain theory to transfinite cases, introduces sobriomorphisms, and proposes a hypothetical completeness theorem.

This paper studies AI development frameworks for software engineering and proposes a six-dimension process taxonomy.

The paper assembles a corpus of 102 papers on edge bundling and derives a task taxonomy for reasoning about bundled visualizations.

The paper introduces a generalized, operator-based mathematical framework to explicitly model and quantify structural discrepancies (conflicts) between raw and contextual data, treating conflict as an…

Researchers introduce a multi-layer taxonomy of capabilities for large language models based on human cognitive science, mapping 15,934 LLM-focused papers and demonstrating operational utility.

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

This paper introduces StructureClaw, an artifact-centered workbench for evaluating structural-engineering agents, and presents StructureClaw-Bench, an executable benchmark for testing these agents.