SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition

Recent advancements in 3D scene-language understanding have led to the development of the 3D Spatial Language Instruction Mask (3D-SLIM), which enhances the reasoning capabilities of Large Language Models (LLMs) by replacing traditional causal attention masks with adaptive attention masks tailored to the spatial structures of 3D scenes. This innovation addresses key limitations in current methodologies, such as sequential bias and restricted attention in task-specific reasoning.

A recent study evaluated the performance of Large Language Models (LLMs) in financial tabular classification tasks, revealing discrepancies between LLMs' self-explanations of feature importance and their SHAP values. This divergence raises concerns about the reliability of LLMs in high-stakes applications like financial risk assessment, where accuracy is critical.

AlignSAE introduces a novel approach to Sparse Autoencoders (SAEs) by aligning their features with a defined ontology through a structured training process. This method enhances the interpretability of hidden activations in Large Language Models (LLMs), allowing for better control and inspection of specific features without interference from unrelated data. Empirical results indicate that AlignSAE significantly improves the alignment of features with human-defined concepts.

Recent research introduces Semantically Equivalent and Coherent Attacks (SECA), a method designed to elicit hallucinations from Large Language Models (LLMs) through realistic prompt modifications that maintain semantic coherence. This approach addresses the limitations of previous adversarial attacks that often resulted in unrealistic prompts, thereby enhancing understanding of how hallucinations can occur in practical applications.