Large Language Models (LLMs) have become central to modern AI, with interpretability serving as a critical means of investigating the opaque and highly nonlinear mechanisms encoded within billions of parameters and ensuring trustworthy deployment. However, descriptive interpretability approaches for LLMs remain largely post-hoc, illuminating model behavior without providing the actionable leverage needed to influence or adapt model behavior, thereby limiting their practical utility. Recent work has therefore reframed interpretability as an actionable paradigm, shifting the focus from explanation alone toward methods that connect internal mechanisms to model refinement. This survey reviews LLM interpretability through the lens of actionability, presenting a taxonomy of attributional, concept-based, and mechanistic approaches, along with emerging methods tailored to vision–language models (VLMs). We further examine how interpretability supports downstream objectives such as hallucination mitigation, model editing, fairness, and safety. By positioning interpretability as a pathway to better-guided LLM design and practice, this survey outlines key challenges and future directions toward trustworthy and controllable foundation models.
With the growing adoption of artificial intelligence in high-stakes decision-making domains, identifying the causes of outcomes--particularly failures--and determining who is responsible has become a critical concern. In this work, we investigate how well formal definitions of responsibility attribution, grounded in the framework of actual causality, align with human judgments of responsibility. To this end, we conduct a large-scale survey to elicit human judgments of responsibility in multi-agent sequential decision-making scenarios, using a modified version of the card game Goofspiel. We evaluate different responsibility attribution methods, assessing their alignment with human judgments about responsibility, and identifying factors that significantly shape responsibility judgments. While no single responsibility attribution method consistently aligns with human responses, our findings highlight key factors that influence human responsibility judgments, including agent-specific biases and the amount of information available to agents during decision-making.
Retrieval-Augmented Generation (RAG) systems are widely adopted in question answering, yet they often fail to satisfy complex multi-constraint queries, leading to constraint violations, factual inconsistencies, or hallucinations. We present Structure-Driven RAG System for Multi-Constraint Queries(MC-RAG), a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph. By integrating semantic and structural embeddings with path-level indexing, MC-RAG performs interpretable, structure-aware, and constraint-consistent retrieval and generation. During the demonstration, participants can input medical or encyclopedic multi-constraint queries, visualize how the system parses constraints, performs structural matching, and generates answers, thereby experiencing an end-to-end, interactive, and explainable RAG pipeline. A demo video is available at https://youtu.be/J8kahzmAnu0.
Active feature acquisition (AFA) studies how a predictive system can sequentially choose which feature values to obtain for each instance to balance predictive accuracy against feature acquisition cost (financial, time, invasiveness, or privacy). This survey provides the first unified treatment of modern AFA through an explicit MDP and POMDP formulation, showing that most existing methods can be understood as different approximations of the same underlying sequential decision problem. The survey proposes an up-to-date taxonomy organizing AFA into three families: (i) embedded cost-aware predictors (notably cost-sensitive decision trees and ensembles), (ii) model-based methods that plan using learned probabilistic components, and (iii) model-free or hybrid methods that learn policies from simulated acquisition episodes. We hope this POMDP-centric view both clarifies existing work and motivates new AFA methods that more directly build on the mature literature on POMDP planning and approximation. It concludes by outlining open challenges for achieving robust cost–accuracy trade-offs in practice, including reliable evaluation under realistic missingness and logging, computational constraints, and deployment requirements such as robustness and interpretability.
In the field of skeleton-based human action recognition, Graph Convolutional Networks (GCNs) have become a dominant framework. However, existing GCN-based approaches often treat the sequences of two-person interaction as separate entities, ignoring the inherent semantic dependencies and spatial correlations between interacting subjects. Furthermore, high-order skeleton representations naturally exhibit non-Euclidean structures, where Euclidean deep learning models are inherently limited in explicitly capturing and preserving such geometric information. As a countermeasure, we propose a Riemannian Graph Convolutional Network (RGCN) that operates on the Symmetric Positive Definite (SPD) manifolds. Specifically, we model high-order skeletal statistics via Gaussian embedding and propose a Riemannian network to capture inter-subject interactions and global correlations. The proposed RGCN is instantiated under three SPD geometries, and its effectiveness is validated through extensive experiments on three interaction benchmarks. Extensive experimental results show that RGCN provides a competitive and geometrically grounded alternative for skeleton-based interaction recognition.
