taxonomy

This paper proposes ActionRating, a formulation that places clarification inside an agent's action space on a shared ordinal scale with navigation, enabling two information-seeking modes (mandatory and opportunistic). On hierarchical taxonomy classification benchmarks, experiments with 9 LLMs show that opportunistic clarification improves accuracy and information-seeking effectiveness.

Introduces ArabiGEE, the first comprehensive Arabic grammatical error explanation taxonomy with a hierarchical structure spanning orthographic, morphological, syntactic, and lexical dimensions, comprising 27 error types, 140 correction types, and 324 explanations.

This survey reviews the use of large language models for graph computation, categorizing them into two paradigms: LLMs as executors and LLMs as planners. It finds LLMs promising for simple tasks but unreliable for large-scale exact computations, and suggests future directions.

This paper presents a comprehensive taxonomy of 3D vision research, covering geometric representations, datasets, learning paradigms, and applications in reconstruction, generation, and video modeling.

This paper presents a comprehensive 33-class taxonomy of recurrent distortion patterns (heuristic parasites) in LLM outputs, along with operational definitions, recognition criteria, and a reproducible measurement protocol (PPE) for quantifying behavioral degradation across conversations.

This paper presents A2X, an LLM-native pipeline that recursively constructs and searches hierarchical service taxonomies to overcome the limited effective context window of LLMs for service discovery in the Internet of Agents. It significantly improves retrieval accuracy and reduces token consumption compared to full-context and embedding-based baselines.

This paper presents a comprehensive survey and taxonomy of federated learning over human-body communication for on-body edge intelligence, including a scheduling vignette called BODYFED-HBC.

This learning note introduces the concept of an agent harness as the infrastructure layer around an LLM, proposing the ETCLOVG taxonomy (Execution, Tooling, Context, Lifecycle, Observability, Verification, Governance) and demonstrating its application through a coding agent case study.

GrandGuard introduces a comprehensive taxonomy, benchmark, and safeguards for elderly-specific risks in LLM chatbot interactions, finding that leading LLMs mishandle over 50% of such risks and proposing two safeguards achieving up to 96.2% detection accuracy.

This paper introduces AgentAtlas, a framework that goes beyond outcome-only leaderboards for LLM agents by proposing a six-state control-decision taxonomy and a nine-category trajectory-failure taxonomy to evaluate agent behavior more comprehensively.

This paper proposes a three-level taxonomy for evaluating AI cultural capabilities—Cultural Awareness, Sensitivity, and Competence—grounded in intercultural communication theory, aiming to improve validity and interpretability of AI evaluations in multicultural settings.

This position paper argues that interactive AI evaluation should be treated as a design science paradigm, proposing a two-axis taxonomy and reporting standards for assessing dynamic system behavior through trajectories.

This paper proposes a two-dimensional classification framework for AI agent design patterns that combines cognitive function and execution topology axes, identifying 27 named patterns and deriving empirical laws from cross-domain analysis.

This paper presents a comprehensive analysis of the Neural Tangent Generalization Attack (NTGA) for data protection, including a taxonomy of related attacks, and discusses future research directions.