explainability

This paper proves that across a broad class of ANNs, inference logic can be reformulated as sparse symbolic interactions, supported by mathematical criteria and extensive experiments, offering novel insights into explainability and generalization.

This paper proposes using signed, fusion-aware Integrated Gradients for attributing predictions in feature-tokenized transformers like BiomeGPT, overcoming limitations of CLS attention weights and revealing disease-supporting versus protective microbial signals.

Semantica is an open-source tool that turns any data into a Context Graph, logging every AI agent decision with its full causal chain and W3C PROV-O provenance, enabling deterministic explainability for finance, healthcare, and government.

This paper introduces THBKG, a temporal heterogeneous biomedical knowledge graph covering 110k entities and 11.1M edges with yearly evidence timestamps, designed to predict whether target-disease pairs entering Phase II trials advance to Phase III using only evidence available at the decision time. The graph-based approach outperforms direct-evidence baselines, especially for pairs lacking direct evidence, and the authors release it as a continually updated resource.

This paper presents a comparative evaluation of post-hoc analysis methods (Random Forest surrogate, LIME, PCA) for detecting structured patterns in clustering results, using synthetic datasets with injected patterns. It finds that none of the methods consistently detects all pattern types, highlighting a gap in existing explainability tools.

This paper proposes an explainable LLM agent layer placed downstream of an open-world learning pipeline for oil well anomaly detection, using the Qwen3.5-397B-A17B model to provide natural-language justifications and novelty naming on the 3W dataset.

GraphArc is an open-source tool that visualizes AI agent workflows as interactive, real-time graphs, enabling users to inspect, debug, and approve agent actions before execution to make agentic AI more explainable and controllable.

FADEx introduces a novel local per-instance feature attribution method for explaining dimensionality reduction techniques, using Taylor expansion and Singular Value Decomposition to provide model-agnostic explanations and distortion analysis.

This paper introduces CoT-Mediate, a behavioral framework to test whether chain-of-thought reasoning in medical vision-language models actually drives predictions or merely decorates them. Auditing LLaVA-Med and MedGemma on VQA-RAD, it finds that how reasoning is injected (prefix-forcing vs re-prompting) and the attributed source (self vs expert) significantly affect model faithfulness and sycophancy.

This paper presents TraceCoder, a code generation system that records and visualizes the repair history of AI-generated code at snippet granularity, enabling explainable and auditable auditing of LLM-based coding agents.

This paper identifies a fundamental issue in top-k explanations for graph neural networks: automorphisms in input graphs cause non-unique explanations, as the model cannot distinguish symmetric elements. The authors provide a criterion to detect such arbitrariness and verify it using automated reasoning in Lean 4, showing the problem is widespread in molecular datasets.

TanML is an MIT-licensed automated model-validation toolkit for tabular machine-learning models, designed for regulated environments. The developers seek feedback on its features and reports.

This paper proves that existing marginal influence-based attribution methods fundamentally fail to capture the conditional dependency structure of time series models, and proposes DAG-faithfulness as a new criterion for faithful explanations.

This paper proposes a unified black-box robustness evaluation framework for token-level explanations in enterprise NLP, comparing encoder (BERT, RoBERTa) and decoder (Qwen, Llama) models. It finds decoder LLMs produce substantially more stable explanations, with stability improving with scale, and provides a cost-robustness tradeoff curve for pre-deployment model selection.

This position paper argues that LLM self-explanations can be plausible, questionably faithful, but highly actionable, and proposes evaluation guidelines beyond traditional metrics.

This paper proposes a Context-Augmented Prompting framework that uses a GNN expert model to provide predictive hints and explanatory subgraphs to improve molecular property prediction in small language models. Experiments on MUTAG and Tox21 show accuracy gains of up to 74% over SMILES-only baselines.

This paper introduces Evidence-Backed Video Question Answering (E-VQA), a new task requiring models to output both semantic answers and precise spatio-temporal evidence like tracked object segmentation masklets. The authors create a human-verified benchmark and a scalable training dataset, showing significant improvements over baselines.

This paper introduces MemExplainer, a method to explain predictions of Temporal Graph Networks (TGNs) by attributing contributions through topology attribution trees and memory backtracking trees, using Layer-wise Relevance Propagation (LRP) for faithful explanations.

This article provides a systematic and comprehensive overview of AI explainability, covering its needs (debugging, compliance, safety), classic methods, and cutting-edge challenges, emphasizing that faithful explanations are more important than plausible ones.

A comprehensive survey examining the dual-use risks and benefits of LLMs and generative AI in cybersecurity, covering AI-generated malware, defensive strategies, and explainability, with case studies from major platforms.