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Tag

#explainability

8 articles tagged #explainability

arxiv6d ago

Binary Spiking Neural Networks as Causal Models

arXiv:2604.27007v2 Announce Type: replace Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking activity as a binary causal model. Thanks to this causal representation, we are able to explain th

BI1 model#explainability#neural-networks#causal-analysisRead on arxiv →
arxivMay 8bullish

Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions

arXiv:2605.06058v1 Announce Type: new Abstract: Document Visual Question Answering (DocVQA) requires vision-language models to reason not only about what information in a document is relevant to a question, but also where the answer is grounded on the page. Existing DocVQA models entangle question-r

CO1 model#explainability#document-visual-question-answering#machine-learningRead on arxiv →
arxivMay 5

Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics

arXiv:2512.01020v2 Announce Type: replace Abstract: Evaluating the quality of LLM-generated reasoning traces in expert domains (e.g., law) is essential for ensuring credibility and explainability, yet remains challenging due to the inherent complexity of such reasoning tasks. We introduce LEGIT (LEG

LL1 model#evaluation#reasoning#legalRead on arxiv →
arxivApr 29

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

arXiv:2604.23786v1 Announce Type: new Abstract: In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental health. However, with the rapid advancement of Vision-Language Models (VLMs), their deployment in clinica

PHQW2 models#fairness#explainability#multimodalRead on arxiv →
arxivApr 21

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

arXiv:2604.16042v2 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing surveys in explainable AI largely focus on post-hoc explanation methods t

#explainability#nlp#researchRead on arxiv →
arxivApr 16bullish

RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows

arXiv:2509.20490v4 Announce Type: replace-cross Abstract: Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use and external knowledge bases. Nevertheless, for chest X-ray (CXR) interpretation, prevailing methods

RA1 model#multiagent#medical-imaging#explainabilityRead on arxiv →
arxivApr 10

On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

arXiv:2604.08424v1 Announce Type: cross Abstract: The increasing autonomy of spacecraft demands fault-detection systems that are both reliable and explainable. This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Co

CO1 model#explainability#anomaly-detection#spacecraftRead on arxiv →
arxivApr 10bullish

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM

arXiv:2604.06666v1 Announce Type: cross Abstract: Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are often inefficient and struggle with breaking news. Recent advances in

LAGRRE3 models#explainability#fake-news-detection#natural-language-processingRead on arxiv →
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