“EfficientNet-B0 outperformed DenseNet121, achieving an accuracy of 84.6%, F1-score of 0.8899, and MCC of 0.6849.”

“This is a placeholder, as the provided content snippet is insufficient for a key quote. A relevant quote would discuss challenges or opportunities for AI in medical applications.”

“Experiments on a real-world image classification dataset demonstrate that EGT achieves up to 98.97% overall accuracy (matching baseline performance) with a 1.97x inference speedup through early exits, while improving attention consistency by up to 18.5% compared to baseline models.”

“This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare.”

“The article's core message is about avoiding the 'death' of AI projects in production due to unexplainable and undocumented code.”

“AI is not your 'smart friend'.”

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“AI misses nearly one-third of breast cancers, study finds”

“OmniNeuro is decoder-agnostic, acting as an essential interpretability layer for any state-of-the-art architecture.”

“Exploratory results demonstrated that ConvNeXt-Tiny achieved the highest performance, attaining a 96.88% accuracy on the test”

“CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves +22.7 pp in disease classification and +19.5 points in QA score over no-caption baselines.”

“While models achieve high semantic similarity scores (BERTScore F1: 0.81-0.90), all our factuality metrics reveal alarmingly low performance (LLM-based statement-level precision: 4.38%-32.88%).”

“CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules.”

“McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis.”

“The paper argues for a shift in perspective, prioritizing the user's informational needs and perspective by incorporating ToM within XAI.”

“The combined analysis demonstrates that explainable AI(XAI) techniques can illuminate hidden failure modes, guide architectural refinements, and inform obstacle aware deployment strategies for learning based IK.”

“最初にそのログを見たとき、私は「これはまさにインターンに教えていることと同じだ」と感じました。”

“The system delivers interpretable, real-time predictions via Explainable AI (XAI) visualizations, supporting transparent clinical decision-making.”

“GRExplainer extracts node sequences as a unified feature representation, making it independent of specific input formats and thus applicable to both snapshot-based and event-based TGNNs.”

“DICE achieves 85.7% agreement with human experts, substantially outperforming existing LLM-based metrics such as RAGAS.”

“I don’t start from code. I start by talking to the AI, giving my thoughts and structural ideas first.”

“CM^2 achieves human-level concept learning by identifying only the structural "important features" a human would consider, allowing it to classify very similar documents using only one sample per class.”

“Models learn discriminative and interpretable features from as few as one or five training examples.”

“The framework outperforms state-of-the-art methods in both predictive accuracy and interpretability.”

“The paper proposes two techniques for addressing this problem of statute prediction with explanations -- (i) AoS (Attention-over-Sentences) which uses attention over sentences in a case description to predict statutes relevant for it and (ii) LLMPrompt which prompts an LLM to predict as well as explain relevance of a certain statute.”

“The paper focuses on secure and explainable fraud detection.”

“CENNET provides causal explanations for predictions by NNs and uses structural causal models (SCMs) effectively combined with the NNs although SCMs are usually not used as predictive models on their own in terms of predictive accuracy.”

“The architecture uses a consortium of heterogeneous LLM and VLM agents to generate candidate outputs, a dedicated reasoning agent for consolidation, and explicit cross-model comparison for explainability.”

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“The source is ArXiv, indicating a research paper.”

“The paper focuses on improving both accuracy and explainability in the context of medical image analysis.”

“The research is sourced from ArXiv.”

“The research focuses on 'Measuring Mechanistic Multiplicity Across Training Runs'.”

“The article focuses on explainable time-series forecasting using a sampling-free SHAP approach for Transformers.”

“The article is sourced from ArXiv, indicating it is a pre-print publication.”

“The paper likely introduces a novel methodology to address the limitations of existing XAI methods, given the title's focus.”

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“The paper leverages cross-modal counterfactual explanations.”

“The article likely discusses limitations of current methods.”

“The study utilizes blood cell images for malaria diagnosis.”

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“The research focuses on an expert system for explainable place recognition in changing environments.”

“The paper focuses on an Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability.”

“The research focuses on industrial compressed air systems.”

“The research focuses on explainable Transformer-CNN fusion.”

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“The article discusses the use of Neuro-Symbolic Generalization and Unbiased Adaptive Routing within medical AI.”

“In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples).”