{"total":39,"items":[{"citing_arxiv_id":"2607.00542","ref_index":43,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AI-Centered Grand Challenges in Visual Analytics for Healthcare: Synthesizing the VAHC 2025 Community Experience","primary_cat":"cs.HC","submitted_at":"2026-07-01T07:33:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Synthesizes VAHC 2025 workshop papers and group discussions into five grand challenge clusters for AI in visual analytics for healthcare.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.31755","ref_index":115,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Technical Typology of AI Systems in Public 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terms, and 221 interactions, raising C-index from 0.805 to 0.815.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02589","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Rashomon-Seeded Annealing for Robust Bayesian Inference in Factorial Designs","primary_cat":"stat.ME","submitted_at":"2026-05-21T05:01:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Rashomon-seeded annealing repurposes Rashomon sets as warm starts for annealed importance sampling to enable full posterior inference in factorial designs without exhaustive enumeration.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21454","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ProtoPathway: Biologically Structured Prototype-Pathway 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series.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19826","ref_index":12,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support","primary_cat":"cs.AI","submitted_at":"2026-05-19T13:19:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18889","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Soft Learning","primary_cat":"cs.LG","submitted_at":"2026-05-16T22:14:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12809","ref_index":183,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces","primary_cat":"cs.LG","submitted_at":"2026-05-12T23:01:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11179","ref_index":19,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes","primary_cat":"stat.ML","submitted_at":"2026-05-11T19:42:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A Lie-algebraic kernel reparameterizes 3D rotationally anisotropic Gaussian processes with explicit principal length-scales and SO(3) orientations, matching full SPD flexibility but improving interpretability over axis-aligned ARD.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Shankar Sastry. 1994.A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton, FL. [17] Radford M. Neal. 1996.Bayesian Learning for Neural Networks. Lecture Notes in Statistics, Vol. 118. Springer, New York, NY. [18] Carl Edward Rasmussen and Christopher K. I. Williams. 2006.Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA. [19] Cynthia Rudin. 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.Nature Machine Intelligence1, 5 (2019), 206-215. doi:10.1038/s42256-019-0048-x [20] Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong. 2022. Interpretable Machine Learning: Fundamental"},{"citing_arxiv_id":"2605.06943","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data","primary_cat":"cs.LG","submitted_at":"2026-05-07T21:01:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"both predictive performance and interpretability are essential, including clinical waveform analysis [1, 2], audio classification [3, 4], and human-activity recognition [5]. While deep learning networks achieve strong performance in these domains, the representations that drive their predictions are difficult to inspect, and post hoc attribution methods may fail to faithfully reflect the model's actual decision process [6, 7]. Projection-based prototypical part neural networks offer a compelling alternative by building case-based reasoning directly into the model. In theProtoPNetframework [ 8] and its extensions [9-12], predictions are made by comparing inputs to a learned set of prototypes representing characteristic patterns. A defining feature of this family of methods is an explicit"},{"citing_arxiv_id":"2605.02015","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection","primary_cat":"cs.LG","submitted_at":"2026-05-03T18:42:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A divide-and-conquer method decomposes network intrusion detection into focused subtasks, allowing lightweight models to gain up to 43.3% higher local accuracy and 257x smaller size while improving robustness and explainability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01164","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"LLMs Should Not Yet Be Credited with Decision Explanation","primary_cat":"cs.AI","submitted_at":"2026-05-01T23:46:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"LLMs support decision prediction and rationale generation but lack evidence for genuine decision explanation, requiring stricter standards to avoid over-crediting.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"not just an attractive afterthought, but part of a system optimized or evaluated in relation to behavior. Still, a prediction-useful trace is not automatically a decision explanation. First, faithfulness to a model's own answer is itself an empirical question. Work on faithful chain-of-thought, interventions on reasoning traces, and causal mediation shows that intermediate text may or may not be what the model actually uses to produce its answer [33, 17-19]. Second, even when a trace is faithful to the model's own computation, that is not the same as being faithful to the human decision-generating structure. A trace can be optimized to support a correct prediction, to satisfy a reward signal, or to verbalize familiar psychological concepts, while still functioning as an outcome-conditioned rationalization."},{"citing_arxiv_id":"2604.23136","ref_index":59,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study","primary_cat":"cs.CY","submitted_at":"2026-04-25T04:35:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A think-aloud study reveals that AI tools in early research misrepresent uncertainty, obscure provenance, and create fragile trust, leading researchers to develop compensatory strategies to preserve scholarly judgment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21042","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Interpretable Quantile Regression by Optimal Decision Trees","primary_cat":"cs.LG","submitted_at":"2026-04-22T19:40:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A novel algorithm learns sets of optimal quantile regression trees to predict full conditional distributions interpretably and efficiently.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12793","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Human Agency, Causality, and the Human Computer Interface in High-Stakes Artificial Intelligence","primary_cat":"cs.HC","submitted_at":"2026-04-14T14:26:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper proposes a Causal-Agency Framework to restore human causal control at AI interfaces by combining causal models, uncertainty quantification, and human-centered evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06017","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST","primary_cat":"cs.CV","submitted_at":"2026-04-07T16:14:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A-ROM delivers competitive MedMNIST performance via pretrained ViT metric spaces, a concept dictionary, and kNN without backpropagation or fine-tuning, framed as interpretable few-shot learning under the Platonic Representation Hypothesis.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"In this high-stakes environment, intrinsic transparency has become a functional requirement for both regulatory accountability and patient safety [12,6]. Concept-Centric InterpretabilityA critical distinction exists between ex- plainability, which relies on post-hoc surrogates, and interpretability, where the architecture is understandable by design [29,7]. In a seminal critique, [29] ar- gues that high-stakes decisions should prioritize such inherent interpretability over retroactive \"guessing.\" This has catalyzed \"interpretable-by-design\" frame- works that utilize a \"Concept Dictionary\" to map latent activations to symbolic features, such as vessel tortuosity or nuclei density [13,4]. While these models"},{"citing_arxiv_id":"2604.05539","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"From Large Language Model Predicates to Logic Tensor Networks: Neurosymbolic Offer Validation in Regulated Procurement","primary_cat":"cs.AI","submitted_at":"2026-04-07T07:38:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A neurosymbolic pipeline extracts predicates from offer texts with an LLM and validates them via Logic Tensor Networks, delivering performance comparable to standard models plus built-in interpretability on a real corpus.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.15250","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks","primary_cat":"cs.LG","submitted_at":"2026-03-16T13:21:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19751","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains","primary_cat":"cs.AI","submitted_at":"2026-03-16T11:44:43+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.24176","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions","primary_cat":"cs.CY","submitted_at":"2026-02-27T16:58:27+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"and academic research globally [20]. After nearly a decade, the legacy of the XAI program is increasingly questioned, with critics arguing that challenges have outweighed achievements. It is described as being \"in trouble\" [6], and some scholars suggest it should be \"stopped\" for high-stakes decisions [21] or has no role in the future of human-centric AI approaches [22]; others view it as myth [23] or consider it already \"dead\" [24]. A study by Hoffman[25] demonstrates that most XAIs provide shallow and inadequate explanations. Through a systematic analysis of 34 XAI systems published during 2019-2021, selected from an initial pool of 165 articles using strict criteria requiring actual machine-generated explanations, they classify"},{"citing_arxiv_id":"2512.07988","ref_index":67,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"HOLE: Homological Observation of Latent Embeddings for Neural Network Interpretability","primary_cat":"cs.LG","submitted_at":"2025-12-08T19:20:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HOLE applies persistent homology to latent embeddings in neural networks and uses visualizations such as cluster flow diagrams to reveal patterns of class separation, feature disentanglement, and robustness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.26576","ref_index":54,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"\"Show Me You Comply... Without Showing Me Anything\": Zero-Knowledge Software Auditing for AI-Enabled Systems","primary_cat":"cs.SE","submitted_at":"2025-10-30T15:03:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ZKMLOps is an MLOps framework that uses zero-knowledge proofs to generate verifiable cryptographic evidence of AI model compliance without revealing confidential information.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.03884","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution","primary_cat":"cs.AI","submitted_at":"2024-12-05T05:30:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces a unified evaluation framework for XAI using five principled metrics and the PGCA method that fuses grid perturbation with Grad-CAM++ , reporting top scores in fidelity, interpretability and fairness on ResNet-50 models across five image domains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2307.13702","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Measuring Faithfulness in Chain-of-Thought Reasoning","primary_cat":"cs.AI","submitted_at":"2023-07-17T01:08:39+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Chain-of-Thought reasoning in LLMs is often unfaithful, with models relying on it variably by task and less so as models scale larger.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}