{"total":13,"items":[{"citing_arxiv_id":"2606.27956","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Linking the \"inner\" and \"outer\" self to mental health and brain networks","primary_cat":"physics.soc-ph","submitted_at":"2026-06-26T10:54:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"HCP data analysis clusters individuals by social profiles into two groups where the more socially beneficial cluster scores higher on positive mental health measures and shows lower interconnectivity especially in the default mode network.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.24434","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Do Waders, Swimmers, and Divers Exist? A GPS-Based Pilot Study of Site-Dependent Visitor Movement in Theme Parks","primary_cat":"physics.soc-ph","submitted_at":"2026-06-23T11:10:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GPS tracking across theme parks shows visitor movement forms a continuum rather than discrete types, diverges from self-reports, and reverses feature relationships from site to site, requiring local calibration.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12946","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Data Aphasia: An Institutional Counterfactual Study of the Stability of Academic Cognition Under Letter-Grade Evaluation Systems","primary_cat":"cs.CY","submitted_at":"2026-06-11T06:19:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Converting percentage scores to A/B/C/D grades reduces information entropy by 69 percent, makes optimal student clusters sensitive to single data points, and drops temporal diagnostic consistency from 93-96 percent to 52-96 percent.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05471","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning","primary_cat":"cs.CV","submitted_at":"2026-06-03T21:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19260","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AQuaUI: Visual Token Reduction for GUI Agents with Adaptive Quadtrees","primary_cat":"cs.AI","submitted_at":"2026-05-19T02:13:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AQuaUI uses adaptive quadtrees to cut visual tokens in GUI-agent LMMs by up to 29.52% at inference time while retaining 99.06% of full-token accuracy on grounding and navigation benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16824","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning","primary_cat":"cs.LG","submitted_at":"2026-05-16T05:57:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Token-level confidence trajectories in LLMs encode a content-agnostic geometry that separates correct and incorrect reasoning traces and supports a lightweight correctness estimator called NeuralConf.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06968","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On Similarity of Computational Kernels in our Codes and Proxies","primary_cat":"cs.DC","submitted_at":"2026-05-07T21:36:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"New hardware-usage-based similarity metrics can identify matching computational kernels between proxy applications and performance suites on both CPU and GPU systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08242","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data","primary_cat":"q-bio.QM","submitted_at":"2026-05-07T09:05:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00637","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Class Angular Distortion Index for Dimensionality Reduction","primary_cat":"cs.LG","submitted_at":"2026-05-01T13:19:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12049","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Leveraging Weighted Syntactic and Semantic Context Assessment Summary (wSSAS) Towards Text Categorization Using LLMs","primary_cat":"cs.CL","submitted_at":"2026-04-13T20:41:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"wSSAS is a two-phase deterministic framework that uses hierarchical text organization and SNR-based feature prioritization to improve clustering integrity, categorization accuracy, and reproducibility when applying LLMs to large review datasets.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"Google Business 121,826 03/01/2009 - 08/25/2021 45 Book Titles Restaurant-related Goodreads Book 157,407 12/07/2006 - 11/03/2017 51 Restaurants Literary, subjective 4.3Evaluation Datasets: Multi-Domain Selection and Characteristic Analysis To demonstrate the generalizability of the wSSAS methodology, we utilized three diverse, industry-standard datasets from the University of California, San Diego (UCSD) [45] (Table 3) 1. Google Business Reviews (American & Fast Food restaurants): 121K reviews from North Dakota, used for restaurant sentiment analysis. 2. Amazon Product Reviews (Health & Personal Care Products): 155K reviews, focused on product discourse over a 3.5-year window. 3. Goodreads Book Reviews (Spoilers): The full 157K dataset, testing the model's ability to handle long-form"},{"citing_arxiv_id":"2604.07891","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AFGNN: API Misuse Detection using Graph Neural Networks and Clustering","primary_cat":"cs.SE","submitted_at":"2026-04-09T07:01:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AFGNN detects API misuses in Java code more effectively than prior methods by representing usage as graphs and clustering learned embeddings from self-supervised training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.09136","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation","primary_cat":"cs.IR","submitted_at":"2025-10-10T08:37:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Controlled personalization combining editorial curation with modest algorithmic recommendations in legacy news increases engagement, diversity, and reduces popularity bias per an A/B test.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.04017","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Habitat Classification from Ground-Level Imagery Using Deep Neural Networks","primary_cat":"cs.CV","submitted_at":"2025-07-05T12:07:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Vision transformers with supervised contrastive learning achieve 91% top-3 accuracy and 0.66 MCC on ground-level habitat images, matching experienced ecological experts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}