{"total":14,"items":[{"citing_arxiv_id":"2606.26680","ref_index":5,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"2-Head 2D Returning Finite Automata","primary_cat":"cs.FL","submitted_at":"2026-06-25T07:13:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces 2-HRFA and B2-HRFA for picture languages, proves incomparability with CFMG, proper subset relations with RPDA and RFA, and examines closure properties.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26179","ref_index":31,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction","primary_cat":"cs.LG","submitted_at":"2026-06-24T12:35:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KG-TRACE fuses genomic features with RotatE KG embeddings via an epistemic trust gate for AMR prediction, reporting 0.976 AUROC on isoniazid resistance in the CRyPTIC cohort plus 92.5% symbolic coverage via a new Biological Grounding Ratio metric.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25711","ref_index":8,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Distributed SDN-Based Communication Architecture for the Pods4Rail System","primary_cat":"cs.ET","submitted_at":"2026-06-24T11:24:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Proposes hierarchical SDN-MEC architecture for Pods4Rail claiming lower edge controller latencies than prior literature, with defined workflows for regional policy and local failover.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.24115","ref_index":13,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy","primary_cat":"cs.CV","submitted_at":"2026-06-23T04:04:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"White-box method ReXTrust achieves highest AUC (peak 93.0) on Gut-VLM across five VLMs, outperforming alternatives by statistically significant margins while black-box and some gray-box methods collapse on certain models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18326","ref_index":3,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance","primary_cat":"cs.LG","submitted_at":"2026-06-16T17:27:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"RGNet implements renormalization-group-style hierarchical coarse-graining inside a neural network to produce multi-scale representations that improve fault prediction on the imbalanced AI4I dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09263","ref_index":32,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Jet Bundles as Higher-Order Polarised $k$-Contact Manifolds","primary_cat":"math.DG","submitted_at":"2026-06-08T09:33:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Jet bundles with Cartan distributions are characterized as polarised N^r_π-contact manifolds of jet type via a recognition theorem in k-contact geometry.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07001","ref_index":22,"ref_count":2,"confidence":0.5,"is_internal_anchor":false,"paper_title":"SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair","primary_cat":"cs.SE","submitted_at":"2026-05-07T22:33:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SmellBench is the first benchmark showing LLM agents resolve 47.7% of architectural code smells while accurately spotting false positives, but aggressive repairs often introduce new smells and degrade overall quality.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"across 9 Python repositories, where the best agent solves only 22% compared to 87% for a human developer, confirming that multi-file reasoning remains a major bottleneck. While RefactorBench tar- gets general code restructuring, no existing benchmark specifically addresses the repair of architectural smells. A complementary direction examines smells introduced by LLMs. Velasco et al. [22] performed a causal analysis showing that prompt design and model architecture are the dominant factors influenc- ing smell propensity during code generation, and that targeted mitigation strategies can reduce smell occurrence at inference time. 2.3 Architectural Code Smell Repair Architectural smells have been extensively catalogued and shown to negatively impact software maintainability [12], with systematic"},{"citing_arxiv_id":"2605.02596","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound","primary_cat":"cs.LG","submitted_at":"2026-05-04T13:47:41+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HARMES is the first large-scale dataset to combine wrist IMU, environmental, and audio sensors for recognizing 15 household activities across over 80 hours of data from 20 participants.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[8] Robin Burchard, Hurriat Ali, and Kristof Van Laerhoven. 2026. Improved Strategies for Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection. InSensor-Based Activity Recognition and Artificial Intelligence, Özlem Durmaz Incel, Jingwen Qin, Gerald Bieber, and Arjan Kuijper (Eds.). Vol. 16292. Springer Nature Switzerland, Cham, 308-323. doi:10.1007/978-3-032- 13312-0_18 [9] Robin Burchard, Pascal-André Brückner, Marius Bock, and Kristof Van Laerhoven. 2026. HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound. doi:10.5281/zenodo.19425719 20•Burchard et al. [10] Robin Burchard and Kristof Van Laerhoven. 2025. Multi-Modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing"},{"citing_arxiv_id":"2605.02978","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Observability for Post-Quantum TLS Readiness: A Multi-Surface Evidence Framework","primary_cat":"cs.CR","submitted_at":"2026-05-04T01:30:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A new multi-surface evidence framework for post-quantum TLS observability that combines passive, active, certificate, and registry data to assess endpoint capabilities across TLS 1.2/1.3 scenarios and outperforms prior analyzers in controlled tests and public campaigns.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04091","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"OpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated Learning","primary_cat":"cs.NI","submitted_at":"2026-04-26T14:38:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"OpenCLAW-Nexus uses a single discounted Beta-reputation model to unify reputation-based node selection, Rep-FedAvg aggregation, and reputation-aware BFT consensus, achieving Byzantine resilience in decentralized FL with 72.6% accuracy on non-IID CIFAR-10 under 20% attacks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12969","ref_index":9,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation","primary_cat":"cs.CV","submitted_at":"2026-04-14T17:01:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A sequential diffusion framework generates controllable abdominal anatomies with a Volume Control Scalar that decouples organ size from body habitus, achieving Dice scores around 0.83 and reducing distributional mismatch by 73.6% in a hepatomegaly example.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22828","ref_index":65,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development","primary_cat":"cs.CL","submitted_at":"2026-04-13T10:59:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A survey catalogs text and speech resources for Hausa and Fongbe, documenting sizes, domains, licensing, and gaps including limited Fongbe text diversity and missing Hausa speech corpora.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09833","ref_index":14,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing","primary_cat":"cs.NE","submitted_at":"2026-04-10T19:04:31+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":"the computational question is the I/O mapping and con- trollability of that dynamics. While temporal resolution is fundamentally bounded only by physical limits (Planck time), practical analog systems are constrained by ther- mal and material noise, yielding an effective signal-to- noise-limited bit depth often below 32-bit floating point precision [14]. 5) Resilience and Self-Healing:Semiconductor hard- ware is structurally brittle, whereas biological and flu- idic substrates can remain functional under deformation or partial damage. Reaction-diffusion media such as Be- lousov-Zhabotinsky systems can continue operating de- spite container deformation or division [15]. Synthetic bi- ological implementations additionally offer self-repair and"},{"citing_arxiv_id":"2604.03437","ref_index":29,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Is it Cake or is it AI? A Systematic Review of Human Uncertainty in Distinguishing Generative Artificial Intelligence Content","primary_cat":"stat.AP","submitted_at":"2026-04-03T20:21:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Humans perform at chance levels when distinguishing generative AI content from human content in text, images, and voice.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"research abstracts in shoulder and elbow surgery that mimic published abstracts Experienced peer reviewers in shoulder and elbow orthopaedic surgery 8 Experienced reviewers faced difficulties in distinguishing between human and AI-generated research content within shoulder and elbow surgery. Cardia F et al., LNCS (EC-TEL), 2025 [29] Machine- generated student questions based on University instructors 7 Results show that instructors struggle to differentiate between the two sets of questions, with accuracy close to random chance. university video lectures Šindlerová J et al., Springer Proc. Complexity, 2026 [13] Student essays written to resemble 17- year-old high"}],"limit":50,"offset":0}