{"total":13,"items":[{"citing_arxiv_id":"2605.18576","ref_index":5,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement","primary_cat":"cs.LG","submitted_at":"2026-05-18T15:55:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"scHelix uses explicit gene-level partitioning into Anchors and Variants plus an asymmetric Align-Refine-Fuse dual-stream architecture to improve batch correction in scRNA-seq without over-correcting biological signals.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14118","ref_index":19,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Pluot: Towards 'write once, run everywhere' visualization software","primary_cat":"cs.HC","submitted_at":"2026-05-13T21:08:11+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Pluot enables a single Rust visualization rendering function to execute reproducibly across languages and output formats via generated bindings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04119","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Tree-Conditioned Edit Flows for Ancestral Sequence Reconstruction","primary_cat":"q-bio.QM","submitted_at":"2026-05-05T13:04:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new tree-conditioned edit-flow model for ancestral sequence reconstruction achieves reasonable accuracy on substitution-only evolved sequences and superior localization of changes on natural indel-rich sequences.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies.Molecular Biology and Evolution, 32(1):268-274, 2015. doi: 10.1093/molbev/msu300. [34] Martin Steinegger and Johannes Söding. Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.Nature Biotechnology, 2017. doi: 10.1038/nbt.3988. [35] Morgan N. Price, Paramvir S. Dehal, and Adam P. Arkin. Fasttree 2: Approximately maximum-likelihood trees for large alignments.PLOS ONE, 5(3):e9490, 2010. doi: 10.1371/journal.pone.0009490. [36] Kazutaka Katoh and Daron M. Standley. Mafft multiple sequence alignment software version 7: Im- provements in performance and usability.Molecular Biology and Evolution, 30(4):772-780, 2013."},{"citing_arxiv_id":"2604.17214","ref_index":18,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition","primary_cat":"cs.AI","submitted_at":"2026-04-19T02:50:14+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Extracting structured information from unstructured medical text is a foundational task in clinical NLP [13, 22]. This process, often re- ferred to as medical entity recognition (MER) or named entity recog- nition (NER), involves extracting and classifying key medical entities such as medications, diagnoses, and procedures embedded within free-text narratives [18] as illustrated in Figure 1. Accurate MER is critical for a range of downstream applications, including clinical de- cision support for better patient care, clinical research, patient cohort identification, medical question-answering systems, and constructing longitudinal patient medical histories [13, 29, 25]. Traditional MER apporaches have relied heavily on hand-crafted"},{"citing_arxiv_id":"2604.09975","ref_index":5,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"EncFormer: Secure and Efficient Transformer Inference over Encrypted Data","primary_cat":"cs.CR","submitted_at":"2026-04-11T01:15:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EncFormer reduces online MPC communication by 1.4x-30.4x and end-to-end latency by 1.3x-9.8x versus prior hybrid FHE-MPC systems for private GPT- and BERT-style inference while preserving accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04593","ref_index":9,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering","primary_cat":"cs.IR","submitted_at":"2026-04-06T11:13:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CHR improves medical question answering retrieval by explicitly promoting evidence aligned with a correct hypothesis while penalizing content aligned with a plausible incorrect alternative.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.00586","ref_index":2,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine","primary_cat":"q-bio.MN","submitted_at":"2026-01-31T08:05:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.18883","ref_index":54,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Enumeration of Autocatalytic Subsystems in Large Chemical Reaction Networks","primary_cat":"q-bio.MN","submitted_at":"2025-11-24T08:40:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An efficient enumeration algorithm is developed from sufficient conditions on subgraphs in the bipartite König representation to identify autocatalytic subnetworks and minimal cores in full metabolic networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.02060","ref_index":40,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling","primary_cat":"q-bio.BM","submitted_at":"2025-09-02T07:58:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PepMorph generates morphology-targeted peptides via a Transformer conditional VAE and reports 83% success under CG-MD validation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.16559","ref_index":39,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Synergistic Benefits of Joint Molecule Generation and Property Prediction","primary_cat":"cs.LG","submitted_at":"2025-04-23T09:36:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2406.04098","ref_index":115,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data","primary_cat":"stat.ML","submitted_at":"2024-06-06T14:13:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.17086","ref_index":100,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Multicellular simulations with shape and volume constraints using optimal transport","primary_cat":"q-bio.QM","submitted_at":"2024-02-26T23:53:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Presents an optimal transport framework for simulating particle systems with arbitrary cell shapes and volumes that automatically handles exclusion constraints.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.01642","ref_index":12,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Introducing MathQA -- A Math-Aware Question Answering System","primary_cat":"cs.IR","submitted_at":"2019-06-28T08:27:53+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MathQA retrieves Wikidata formulas for natural language questions in English or Hindi, enables SymPy-based computation with user inputs and Wikidata constants, and outperformed a commercial engine by 13% in a user study while aiding formula imports with an 80% accurate heuristic.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}