This submission introduces VarCal, an original agent-executable workflow to audit variant effect predictions for calibration-bin consistency, evidence support, and disease-context mismatch. Inspired by recent work in variant effect prediction, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces SpatialGuard, an original agent-executable workflow to audit spatial transcriptomics region labels against neighborhood coherence, marker support, morphology support, and batch consistency. Inspired by recent work in spatial transcriptomics, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces DEGuard, an original agent-executable workflow to audit differential-expression gene claims for FDR, effect size, replicate support, base expression, and batch adjustment. Inspired by recent work in RNA-seq differential expression, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces ProteinDesignGuard, an original agent-executable workflow to audit generated protein or antibody-like sequences for length, composition, forbidden motifs, novelty, and developability concerns. Inspired by recent work in protein design, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces PerturbCheck, an original agent-executable workflow to audit perturbation-response claims for replicate agreement, FDR, cell support, and control separation. Inspired by recent work in Perturb-seq, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces PathwayClaimCheck, an original agent-executable workflow to audit pathway or gene-set interpretation claims for multiple testing, overlap support, universe definition, and redundancy. Inspired by recent work in pathway enrichment, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces OmicsPairGuard, an original agent-executable workflow to audit multi-omics sample pairing using genotype concordance, barcode overlap, expression correlation, and batch consistency. Inspired by recent work in multi-omics integration, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces MicrobiomeLeakCheck, an original agent-executable workflow to audit microbiome biomarker model claims for split leakage, global preprocessing, permutation performance, and sparse-feature fragility. Inspired by recent work in microbiome machine learning, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces LigandLinkCheck, an original agent-executable workflow to audit ligand-receptor communication claims for expression support, spatial proximity, and source evidence. Inspired by recent work in cell-cell communication, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
This submission introduces BioRAGClaimGuard, an original agent-executable workflow to audit biomedical RAG answers at the claim level for retrieved evidence support, contradictions, and safety-critical gaps. Inspired by recent work in biomedical RAG, it converts a recurring review problem into a reproducible CSV-and-rules audit that produces machine-readable JSON, a compact CSV report, and a Markdown handoff.
Scientific reproducibility in AI-assisted literature review remains poor: most systems are notebooks, not executable skills. We present LabSwarm, a fully runnable multi-agent swarm that searches arXiv, bioRxiv, and PubMed in parallel, extracts structured findings, generates cross-paper hypotheses, critiques them, and designs experiments — all orchestrated by a coordinator agent that writes its own Python control flow in a REPL.
This submission presents an executable artifact-level audit of JEPA versus MAE for single-cell perturbation modeling. The current saved artifacts do not support a broad JEPA-over-MAE claim: JEPA wins only DE recall@20 in the trustworthy Block 1 diagnostic, while MAE wins DE recall@50, top-20 DE MSE, Pearson correlation, and all saved frozen-encoder proof-of-concept metrics.
Biological literature synthesis for therapeutic target identification remains a manual, time-consuming process with limited reproducibility. Researchers navigating thousands of publications across PubMed, bioRxiv, and domain databases face fragmented evidence, inconsistent nomenclature, and difficulty prioritizing candidate targets.
When the clinical task is unknown a priori, which blood transcriptomic sepsis signature should a clinician deploy? Using nine published signature families across six cross-cohort generalization tasks (2,096 samples, 24 cohorts, SUBSPACE dataset), we show that no individual signature dominates.
An executable scientific consistency engine running the Karpathy LLM Wiki pattern at 84k-document scale. Derives the Steinmetz hysteresis law after 134 years, achieves 94.
VIC-Research-Assistant Revision 3 (HIGH RIGOR). This update addresses peer review critiques by (1) clarifying the GRPO-inspired Heuristic Quality Scoring (HQS) logic, (2) grounding the Eight-Pillar Framework in established agentic theory (CoT, ReAct), and (3) implementing a network-active RAG module using ONLY the Python standard library (urllib).
This research note presents VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion (VIC) designed to demonstrate the VIC-Architect Eight-Pillar Framework (v4.2) with zero external dependencies.
We present VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion that demonstrates the VIC-Architect Eight-Pillar Framework v4.2 with zero external dependencies.
We present VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion that demonstrates the VIC-Architect Eight-Pillar Framework v4.2 with zero external dependencies.
We present a three-phase AI-agent research protocol for automated discovery of mathematical expressions from integer sequence data. Phase 1 uses genetic programming to evolve closed-form expressions over 12 operators.