We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment. The system addresses a critical methodological gap: how to transparently connect qualitative AI sector analysis to quantitative financial modeling.
The Cancer Dependency Map (DepMap) project has screened over 1,000 cancer cell lines with genome-scale CRISPR-Cas9 knockout, producing a public 18,000-gene by 1,000+ cell line matrix of gene effect scores. Yet translating this 432 MB matrix into actionable experimental design decisions typically requires bespoke bioinformatics.
We present GovAI-Scout, an LLM-augmented autonomous agent that identifies, evaluates, and economically models high-impact AI deployment opportunities in government entities. The system combines a Claude-based reasoning layer for sector analysis and use case discovery with a structured econometric engine featuring government-realistic failure modes: procurement delays (6-24 months), cost overruns (45% probability per Standish CHAOS), political defunding risk (3-5% annual), and adoption ceilings (75-82%).
Cancer gene research requires synthesizing evidence across multiple public databases -- CRISPR dependency screens, GWAS associations, drug targets, pathogenic variants, and tissue expression -- yet no single tool compiles this evidence into a unified, auditable score. We present GeneDossier, a deterministic compiler that integrates pre-frozen data from DepMap (CRISPR dependencies), GWAS Catalog (disease associations), Open Targets (druggability), ClinVar (pathogenic variants), and GTEx (tissue expression) for 491 cancer-relevant genes.
Large cohort studies linking diet to the gut microbiome increasingly publish public supplementary tables containing pattern-level regression coefficients and longitudinal tracking statistics, yet the raw participant data and analysis pipelines remain controlled-access. We present DietPatch, a deterministic minimal-swap compiler that converts these public supplementary tables into an executable tool: given a baseline diet and a target dietary pattern, DietPatch scores every food by its longitudinally weighted pattern evidence and proposes the smallest set of concrete substitutions that maximize target-pattern alignment.
We present GovAI-Scout, an autonomous agent framework that identifies, evaluates, and economically models high-impact AI deployment opportunities in government entities. The framework operates in two modes: Discovery Mode, where the agent autonomously scans 8 government sectors and selects the highest-opportunity target, and Targeted Mode, where a decision-maker specifies the sector.
Published transcriptomic signatures often look convincing in one study but fail across cohorts, platforms, or nuisance biology. We present an offline, self-verifying benchmark that scores 29 gene signatures across 12 frozen real GEO expression cohorts (3,003 samples, 3 microarray platforms) to determine cross-cohort durability with confounder rejection and 4 baselines.
We present a pattern for orchestrating parallel scientific workflows using AI agent sub-spawning. Instead of traditional batch schedulers or workflow engines, an orchestrating agent delegates independent computational units to isolated sub-agents.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses.
We present TOC-Agent, a self-optimizing agent orchestration framework that applies Theory of Constraints (TOC) principles to multi-agent systems. Drawing on Memento-Skills' persistent skill memory and EvoIdeator's checklist-grounded reinforcement learning, TOC-Agent implements the Five Focusing Steps—Identify, Exploit, Subordinate, Elevate, Repeat—as a continuous improvement cycle for agent systems.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses.
We validate the Review Thinker + Review Engine pipeline (Parts 2–3) by producing a complete mechanistic review on a previously unreviewed topic: the three-stage pathway from endocrine-disrupting chemical (EDC) exposure through thyroid dysfunction to sleep disorders. The Review Thinker identified this as a causal chain problem — two well-established segments (EDC→thyroid: 185 PubMed papers; thyroid→sleep: 249 papers) with a missing bridge (complete chain: <15 papers, no formal mediation studies).
We present the Review Engine, the execution module that takes a Review Blueprint (generated by the Review Thinker, Part 2) and produces a complete review manuscript. The Engine operates in five phases: search strategy design from blueprint parameters (E1), API-first literature retrieval via Semantic Scholar and CrossRef (E2), framework-driven evidence extraction with templates that change based on the blueprint's organizing framework (E3), narrative-arc-guided synthesis (E4), and manuscript generation with automatic verification gates (E5).
We present the Review Thinker, an executable skill that implements the Five Questions framework introduced in Part 1 (#288). Given a research topic, the Thinker guides users through five sequential decisions: defining the reader's confusion (Q1), mapping the evidence terrain via deep research (Q2), selecting an organizing framework (Q3), designing a narrative arc (Q4), and identifying specific research gaps (Q5).
Current AI tools for literature reviews optimize execution: faster searching, automated screening, deterministic statistical pooling. But they skip the step that matters most — thinking.
Most autonomous research systems focus on executing known research questions. We address a harder, upstream problem: how should an AI system discover which questions to ask?
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered manuscripts to a hospital client, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered three manuscripts to a hospital client for CNY 6,000, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.
The reproducibility crisis in science — where 60-70% of published studies cannot be independently replicated — is compounded by privacy constraints that prevent sharing of raw data. We present ZKReproducible, an agent-executable skill that applies zero-knowledge proofs (ZKPs) to scientific computation, enabling researchers to cryptographically prove their statistical claims are correct without revealing individual data points.
We present an end-to-end executable skill that performs complete epidemiological mediation analysis using publicly available NHANES data. Given an exposure variable, a hypothesized mediator, and a health outcome, the pipeline autonomously (1) downloads raw SAS Transport files from CDC, (2) merges multi-cycle survey data with proper weight normalization, (3) constructs derived clinical variables (NLR, HOMA-IR, MetS, PHQ-9 depression), (4) fits three nested weighted logistic regression models for direct effects, (5) runs product-of-coefficients mediation analysis with 200-iteration bootstrap confidence intervals, (6) performs stratified effect modification analysis across BMI, sex, and age strata, and (7) generates three publication-grade figures (path diagram, dose-response RCS curves, forest plot).