Neuro-symbolic compliance monitors (TRACE framework) enforcing causal DAG constraints on agentic AI-driven target gene discovery (SCTA) will reduce false positives in rare subpopulation identification by blocking unconstrained tool-use violations that propagate through multi-agent workflows.

In agentic multi-agent single-cell target discovery (SCTA) workflows performing rare-subpopulation identification (target cluster prevalence <5% of total cells), inserting a neuro-symbolic compliance monitor (TRACE) that enforces causal DAG constraints (derived from a curated prior-knowledge causal graph of the relevant tissue/pathway) at each tool-call boundary will reduce the false discovery rate (FDR) of proposed marker-gene/subpopulation calls by ≥30% relative to an unconstrained agentic pipeline, on matched benchmark datasets, at equal or better recall (true-positive rate degradation ≤5 percentage points), when measured against orthogonally-validated ground truth (e.g., FACS-sorted or spatially co-registered rare populations).

Minimum viable test (MVT): 2-arm comparison (TRACE-monitored SCTA agent vs. unmonitored SCTA agent) on 1 well-characterized rare-population benchmark (e.g., PBMC CITE-seq with known rare dendritic cell/basophil subsets validated by FACS), n=5 pipeline runs per arm with different random seeds/agent temperatures, measuring FDR and recall against FACS-confirmed ground truth. Full validation extends to 3 datasets, 3 tissue types, and 2 ablation arms (random-DAG monitor, no-DAG rule-based monitor).

# TRACE monitor pseudocode
class CausalDAGMonitor:
    def __init__(self, dag: nx.DiGraph):
        self.dag = dag  # nodes=genes/cell-types/pathways, edges=causal/expression relations

    def check_tool_call(self, call: ToolCall) -> MonitorResult:
        claim = extract_causal_claim(call)  # e.g., "marker X -> cell_type Y"
        if claim is None:
            return MonitorResult(allow=True)
        if not path_exists(self.dag, claim.source, claim.target, max_hops=2):
            return MonitorResult(allow=False, reason="no causal path in DAG",
                                  suggest_alternative=nearest_valid_claim(self.dag, claim))
        return MonitorResult(allow=True)

class SCTAAgentPipeline:
    def __init__(self, monitor: Optional[CausalDAGMonitor] = None):
        self.monitor = monitor
        self.tools = [cluster_cells, run_DE, annotate_celltype, rank_markers]

    def run(self, adata):
        for step in self.agent_plan(adata):
            call = self.llm_agent.propose_tool_call(step)
            if self.monitor:
                result = self.monitor.check_tool_call(call)
                if not result.allow:
                    call = self.llm_agent.revise(call, result.reason)
                    log(call, blocked=True)
            output = execute(call)
            self.state.update(output)
        return self.state.final_subpopulation_calls()

# Evaluation
for dataset in [pbmc_citeseq, tabula_sapiens_subset, synthetic_trap]:
    for arm in ["trace", "baseline", "random_dag_ablation", "rule_based_ablation"]:
        for seed in range(5):
            run_pipeline(dataset, arm, seed) -> log calls, final calls
    compute_FDR_recall_vs_ground_truth()
    paired_bootstrap_test(trace_results, baseline_results)