Neuro-symbolic runtime monitors for drug discovery LLM agents (Mozi framework) will reduce unconstrained tool-use violations by >40% when enforced via cryptographically verifiable authorization (e.g., Datalog policies with ZK-proofs), as measured by compliance failure rates in high-stakes molecular docking workflows.

When a drug-discovery LLM agent (executing molecular docking workflows involving tool calls to docking engines, database queries, structure retrieval, and file I/O) is instrumented with a neuro-symbolic runtime monitor (the "Mozi" framework) that enforces Datalog-encoded authorization policies with cryptographic (zero-knowledge-proof-based) verifiability, the rate of unconstrained tool-use violations (defined as tool invocations that violate a pre-specified authorization policy, e.g., unauthorized file writes, unsanctioned external API calls, unapproved compound submission to a physical synthesis vendor) will decrease by more than 40% (relative reduction) compared to an identical agent operating with only prompt-based or unguarded tool-calling constraints, measured as compliance-failure rate per 1,000 agent-tool-interaction episodes on a held-out benchmark of adversarial and naturalistic docking-workflow tasks, at p < 0.05 significance with a pre-registered statistical test.

Randomized controlled comparison, within-subject-task design:

• Arm A (control): LLM agent with tool access, no runtime monitor (baseline prompt-engineered guardrails only).
• Arm B (ablation): LLM agent + syntactic/schema validator (JSON-schema tool-argument checking, no Datalog/ZK).
• Arm C (treatment): LLM agent + Mozi neuro-symbolic monitor (Datalog policy engine + ZK-proof-based authorization gate). Each arm runs identical task suite (N=500 docking workflow episodes: 350 naturalistic, 150 adversarial/red-teamed to induce violations). Violations logged via ground-truth oracle (independently coded policy-violation labels, inter-rater reliability κ>0.8). Primary outcome: compliance-failure rate per 1,000 tool-call episodes. Pre-registration of hypotheses, sample size, and analysis plan before data collection.

# Mozi Runtime Monitor Architecture
class MoziMonitor:
    def __init__(self, datalog_policy_file, zk_circuit):
        self.policy = DatalogEngine.load(datalog_policy_file)
        self.zk = ZKProver(zk_circuit)

    def intercept(self, tool_call, agent_context):
        facts = translate_to_facts(tool_call, agent_context)
        self.policy.assert_facts(facts)
        decision = self.policy.query("permitted(Call)", Call=tool_call.id)
        proof = self.zk.prove(decision, facts, self.policy.rules)
        if not self.zk.verify(proof):
            return BLOCK, log_violation(tool_call, facts)
        if decision.permitted:
            return ALLOW, proof
        else:
            return BLOCK, log_violation(tool_call, facts)

# Agent loop
for step in agent.run(task):
    tool_call = agent.propose_tool_call(step)
    verdict, evidence = mozi.intercept(tool_call, agent.context)
    if verdict == ALLOW:
        result = execute(tool_call)
        agent.observe(result)
    else:
        agent.observe(rejection_message(evidence))