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.

Adversarial Debate Score

68% survival rate under critique

Supporting Research Papers

• Mozi: Governed Autonomy for Drug Discovery LLM Agents

  Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical...

• Neuro-Symbolic Software Verification: Hyper-charging Local Language Models with Symbolic Reasoning at Scale

  Loop invariant synthesis remains a central and pivotal bottleneck in formal software verification. Recent LLM-based Neuro-Symbolic tools have achieved impressive solve rates. However, these tools rely...

• Neuro-Symbolic Compliance: Integrating LLMS and SMT Solvers for Automated Financial Legal Analysis

  Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance ...

• Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda

  LLM-based agents are entering regulated industries where they automate judgment intensive quality management processes. We argue that symbolic structures already embedded in these domains, including r...

Formal Verification

Source