Agentic AI governance mechanisms that allocate compute budgets via resource-contingent authorization (Resourced Authority) will reduce hallucination-driven failures in drug discovery agents by 40% when combined with UCB-driven Bayesian optimization, as the dual constraints of resource scarcity and uncertainty-aware exploration force convergence on validated biochemical hypotheses rather than speculative multi-target binders.
Agentic AI governance mechanisms that allocate compute budgets via resource-contingent authorization (Resourced Authority) will reduce hallucination-driven failures in drug discovery agents by 40% when combined with UCB-driven Bayesian optimization, as the dual constraints of resource scarcity and uncertainty-aware exploration force convergence on validated biochemical hypotheses rather than speculative multi-target binders.
Adversarial Debate Score
55% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
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...
- Trustworthy Agentic AI: A Survey and Taxonomy of Secure Coordination and Hallucination Mitigation in Multi-Agent Large Language Model Systems
Background: Large language model (LLM)-based agentic systems are evolving beyond single-turn generators into autonomous, toolusing, multi-agent workflows with persistent memory and self-directed plann...
- Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
We give a formal mechanism design model for the continuous participatory governance of a deployed AI agent. The mechanism is built on the principle that governance should control an AI agent through r...
- Optimal Resource Utilization for Autonomous Laboratory Orchestrators
In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely differen...
Literature Assessment
An LLM's reading of the literature — not computational verification.
Resource allocation may improve AI drug discovery but risks remain.
Method: literature_meta · Result: inconclusive
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.