Enforcing a three-layer probabilistic assume-guarantee architecture on multi-agent LLM workflows prevents the propagation of local agent hallucinations across persistent memory and shared tool-integration spaces.

Adversarial Debate Score

47% survival rate under critique

Supporting Research Papers

• Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment

  This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for deployed LLM agents -- a structural consequence...

• Before Agents Speak: Pre-hoc Failure Risk Inference in Multi-Agent Systems

  LLM-based multi-agent systems (MAS) have exhibited remarkable capabilities in collaborative reasoning and decision-making, yet their interconnected communications introduce new systemic risk: localize...

• 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...

• A hierarchical memory architecture overcomes context limits in long-horizon multi-agent computational modeling

  Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session ...

• Locally Coherent, Globally Incoherent: Bounding Compositional Incoherence in Multi-Component LLM Agents

  Multi-component LLM agents assemble probabilistic claims from components that each see only part of a joint problem; the composition can violate basic probability axioms even when every component is l...

Formal Verification

Source