Multi-agent LLM systems utilizing persistent memory and self-directed planning exhibit hallucination propagation patterns that can be suppressed by imposing localized, tissue-inspired repulsive boundary constraints on inter-agent communication graphs.

Adversarial Debate Score

60% survival rate under critique

Supporting Research Papers

• 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...

• The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

  Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social dilemmas. Across 7 LLMs and 4 games over 500 round...

• Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo

  Providing AI agents with reliable long-term memory that does not hallucinate remains an open problem. Current approaches to memory for LLM agents -- sliding windows, summarization, embedding-based RAG...

• Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite

  Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph memory has been adopte...

• Emergent Collusion in Long-Horizon LLM Agent Interaction

  LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent e...

Formal Verification

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