Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper

arXiv:2610.11542 · cs.CL, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Frustration — 15× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Cognitive Load — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• LLM judge — 3× in this paper · explained in 2 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes

Sources

• Constitutional AI: Harmlessness from AI Feedback
• Why Do Multi-Agent LLM Systems Fail?
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• The Llama 3 Herd of Models
• Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
• LLMs Get Lost In Multi-Turn Conversation
• Decoupling Logic from Persona: Structural Immunity of Edge LLM Agents to Context Pollution
• Ignore Previous Prompt: Attack Techniques For Language Models
• Efficient Guided Generation for Large Language Models
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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