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LLM agents fail complement consistency test, research finds

A new research paper investigates the consistency of probabilities reported by LLM agents in opinion simulations. The study found that three small open-source models—Qwen3-4B, Llama 3.2:3b, and Gemma 3-4B—exhibit a failure in complement consistency, meaning that the probability of a claim being true and the probability of its complement being true do not sum to one as expected. This discrepancy is significant, exceeding the impact of paraphrasing and persisting across various simulation parameters. The research suggests that this inconsistency needs further testing before simulated probabilities can be reliably interpreted as beliefs. AI

IMPACT Reveals a fundamental inconsistency in how LLM agents simulate beliefs, potentially impacting their reliability in opinion-based tasks.

RANK_REASON Research paper published on arXiv detailing a specific failure mode in LLM agents.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM agents fail complement consistency test, research finds

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Anouar Imel, Jin Qi ·

    Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion Simulations

    arXiv:2610.32874v1 Announce Type: new Abstract: In LLM-agent opinion simulations, each agent reports the probability that a claim is true after seeing its own previous probability and the probabilities reported by its neighbors, and that number becomes its state in the next round…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jin Qi ·

    Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion Simulations

    In LLM-agent opinion simulations, each agent reports the probability that a claim is true after seeing its own previous probability and the probabilities reported by its neighbors, and that number becomes its state in the next round. If the number describes the agent's position r…