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LLM Agents Fail Complement Consistency Test, Study Finds

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates the phenomenon of "complement consistency" in large language model agents, specifically examining if their reported probabilities for a claim and its complement sum to one. The study tested three open-source models: Qwen3-4B, Llama 3.2:3b, and Gemma 3-4B. Results indicated that all three models failed to maintain this consistency, with their paired reports exceeding one on average. This inconsistency was found to be significantly larger than the effect of paraphrasing and persisted across various simulation parameters, suggesting a fundamental issue in how these agents simulate beliefs. AI

IMPACT Reveals potential flaws in how LLM agents simulate beliefs, impacting the reliability of opinion simulations and agent interactions.

RANK_REASON Research paper published on arXiv detailing experimental findings on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Agents Fail Complement Consistency Test, Study Finds

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Research paper published on arXiv detailing experimental findings on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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…

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