Close Menu
techwavedispatch.com

    Subscribe to Updates

    Get the latest creative news from FooBar about art, design and business.

    What's Hot

    Judge Arena: Benchmarking LLMs as Evaluators

    November 11, 2026

    Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration

    November 11, 2026

    The First Multilingual LLM Debate Competition

    November 11, 2026
    Facebook X (Twitter) Instagram
    Trending
    • Judge Arena: Benchmarking LLMs as Evaluators
    • Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration
    • The First Multilingual LLM Debate Competition
    • [2605.08007] Interpreting Reinforcement Learning Agents with Susceptibilities
    • From Files to Chunks: Improving HF Storage Efficiency
    • Probabilistic Graphical Modelling for Supervised Finetuning
    • Faster Text Generation with Self-Speculative Decoding
    • Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning
    Facebook X (Twitter) Instagram
    techwavedispatch.com
    Subscribe
    Wednesday, November 11
    • Home
    • AI Tools & Software
    • Machine Learning
    • Generative AI
    • Robotics & Automation
    • Future Tech & Hardware
    • AI in Business
    • Ethics & Regulations
    • More
      • Gadgets & Smart Devices
      • Cybersecurity & AI
      • Tech Breakthroughs
    techwavedispatch.com
    Home»Machine Learning»[2610.33026] False Belief Dissemination, Persistence and Mitigation in Multi-Agent Social Networks Through the Lens of the Mandela Effect
    Machine Learning

    [2610.33026] False Belief Dissemination, Persistence and Mitigation in Multi-Agent Social Networks Through the Lens of the Mandela Effect

    adminBy adminNovember 9, 2026No Comments2 Mins Read
    Facebook Twitter Pinterest LinkedIn Tumblr Email
    [2610.33026] False Belief Dissemination, Persistence and Mitigation in Multi-Agent Social Networks Through the Lens of the Mandela Effect
    Share
    Facebook Twitter LinkedIn Pinterest Email

    [Submitted on 11 Oct 2026 (v1), last revised 6 Nov 2026 (this version, v2)]

    View a PDF of the paper titled False Belief Dissemination, Persistence and Mitigation in Multi-Agent Social Networks Through the Lens of the Mandela Effect, by Dianzhi Yu and 9 other authors

    View PDF
    HTML (experimental)

    Abstract:Multi-agent social networks are emerging as communities in which agents communicate, retain memories, and publish information that others can find as evidence. These interactions create opportunities for false claims to spread and remain influential beyond their original sources, analogous to the Mandela effect in human societies, in which many people recall the same false memory. In this paper, we systematically study false-belief dynamics through the lens of the Mandela effect, focusing on dissemination, persistence, and mitigation. Specifically, we build a controlled framework connecting agent communication, private memory, and a shared simulated web to mimic information dissemination in practical social networks, supported by 540 fabricated claims spanning 8 domains and 4 plausibility levels. For dissemination, we measure how widely and quickly claims gain acceptance across models and communication topologies. For persistence, we withdraw agents promoting fabricated claims while retaining existing articles and memories, and track subsequent acceptance. For mitigation, we compare preventive measures applied before implantation with corrective interventions introduced after claims have spread. Experimental results show that evaluated models readily accept and disseminate fabricated claims, with dissemination reach as high as 96.0%. Dissemination reach varies with claim plausibility, domain, and network size and composition, while communication topology shapes dissemination speed across model families. Acceptance barely declines after fabricators withdraw, and targeted corrective interventions outperform the preventive measures without eliminating false beliefs. These findings identify persistent false beliefs as a safety challenge for multi-agent social networks and highlight the need to address both their initial dissemination and the accumulated evidence that sustains them.

    Submission history

    From: Dianzhi Yu [view email]
    [v1]
    Sun, 11 Oct 2026 06:49:02 UTC (3,059 KB)
    [v2]
    Fri, 6 Nov 2026 07:01:22 UTC (3,054 KB)

    Belief Dissemination Effect false Lens Mandela Mitigation MultiAgent Networks Persistence Social
    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    admin
    • Website

    Related Posts

    Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration

    November 11, 2026

    [2605.08007] Interpreting Reinforcement Learning Agents with Susceptibilities

    November 11, 2026

    Probabilistic Graphical Modelling for Supervised Finetuning

    November 11, 2026
    Leave A Reply Cancel Reply

    Top Posts

    NVIDIA Is Investigating Reports That CEO Jensen Huang Was The Target Of Chinese Spy Efforts

    November 8, 202619 Views

    Shaping & Monitoring Models through their Internal Representations

    November 7, 20263 Views

    If AGI is so close, why is everyone leaving? The neolabs and their bets

    November 6, 20263 Views

    F1 in Brazil: Drivers at their very best in tricky conditions

    November 9, 20262 Views
    Don't Miss
    AI Tools & Software

    Judge Arena: Benchmarking LLMs as Evaluators

    By adminNovember 11, 20260

    LLM-as-a-Judge has emerged as a popular way to grade natural language outputs from LLM applications,…

    Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration

    November 11, 2026

    The First Multilingual LLM Debate Competition

    November 11, 2026

    [2605.08007] Interpreting Reinforcement Learning Agents with Susceptibilities

    November 11, 2026
    About Us

    Welcome to TechWave Dispatch

    At TechWave Dispatch, we bring you the latest news, insights, and developments from the ever-evolving world of technology. Our mission is to keep readers informed about the innovations shaping our digital future.

    From artificial intelligence and machine learning to cybersecurity, robotics, smart devices, and groundbreaking technologies, we cover the topics that matter in today's fast-moving tech landscape.

    Our Picks

    Judge Arena: Benchmarking LLMs as Evaluators

    November 11, 2026

    Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration

    November 11, 2026

    The First Multilingual LLM Debate Competition

    November 11, 2026
    Most Popular

    Ramp data shows AI spending stalling just as Anthropic nears its IPO

    November 6, 20260 Views

    AI Companies Really, Really Want to Buy Your Work Files and Emails

    November 6, 20260 Views

    Who Controls Scientific Discovery? – O’Reilly

    November 6, 20260 Views
    Facebook X (Twitter) Instagram Pinterest
    • Terms and Conditions
    • Privacy Policy
    • Contact Us
    • About Us
    © 2026 TechWave Dispatch. All Rights Reserved.

    Type above and press Enter to search. Press Esc to cancel.