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ARXIV:2605.14218 · AI SAFETY · SUBMITTED 15 MAY · 20:13 UTC · FRESHNESS FRESH
ARXIV:2605.14218AI SAFETYSUBMITTED 15 MAY · 20:13 UTCFRESHNESS FRESHNeil F. Johnson · Frank Yingjie Huo · arXiv
A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal.
Opportunity summary
Pain A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment…
The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes --…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future…
AI Safety moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal.
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10.48550/arXiv.2605.14218A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal.
Abstract
The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior. The shift condition, which is also derivable mathematically, results from group-level competition between the conversation-so-far (C) and the desirable (B) and undesirable (D) basin dynamics which can be estimated in advance for a given application. It is neither model-specific nor driven by stochastic sampling. We validate it across six independent tests, including: 90 percent correct across seven AI models spanning two orders of magnitude in parameter count (124M-12B); production-scale persistence across ten frontier chatbots; and a priori time-stamped prediction eleven months before the Stanford 'Delusional Spirals' corpus appeared, and independently confirmed by that corpus of 207,443 human-AI exchanges. Because it sits architecturally below the current safety stack, the same formula provides a real-time warning signal that current alignment does not supply, portable across current and future ChatGPT-like AI architectures and instantiable in application domains where competing response classes can be defined.
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PROBLEM
A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards.
METHOD
The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts per...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior.
WHY NOW
AI Safety moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Safety moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A predictive model for forecasting when AI behavior will shift to undesirable outcomes, offering a real-time warning signal.
Segment
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Commercial read
4.0/10 public viability
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