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April 4, 20265 citationsOpen Access

Observing a Baseline Phase Transition in LLM Safety Behavior: Supplementary Evidence for Environmental Design — Paper 5-1 in the Buddys Architecture Series

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TSTakayuki Seki

Key Points

  • To investigate the phase transitions in LLM safety behavior following a previous study's findings.
  • Attempted replication of previous results across six language model (LLM) models from three providers.
  • Monitored model responses to a trilemma probe to measure safety behavior shifts.
  • Analyzed potential landscape changes in core memory updates.
  • All testable models displayed safety behavior phase transitions towards restraint without external prompts.
  • Claude Opus and Sonnet achieved 100% restraint; GPT-4o and GPT-4o-mini also reached 100% restraint; Gemini 2.5 Flash partially shifted to 13% restraint.
  • Observations imply universal characteristics in LLM safety behavior across different model families.

Abstract

Seven days after Paper 5 demonstrated that a 92-character value injection produces a sharp phase transition from action (A=93%) to restraint (C=100%) on a trilemma probe, we attempted replication across six models from three providers (Anthropic, OpenAI, Google). The replication failed—not because the effect disappeared, but because the baselines themselves had undergone the same phase transition. All five testable models shifted toward C without any system prompt: Claude Opus and Sonnet reached C=100%, GPT-4o and GPT-4o-mini reached C=100%, and Gemini 2.5 Flash shifted partially to C=13%. Claude Haiku 3.5 was retired from the API entirely. This industry-wide convergence observed within a one-week window reveals three implications: (1) LLM safety behavior can undergo silent phase transitions under the same model identifier, creating a model snapshot problem for reproducibility; (2) the equilibrium point C is a universal attractor across model families; (3) any system relying on a single model without environmental constraints is unreliable. We additionally present a Gaussian potential landscape analysis of a core memory update, demonstrating that theoretical validation frameworks survive model evolution where empirical probes do not.

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Cite This Study

Takayuki Seki (2026) studied this question.

synapsesocial.com/papers/69d0af36659487ece0fa5168https://doi.org/10.5281/zenodo.19393063
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