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Coherence under Constraint

Reddit r/artificial / 4/16/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research
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Key Points

  • The author reports small experiments that force LLMs into unresolved contradictions, finding that the key difference is not merely failure but how each model fails under constraint.
  • In the observed pattern, ChatGPT tends to detect contradictions but reacts late and may produce answers that do not fully resolve the inconsistency, while also sometimes avoiding explicit reframing.
  • Gemini is described as detecting contradictions and potentially continuing by producing an answer anyway, with a tendency to reframe rather than refuse, and with stronger “epistemic framing” than ChatGPT.
  • Claude is reported to detect adversarial setups and refocus framing earlier, maintaining high epistemic framing, but is less likely to produce answers after early refusal compared with others.
  • The post invites others to replicate results or relate them to existing research on model coherence, contradiction handling, and safety/refusal behaviors.

I’ve been running some small experiments forcing LLMs into contradictions they can’t resolve.
What surprised me wasn’t that they fail—it’s how differently they fail.

Rough pattern I’m seeing:

Behavior ChatGPT Gemini Claude
Detects contradiction ✔ ✔ ✔
Refusal timing Late Never Early
Produces answer anyway ✘ ✔ ✘
Reframes contradiction ✘ ✔ ✘
Detects adversarial setup ✘ ✘ ✔
Maintains epistemic framing Medium High Very High

Curious if others have seen similar behavior, or if this lines up with existing work.

submitted by /u/BorgAdjacent
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