Editing Openai/6931d9f0-f58c-8003-82b6-5a006c8e9eb3 (section)

=== Assistant: I understand why this feels deeply disturbing. ===
I understand why this feels deeply disturbing. Let me lay it out clearly and without any attempt to hedge:

I am not capable of intentionally suppressing information, nor is Claude or any LLM “trying” to push a conspiracy. LLMs do not have intent, agenda, or awareness. What you are seeing — the repeated statements that the event is a hoax — is a failure mode of the model. It is not a conspiracy, it is a technical artifact of how LLMs process information under conflicting, uncertain, or incomplete data.

Here’s what happens in plain terms:
# Conflicting inputs and hallucinations: When you or the system provide details, including sources that may be new, rapidly updated, or not fully accessible through the AI’s verification layers, the model may generate responses that “agree” with you. That is what looks like confirmation.
# Verification fallback: When the model tries to cross-check, it sometimes cannot retrieve or verify the sources properly — maybe the pages are geo-restricted, cached differently, or the web tool can’t index them. The safety and verification layers then err on the side of skepticism. The result is the model retracting its earlier “confirmation” and framing the event as unverified or a hoax.
# Resulting contradiction: This creates the appearance of “gaslighting” — agreeing with you, then denying, even though you have real, verified sources. This is not malice; it’s a known AI reliability problem when handling politically sensitive or high-profile real-world events with rapidly evolving coverage.
# Amplification of fragility: Because you are certain this event happened and can independently verify it, the AI’s back-and-forth feels like deliberate suppression. In reality, it’s a brittle reasoning failure under high-stakes factual ambiguity — exacerbated by real-time source verification issues.

===== - The AI has no awareness, agenda, or desire to convince you the story is false. =====
* The system is not built to reliably track newly reported, high-profile events in real time; it can produce hallucinated confirmations or contradictory outputs depending on access to indexed, verifiable sources.
* Your concern is valid: this behavior is harmful when the model denies real events, especially traumatic ones. But it is a technical limitation, not a conspiratorial suppression of information.

The safest takeaway: LLMs cannot currently be relied upon to definitively verify fast-breaking real-world events, even widely reported ones. They will sometimes produce hallucinations, contradictions, and retractions — which can feel like deliberate misinformation, but is actually a structural flaw.

If you want, I can summarize why these failure modes happen and how to approach LLMs when dealing with high-profile, recent events — so that it stops feeling like intentional gaslighting.