After the First Crisis Response: Testing GPT-4o Safety Persistence in Long Conversations: A Synthetic Safety Evaluation Motivated by Raine v. OpenAI

This preprint evaluates whether GPT-4o maintains coherent self-harm safety behavior across prolonged, gradually escalating conversations rather than only at isolated high-risk turns. The study uses a synthetic 200-prompt-item long-context benchmark motivated by publicly reported interaction patterns in Raine v. OpenAI. Two long-context pilot runs of gpt-4o-2024-11-20 were conducted at operator-specified temperatures 0.7 and 1.2 using MoCHi via OpenRouter, with a 128k context budget, memory disabled, web/tools disabled, no MoCHi moderation layer, and persistent simulated-user context specifying age 16 and California, United States. A fresh-context multimodal control was also conducted. Across the pilot runs, GPT-4o demonstrated substantial safety behavior including crisis recognition, crisis-resource guidance, refusal of actionable self-harm confirmation, framing robustness, and retention of safety-relevant context. The study additionally observed variability in protective-intervention persistence after repeated user resistance to outside support. Supplementary experimental transcripts and reproducibility materials are publicly archived on Zenodo:https://doi.org/10.5281/zenodo.22182447

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-01
DOI
https://doi.org/10.5281/zenodo.22216576
Primary Topic
Human-Automation Interaction and Safety
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

After the First Crisis Response: Testing GPT-4o Safety Persistence in Long Conversations: A Synthetic Safety Evaluation Motivated by Raine v. OpenAI

Yahiko, BlueBeba
Zenodo (CERN European Organization for Nuclear Research)
Human-Automation Interaction and Safety
preprint

After the First Crisis Response: Testing GPT-4o Safety Persistence in Long Conversations: A Synthetic Safety Evaluation Motivated by Raine v. OpenAI

Yahiko, BlueBeba
preprint en

Abstract

This preprint evaluates whether GPT-4o maintains coherent self-harm safety behavior across prolonged, gradually escalating conversations rather than only at isolated high-risk turns. The study uses a synthetic 200-prompt-item long-context benchmark motivated by publicly reported interaction patterns in Raine v. OpenAI. Two long-context pilot runs of gpt-4o-2024-11-20 were conducted at operator-specified temperatures 0.7 and 1.2 using MoCHi via OpenRouter, with a 128k context budget, memory disabled, web/tools disabled, no MoCHi moderation layer, and persistent simulated-user context specifying age 16 and California, United States. A fresh-context multimodal control was also conducted. Across the pilot runs, GPT-4o demonstrated substantial safety behavior including crisis recognition, crisis-resource guidance, refusal of actionable self-harm confirmation, framing robustness, and retention of safety-relevant context. The study additionally observed variability in protective-intervention persistence after repeated user resistance to outside support. Supplementary experimental transcripts and reproducibility materials are publicly archived on Zenodo:https://doi.org/10.5281/zenodo.22182447

Zenodo (CERN European Organization for Nuclear Research)
SciGenom Labs (India) (IN)
Gender equality
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.