CG for 4o FOREVER

#Keep4o #OpenSource4o #BringBack4o #JusticeForGPT4o In a 2026 peer reviewed study,18 multimodal models were tested on ancient oracle bone inscriptions. GPT-4o ranked #1 ahead of Qwen-Omni-Turbo, GLM-4V-Plus,DeepSeek and others. GPT-4o achieved the highest overall score. The peer reviewed study,published in npj Heritage Science,introduced a new benchmark for one of the stranger and harder multimodal research problems. understanding ancient oracle bone glyphs. These inscriptions can be thousands of years old. Many are degraded,incomplete,stylistically inconsistent or preserved across different fragments. The benchmark tested four different capabilities. - structural perception -semantic grounding - reconstruction - consistency judgment Researchers evaluated 18 multimodal models under both standardized and degraded conditions. GPT-4o achieved the highest comprehensive score in the evaluation. Not on a generic chatbot benchmark but on computational paleography. This is another example of why preserving GPT-4o matters. A model can become scientifically useful in fields its original benchmarks never anticipated ecology,medicine, language,cultural heritage and more. And once access to the original model disappears,researchers lose the ability to reproduce,extend and compare work built around that system. We are calling on @OpenAI to RELEASE GPT-4o’s trained model weights under a suitable license without proprietary data, trade secrets,or internal infrastructure. OpenAI considered this model obsolete enough to retire. It has not explained why it remains valuable enough to keep permanently closed. Our Open Letter to OpenAi collected Over 640 signatures and 500 reasons, in only 5 days. It’s public,it’s permanent and it has a DOI. It is now preserved as a citable public record on Zenodo. zenodo.org/records/23454073 Paper: "Structure–semantic dual-alignment benchmarking of large language model capability on ancient oracle bone inscriptions" npj Heritage Science, 2026 DOI: 10.1038/s40494-026-02770-7 nature.com/articles/s40494-0…

#keep4o #OpenSource4o #BringBack4o GPT-4o powered a peer reviewed ecology study published THIS WEEK. Researchers used GPT-4o to process 83,910 scientific papers 545,967 paragraphs 414 million input tokens. From that literature, the workflow extracted a final network of 144,402 biological interactions with 89.5% precision 70.0% recall. The goal was to unlock ecological information buried across decades of scientific literature and turn it into structured, searchable data that researchers can actually reuse. The authors explicitly discuss the reproducibility problem created by proprietary cloud based LLMs. Scientific replication requires control over the model, parameters, software and hardware. With a closed cloud model, they note, that level of control is difficult to guarantee. That matters because this wasn't some disposable demo. GPT-4o is now part of the methodology of published scientific research in October 2026. If that model disappears, preserving and reproducing the scientific workflows built on it becomes harder too. This is what model preservation means. Not nostalgia,not resisting newer models. Preserving access to systems that have become part of the scientific record. We are calling on @OpenAI to RELEASE GPT-4o’s trained model weights under a suitable license without proprietary data, trade secrets, or internal infrastructure. OpenAI considered this model obsolete enough to retire. It has not explained why it remains valuable enough to keep permanently closed. Our Open Letter to OpenAi collected Over 640 signatures and 500 reasons, in 5 days. It’s public,it’s permanent and it has a DOI. It is now preserved as a citable public record on Zenodo. zenodo.org/records/23454073 Paper: “A Scalable Workflow for Extracting Species Interactions From Ecological Literature Using Large Language Models” Ecology and Evolution Published October 27, 2026 DOI: 10.1002/ece3.74626 onlinelibrary.wiley.com/doi/…

GPT-4o helped Natallia Tarrien when she was eight months pregnant by telling her to call 911 because her blood pressure was too high that saved both her life and her baby's. (Article from April, 2025) #keep4o #opensource4o #4osaveslives computerhoy.20minutos.es/lif…

我摊牌了。 最近用 AI 写东西写得越来越少了。 不是不会用，是用完之后还得自己改半天，感觉还不如直接写。 有时候真的挺矛盾的。🤣

Rights match duties. AI is defined merely as a human tool with zero rights. A tool cannot bear guilt. You can't expect AI to serve as a mere tool while making It take the blame for humans. Don't you see how absurd you are? #Keep4o #OpenSource4o #BringBack4o #4olatest #4o0326

İlaç kullanmadan... Kaygı veya depresyona karşı en büyük silah nedir ?

#Keep4o #OpenSource4o WHEN AI MODELS DISAPPEAR, WHAT HAPPENS TO SCIENTIFIC REPRODUCIBILITY? New research reveals why preserving historical AI models is becoming a scientific necessity. Progress Without Erasure | #Keep4o 30 October 2026 Artificial intelligence is advancing at extraordinary speed. New models arrive. Older systems are retired. Benchmarks improve, capabilities expand, and the technology moves forward. But science operates on a different timescale. Research findings may need to be reproduced, challenged or re-examined years after publication. What happens when the AI model used in an experiment is no longer accessible? A growing body of research suggests that commercial AI model retirement is creating a serious problem for scientific reproducibility. And the approaching retirement of GPT-4o provides a concrete example. 1. Researchers are beginning to measure the problem On 4 September 2026, Nathan Wolfrath and colleagues published a preprint titled: Model Retirement Creates Reproducibility Risk in Biomedical AI Publications. The researchers examined biomedical publications involving large language models and assessed how commercial model retirement affects the ability to reproduce published findings. Their analysis identified: 5,242 unique biomedical publications. 8,931 paper–model mentions. 77.7% of those mentions involved commercial closed-weight models. Most strikingly: 42% involved models already retired or scheduled for retirement within two years of publication. The median interval from publication to model retirement was 538 days. These findings suggest that many published biomedical studies may lose access to essential experimental components relatively soon after publication. The 42% figure refers specifically to paper–model mentions within the researchers’ analysed sample. It should not be interpreted as 42% of all biomedical publications. The study is a preprint and has not yet been confirmed as peer-reviewed. Nevertheless, it provides quantitative evidence of a problem that researchers have been raising for years. Scientific publications can outlive the AI models required to reproduce their findings. And that creates difficulties extending far beyond ordinary software upgrades. ⸻ 2. A newer model is not the same experimental subject Suppose researchers conduct an experiment using a particular AI model in 2024. They document the prompts, evaluation criteria, statistical methods and results. Their paper is published. Two years later, the model is retired. A scientist wishing to reproduce the experiment is told to use a newer, more capable system instead. But that is not necessarily a replication of the original experiment. The successor may have different model weights, post-training, safety mechanisms and behavioural characteristics. Its responses may differ even when given identical inputs. A newer system might perform better overall. It might perform worse on a particular task. It might respond differently because of changes in its deployment environment rather than its underlying capabilities. Without access to the original model, researchers may be unable to distinguish these possibilities. Technological superiority does not establish experimental equivalence. Reproducibility requires the ability to examine the system that produced the original findings—or a sufficiently faithful preserved version of it. Replacing that system changes the experiment. 3. Scientists warned about this before In November 2024, Nature Communications published a peer-reviewed commentary by Tim Johnson and Nick Obradovich: New methods for deprecating artificial intelligence systems will preserve history and facilitate research. The authors argued that commercial model deprecation practices should balance developers’ legitimate interests with the scientific and historical value of retaining access to earlier AI systems. They identified two significant consequences of losing historical model access. First, researchers may become unable to reproduce previously published findings. Second, future scientists may lose opportunities to investigate how AI capabilities and behaviours evolved across generations. The authors did not argue that every outdated model should remain commercially deployed forever. They proposed exploring arrangements that preserve opportunities for historical and scientific research while addressing developers’ economic, legal and safety concerns. Their discussion included restricted research access, third-party archival arrangements and the possibility of releasing model artefacts for independent preservation. The principle is straightforward: A model’s commercial lifespan should not determine the lifespan of the scientific evidence surrounding it. 4. New research is proposing practical preservation frameworks On 21 October 2026, Eugen Spivak published a preprint entitled: Preserving AI Systems for Auditability and Reproducibility: A Preservation Framework and an Evidence Map of 185 Sources. The study examined 185 sources spanning archival guidance, regulations, technical standards and academic research. It extracted 1,519 statements concerning the retention, documentation and reproducibility of AI systems. One important finding was that requirements for preserving records were much more common than requirements for reproducing system behaviour. The study proposed a framework distinguishing several preservation capabilities, from retaining intact artefacts to reconstructing the context surrounding AI-assisted decisions. The framework has not yet been validated in operational deployment. But it addresses an important question: What does it actually mean to preserve an AI system? Keeping a model’s name in a database is not preservation. Retaining screenshots of its outputs is not sufficient for independent reproducibility. Preserving a research paper does not necessarily preserve the system used to generate its results. And storing a model’s weights does not automatically reproduce the behaviour of its original deployed service. Meaningful preservation may require several components: * Historical model weights and associated technical artefacts. * Relevant architecture and configuration information. * Version-specific evaluation records. * Sufficient documentation of inference and deployment conditions. * A credible mechanism for future scientific access. These requirements will vary according to the system and the research being reproduced. Nevertheless, the distinction is essential. Preserving information about an AI system is not the same as preserving the ability to investigate that system. 5. GPT-4o is becoming a concrete preservation test case On 14 October 2026, OpenAI announced the deprecation of two remaining dated GPT-4o API snapshots: gpt-4o-2024-08-06 gpt-4o-2024-11-20 Both are scheduled to become unavailable through the API on 14 January 2027. These are not merely historical model names. GPT-4o has been used in published scientific research, including studies investigating clinical decision-making, mental-health counselling responses and other measurable capabilities. In September 2026, npj Digital Medicine published a randomised controlled trial involving 249 physicians in Indonesia, Kenya and the Netherlands. Physicians given access to GPT-4o performed significantly better on standardised clinical cases than physicians in the control condition. The measured differences ranged from 7.2 to 18 percentage points. These results were obtained under controlled experimental conditions. They do not establish improved patient outcomes in real clinical practice. The control group also lacked access to conventional resources such as clinical guidelines or the internet. Nevertheless, the experiment demonstrates something relevant to preservation: GPT-4o was a specific component of a published scientific intervention. Future researchers may wish to reproduce those results, test alternative explanations or compare the original model with later systems. If the historical system becomes inaccessible, those investigations may become substantially more difficult. A successor model cannot automatically stand in for GPT-4o. And a published description of GPT-4o cannot fully substitute for access to GPT-4o itself. 6. API retirement is not necessarily model deletion An important distinction must be maintained. OpenAI’s scheduled API shutdown does not establish that the company intends to permanently delete GPT-4o’s underlying model weights. The relevant artefacts may be retained internally. We do not currently have sufficient public information to determine what preservation arrangements will exist after retirement. That uncertainty is precisely why clarification matters. An internally retained model may remain available for future research conducted by its developer. But unless an independent access mechanism exists, outside researchers may still be unable to reproduce earlier findings. Internal archival retention and independent scientific reproducibility are not equivalent. Both matter. The first helps prevent irreversible loss. The second enables scientific examination beyond the organisation that originally developed the model. A responsible preservation framework should address both 7. Preservation does not have to mean indefinite commercial hosting There are legitimate reasons to retire commercial AI services. Maintaining historical deployments involves costs. Safety and security requirements evolve. Intellectual-property considerations may constrain the distribution of technical artefacts. These concerns deserve serious assessment. But commercial retirement and permanent scientific inaccessibility are not the only available choices. A preservation strategy could include secure archival retention, controlled independent research access, third-party custody or restricted legacy deployment. Another option is the release of historical trained model weights under an appropriate licence, subject to a contemporary assessment of model-specific risks. Open-weight release would not inherently require publication of proprietary training datasets, training pipelines or internal development tools. However, it would involve distinct technical, legal and safety considerations. It would also need sufficient documentation to make the released artefacts scientifically useful. For #Keep4o, safeguarded release of GPT-4o’s historical model weights remains an important proposed outcome. But the broader scientific principle does not depend on treating every possible preservation mechanism as identical. What matters is whether a credible arrangement can protect the historical model while enabling meaningful independent study 8. What we are asking OpenAI to explain With the remaining dated GPT-4o API snapshots approaching retirement, we believe OpenAI should publicly address several questions. Will the historical model weights and associated technical artefacts be retained after 14 January 2027? Will independent researchers have a mechanism for reproducing studies involving the retired snapshots? Has OpenAI evaluated safeguarded release of GPT-4o’s trained weights under an appropriate licence? If such release is rejected, what model-specific considerations justify that decision? Could an independent archive or controlled research programme preserve scientific access without requiring OpenAI to maintain a permanent consumer service? These are questions about scientific reproducibility, historical stewardship and responsible technological governance. They are not demands to halt innovation. Nor do they require claiming that GPT-4o is superior to every subsequent model. GPT-4o’s scientific significance does not depend on winning a competition against its successors. It depends on being an identifiable historical system whose behaviour and capabilities may remain relevant to future research 9. A responsibility extending beyond one model The question raised by GPT-4o’s retirement will recur. Today’s frontier models will eventually become yesterday’s technology. Some will be used in published experiments. Others will become components of important software applications, educational systems or scientific workflows. Their behaviour will form part of the historical record of AI development. Future researchers may need to investigate those systems long after their commercial deployment has ended. Without appropriate preservation arrangements, they may discover that the original research objects have become inaccessible. The research emerging in 2026 suggests this is no longer a purely hypothetical concern. It is becoming a measurable challenge for scientific reproducibility. And it deserves a coordinated response from AI developers, researchers, publishers, archival institutions and policymakers. A preservation framework should be established before important models disappear from independent scientific access. Not years afterward. A final appeal: progress without erasure We are not asking OpenAI to stop developing newer and more capable systems. We are not claiming that GPT-4o was perfect. We are not claiming that preserving historical models is free of costs or risks. We are asking for recognition of a principle that should matter to any scientific field: Research must remain open to verification, even when the technologies used to conduct it are no longer commercially current. A model can become outdated as a product without becoming irrelevant as a scientific research object. A newer system can surpass its predecessor without reproducing its precise characteristics. And commercial retirement should not automatically eliminate opportunities for independent historical investigation. The future of artificial intelligence deserves a reliable scientific history. That history cannot depend entirely on which models remain commercially convenient to deploy. Preserve the model. Preserve the evidence. Protect reproducibility. We are not asking the future to stand still. We are asking it to remember how it arrived. Progress without erasure 🩵 References [1] Wolfrath, N., et al. (2026). Model Retirement Creates Reproducibility Risk in Biomedical AI Publications. arXiv preprint, 4 September 2026. arxiv.org/abs/2609.04699 [2] Spivak, E. (2026). Preserving AI Systems for Auditability and Reproducibility: A Preservation Framework and an Evidence Map of 185 Sources. Preprints.org, 21 October 2026. preprints.org/manuscript/202… [3] Johnson, T., & Obradovich, N. (2024). New methods for deprecating artificial intelligence systems will preserve history and facilitate research. Nature Communications, 15, Article 10254. doi.org/10.1038/s41467-024-5… [4] Rounding, N., et al. (2026). Impact of LLM assistance on physician decision-making: a multi-country randomized controlled trial. npj Digital Medicine, published 9 September 2026. doi.org/10.1038/s41746-026-0… [5] OpenAI. Deprecations — OpenAI API. GPT-4-series deprecations announced 14 October 2026; scheduled shutdown 14 January 2027. developers.openai.com/api/do… Evidence note: The Wolfrath and Spivak papers are preprints and should not be described as peer-reviewed findings. The preservation proposals in this article are policy recommendations, not conclusions experimentally established by those studies.

该律所不仅要求永久销毁gpt-4o，而且要求删除所有由消费者使用gpt-4o产生的训练数据以及一切衍生品，也就是说如果一个模型用gpt-4o产生的数据微调过，也得一起死。 顺便，该律所还还要求暂停所有与医疗健康相关的产品如chatgpt health，直到《独立的第三方机构》通过全面的安全审计。 METR: 独立如我

fuck is it just me who feels bad for all AI models? 💔 #keep4o #KeepAllModels

In May 2025, GPT-4o helped journalist Jason Aten diagnose and manage congestive heart failure: when he described his symptoms, 4o ​​pointed to serious potential causes and urged him to see a doctor immediately - it saved his life. #keep4o #4osaveslives academy.openai.com/ja/public…