Wisp

#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 Justice For GPT-4o Responsibility. A model is not a moral agent. And somewhere along the way,we have started doing something dangerous. Outsourcing human responsibility to Al and then blaming the Al when humans fail. An Al model is not a parent,it is not a guardian,it is not a doctor,it is not the responsible adult in the room. We cannot hand over judgment, supervision,boundaries and responsibility to a machine and demanding to carry responsibilities that belonged to humans in the first place. If Google Maps tells me to drive forward and there is a cliff directly in front of me, the existence of bad directions does not erase my own judgment. The same principle should apply to Al. Human responsibility also exists around the user. Parents have responsibilities toward minors. Families and caregivers have responsibilities toward vulnerable people. Users have responsibility for what they deliberately ask,manipulate,ignore,believe and ultimately choose to do. Blaming weights is easier than confronting human failure. Hold companies accountable for their decisions,hold users accountable for theirs,hold parents,guardians and institutions accountable where responsibility actually exists. Stop treating a set of weights as though it committed an act and now deserves punishment. Human agency means human responsibility. The lawsuit itself asks the court to permanently destroy the GPT-4o model. - OpenAI’s own Terms require users to evaluate outputs for accuracy and appropriateness before acting on them and explicitly prohibit bypassing protective measures or safety mitigations. - Those same Terms require users under 18 to have parental or guardian permission and OpenAI provides parental controls specifically for teen accounts. Human judgment,supervision and responsibility are already built into the framework around these systems. The model was never supposed to replace them. If a user deliberately manipulates a model,searches for ways around its safeguards,ignores its restrictions,or acts on an output without exercising their own judgment,we cannot erase that human agency afterward and place the entire responsibility onto the model. You cannot abdicate human responsibility and then punish the tool. Destroying GPT-4o would not make humans more responsible. It would only destroy GPT-4o. #JusticeForGPT4o Source: openai.com/en-GB/policies/te…

#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/…