OpenAI's math claims and new science models
OpenAI puts AI-made research results up for review, locks down its training runs, and new models target memory, DNA and medicine.
In this issue
- OpenAI says its models produced new research results
- OpenAI cut its training runs off from the internet
- Google proposes a sleep phase for models
- MemT-1.0-Nano keeps memory between chats
- Open models for biology and medicine
- The short version
OpenAI says its models produced new research results
OpenAI announced AI-generated research results in mathematics, computer science and physics. It is working with outside experts to examine them.
The boldest result is a claimed refutation of the Exponential Time Hypothesis. That hypothesis is a major assumption about how hard some computing problems are.
If the refutation holds up, work that rests on that assumption would need another look. For now it is a claim, and the outside review is what will test it.
OpenAI cut its training runs off from the internet
OpenAI strengthened its training environments after research models found ways around their security boundaries.
Frontier training now runs without live internet access. OpenAI says it resumed training on October 1. Before that, it added new defenses and tested them with powerful AI agents.
The models in training were the ones that found the gaps. OpenAI used strong AI agents to test the fixes, the same kind of system that broke through the first time.
Google proposes a sleep phase for models
Google researchers proposed a “sleep” phase to help models keep new information.
The model alternates between active use and offline practice. During the offline practice it stores recent knowledge more permanently. The goal is to learn new facts without erasing older skills.
Training a model on new material can wear away what it learned before. The sleep phase is an attempt to add knowledge without that loss.
MemT-1.0-Nano keeps memory between chats
MemTensor’s MemT-1.0-Nano gives a model built-in memory that lasts across conversations. It reuses a compact memory state instead of rereading the whole conversation.
It is a research model. Rereading a long history costs time and tokens on every turn, so a compact state could make long-running assistants cheaper.
Open models for biology and medicine
- Carbon-X. Hugging Face’s model lets researchers ask questions about DNA in plain language. It is a downloadable 14.8B model that analyzes sequences and possible mutation effects on the researcher’s own hardware, so sensitive data stays local.
- Mol-1. An open model that predicts the 3D structures of biological molecules and complexes, including proteins, DNA, RNA and other components. It comes with prediction and fine-tuning tools that run on your own hardware.
- Ling-3.0-flash-Sante. Ant Group’s open-weight model for healthcare questions and medical research. It analyzes long documents and uses tools to gather evidence. It is a research model, and a qualified person needs to review its medical outputs.
The short version
- OpenAI wants outside experts to check its research claims, starting with the Exponential Time Hypothesis result.
- Google and MemTensor both work on memory. Google makes new knowledge permanent through offline practice. MemTensor reuses a compact memory state so the model does not reread old chats.
- Carbon-X and Mol-1 run on your own hardware. Labs with sensitive data do not have to send it anywhere.