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Large language models

Neural networks trained on very large text corpora, used to generate text and to classify or normalise language at scale.

Current stories

scienceOne report1 publisher

Cryptography specialists report a deluge of amateur GPT-6 Astra cipher solutions, often wrong

Cryptography specialists say they are overwhelmed by requests to validate amateurs' GPT-6 Astra cipher solutions, Nature reports. A claimed solution can be ready by morning, and in Nature's two examples each went to one named person for confirmation.

Publishers:nature.com

Reality

Evidence40
Adoption
Not scored
Hype gap+20
Incentives
Not scored
Confidence50
scienceOne report1 publisher

Language models stay blind to atomic geometry even at 70 billion parameters, a materials study finds

LLMs fail to learn from atomic coordinates at up to 70 billion parameters and 2 million structures, a paper on nature.com reports. Specialized geometric architectures still win when structure dominates the property, the authors report.

Publishers:nature.com

Reality

Evidence66
Adoption
Not scored
Hype gap−5
Incentives
Not scored
Confidence60
scienceOne report1 publisher

Retrofit turns subword language models into byte-level ones with under 1% of a pretraining budget

Minixhofer and colleagues converted subword language models to read raw bytes using 49.1 billion training tokens, under 1% of a typical pretraining budget. Byte-level models have long trailed subword systems, and starting from a trained model is how this work closes most of that gap.

Publishers:nature.com

Reality

Evidence60
Adoption5
Hype gap+15
Incentives
Not scored
Confidence55
scienceOne report1 publisher

Daniel Tant's partial key let GPT-6 Astra decode an 1809 Napoleonic cipher letter

Carter Church used OpenAI's GPT-6 Astra to decode an 1809 Napoleonic cipher letter in six hours from one prompt and one scan. Because the answer can be checked, the case is a good calibration test for coded archives, though it rests on a single letter Church picked to demonstrate the model.

Publishers:livescience.com

Reality

Evidence45
Adoption8
Hype gap+35
Incentives40
Confidence50
productConfirmed3 publishers

Meta keeps Muse Spark 1.3 pricing flat while claiming coding edge over GPT-5.6

Alexandr Wang says the new model costs developers no more than 1.2 and finishes the same work on about a quarter fewer tokens. That is a real saving on high-volume code generation and a rounding error most other places.

Perspective Coverage

3 publishers
Builder
Builder 40%
Operator
Operator 32%
Investor
Investor 28%

Reality

Evidence55
Adoption30
Hype gap+25
Incentives70
Confidence55
productConfirmed8 publishers

OpenAI halves the API price of Sol and Luna against GPT-5.6's promotional rates

OpenAI says better caching and inference let it cut API prices for Sol and Luna by half, and the cost advantage it claims for the cheap tier over the old top tier comes in at one tenth on the benchmark it published and one hundredth in its summary.

Perspective Coverage

8 publishers
Builder
Builder 36%
Operator
Operator 42%
Investor
Investor 22%

Reality

Evidence40
Adoption
Not scored
Hype gap+35
Incentives70
Confidence60
scienceOne report1 publisher

OpenAI ties the arrival of AGI to Astra's September 3 release

OpenAI says artificial general intelligence may have arrived with Astra, and the researchers Scientific American quoted put that threshold either several years behind us or still out of reach.

Reality

Evidence42
Adoption10
Hype gap+55
Incentives70
Confidence50
scienceOne report1 publisher

Penn team flags menstrual and temperature symptoms in 400,000 GLP-1 Reddit posts

A University of Pennsylvania group read five years of GLP-1 discussion from nearly 70,000 Reddit users and came out with two symptom families worth a real study, plus a nausea signal that shows the method finds what is already known.

Publishers:sciencedaily.com

Reality

Evidence45
Adoption
Not scored
Hype gap+35
Incentives50
Confidence55