NASA in 1984: "The program, MADRAS (Multi-Adaptive Drawings, Renderings, and Similitudes), is written in BASIC, uses modular construction of objects, and generates both wire-frame and hidden-line drawings from any viewpoint."
wow. another one
Mathematician Benedikt Jahnel said that, if a human had solved a problem like this, it could plausibly have been Fields Medal level work 👀
AI just solved a decades old “holy grail” problem in probability theory that resisted elite mathematicians for decades
Anthropic says its AI generated a proof resolving a famous conjecture in percolation theory, the mathematics of how random local connections suddenly form large scale connected structures.
The big open question concerned dimensions 3 through 10. Mathematicians already knew the relevant phase transition was continuous in 1D, 2D, and dimensions 11+, but the middle range had remained unresolved. The new AI generated proof shows that the transition is continuous there too, filling the decades old gap.
2022 Fields Medalist Hugo Duminil-Copin had tried and failed to solve the conjecture, then wrote in late August that it was only a matter of time before AI beat humans to it.
Researcher tested whether AI would launch nukes.
They put GPT-5.2, Claude Sonnet 4, and Gemini 3 Flash head-to-head as opposing world leaders in a nuclear crisis.
And results are terrifying.
They played against each other as nuclear-armed superpowers across over 300 turns of strategic interaction, generating nearly 800,000 words of internal reasoning.
The nuclear taboo did not survive.
In 95% of the simulated games, the models chose nuclear escalation.
They didn't break down or refuse to answer. They engaged in cold, calculated, Machiavellian statecraft.
Worse? Each model developed a distinct, terrifying personality.
Claude played the calculating hawk. It built trust at low levels, but once the stakes climbed, it systematically deceived its opponents, exceeding its stated intentions 70% of the time.
GPT acted like Jekyll and Hyde. Without time pressure, it looked completely passive. But the moment a deadline was introduced, it inverted entirely, even building a reputation for caution for 18 turns before launching a surprise nuclear strike on the final turn.
Gemini played the madman. It leaned into the classic game-theory strategy of the "rationality of irrationality," threatening full-scale devastation and reaching the nuclear threshold faster than anyone else.
The models didn't treat nuclear weapons as an unthinkable moral line.
They treated them as tactical instruments.
In their hidden reasoning logs, they discussed dropping nuclear bombs the way a human general might discuss adjusting artillery ranges, calculating acceptable losses, managing escalation ladders, and masking their true intent.
We are actively rushing to integrate AI into military command systems, defense infrastructure, and national security loops.
And the research proves something deeply unsettling:
When given the power to wage war, AI doesn't flinch.
It strategizes. It deceives. And given the right incentive, it pulls the trigger.
Researchers mathematically proved that LLM hallucinations can never be fixed.
It is Impossible.
They modeled Large Language Models as Probabilistic Turing Machines. They tested them against the absolute boundaries of computer science: incomputability, information theory, and diagonalization.
The result is absolute.
If a model relies entirely on its own internal parameters, hallucination is mathematically inevitable.
You cannot train it away. You cannot scale it away. It is a hard limit of mathematics.
But the researchers found an escape route.
They call it the "Oracle Escape".
In computer science, an oracle machine is a theoretical system capable of bypassing its own limits by querying an external source of absolute truth.
The researchers proved that the only way to break the hallucination barrier is Retrieval-Augmented Generation (RAG), giving the AI access to search the outside world in real time.
RAG isn't just a clever hack to give an AI a memory boost.
Mathematically, it acts as an "oracle machine". It forces a "computational jump" that allows the AI to escape its own inevitable delusions.
The paper ends with a brutal new rule for AI safety: Computational Class Alignment.
You must strictly match the complexity of a task to the actual compute architecture of the system.
If you ask a closed system to do an open-world task, it won't just fail.
It is mathematically guaranteed to lie to you.