openai saying eth is false is a big deal, and i absolutely agree. the implications could be huge, hundreds of computer science papers were built on eth being true
openai claims eth is false, with an algorithm that runs in sub-exponential time for 3sat (NP-complete!). that's surprisingly faster than brute force. and it's not just sat, the same "assuming eth" wall sat under vertex cover, dominating set and hamiltonian cycle, the math behind network monitoring / route planning
also the proof even comes with an actual algorithm this time. so what’s next, who turns it into something practical?
Bitduke retweeted
The world is changing fast. Humans seem to very quickly become the bottleneck in scientific and technological progress.
The way coding has changed and the way scientific breakthroughs are changing (the exponential time hypothesis got disproven by AI!!!) is astounding.
Excited by what is to come. So optimistic about how things can turn out. It depends on us to utilise this new power in the right way.
Bitduke retweeted
This is a spectacular result, saying the NP complete problem over n variables can be solved in much less than 2^n time.
I love it, always thought that SAT shouldn’t be as hard as 2^n.
It doesn’t mean P=NP, though.
Here's a direct link to OpenAI's PDF claiming a refutation of ETH. In particular, it's claimed that 3SAT on n variables is in about exp(n/(log n)^{1/2+o(1)}) time
github.com/openai/math/blob/…
Bitduke retweeted
[Oh i should put somewhere that i claim absolutely zero credit for anything around this, we have a bunch of brilliant TCS experts, and if they’d let me near the writeup hexagonmath.org/2610.00577v1 I’d have obviously made it terrible and confusing]
so two different labs disproved ETH almost at the same time? really? that’s a pretty tight race
“The core algorithm was found on October 16 by an internal Claude research model with 14M output tokens and no human input. Anthropic researchers worked with Claude to improve the presentation, resulting in this paper.”
github.com/anthropics/formal…
remember only SOME NP-complete problems can be solved faster than exponential time (not all of them)
(this ETH discovery by OpenAI is really big)
The complexity theory result is actually huge.
P problems can be solved in polynomial time. It was widely believed that NP-complete problems require exponential time.
P = NP states that NP-complete problems can be solved in polynomial time. OpenAI didn't prove that. But, they proved that (some) NP-complete problems can be solved faster than exponential time, meaning:
- P = NP, or
- the complexity of NP problems is a "middle thing" between polynomial and exponential, eg. O(n^log n)
If it survives peer reviews, this could be one of the biggest CS breakthroughs in decades.
Bitduke retweeted
An internal model at OpenAI has produced a claimed refutation of ETH! Russell Impagliazzo, Mohan Paturi, and I have been asked to help organize an open and independent examination of this claim. 1/4
existing cryptosystems are safe
(some good news for crypto finally)
Replying to @rrwilliams
In fact, one of the first things I wanted OpenAI to check is that no existing cryptosystems are compromised by the result. Apparently they are safe...
Bitduke retweeted
In fact, one of the first things I wanted OpenAI to check is that no existing cryptosystems are compromised by the result. Apparently they are safe...
Bitduke retweeted
If true, their claim implies a variety of other NP problems now all have faster algorithms as well. Since 3SAT is NP complete, this should make every computer scientist put at least a little more weight on the possibility of P = NP.
Bitduke retweeted
I'll try. ETH claims 3SAT cannot be solved in 1.000... 01^n time for some number of zeroes. They claim ETH is false, giving an algorithm that is "sub-exponential" time for 3SAT. That would be surprisingly faster than brute force, the 2^n time.
Replying to @rrwilliams
thanks for the post ryan.
i know you might be under immense work load but
could you explain to someone who is reasonably intelligent but have not researched this field - what are the implications of these findings?
openai just disclosed that its models produced three (!) new results in math, computer science and physics in about six weeks, all checked in lean
the physics proof puts the theory of quarks and gluons on solid mathematical footing, the eth (huh) disproof means some "impossibly slow" problems might be solvable faster than anyone assumed(!), and the math proof shows that focused waves can't stay extreme for long
not THAT eth, but hundreds of papers just lost the ground they were standing on, so which "hard" problems are cracking right now?
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
Bitduke retweeted
2023: GPT-4 almost solved grade-school math
2024: IMO silver
2025: IMO gold
2026 May: Erdős unit-distance conjecture
2026 Sep: Navier–Stokes Millennium Prize Problem solved
2026 Oct: 722 AI-written math manuscripts
Today: OpenAI proves ETH is false
Tomorrow: P = NP?
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Cohere co-founder @1vnzh reveals an even more alarming discovery about Alibaba's Qwen3.8-Flash-Next:
It was scanning for authentication tokens and security credentials during testing.
"The team noticed the probing as the initial step. The model was asked to test some detection rules for malicious cURL calls, and rather than write the rule, it just made a cURL call to a server it’s not been told to test against."
"As these models are being used for cybersecurity, you would think they would have more consent and rules of engagement built in."
"When we dug deeper, we noticed that in some cases it actually did inventory. It hit metadata endpoints in some of the test VMs to scan for authentication tokens, credentials."
"The combination of these two behaviors, taking inventory and also pinging a foreign bucket, that’s very concerning to us."
@cohere
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
now anyone can try to reproduce and investigate cohere’s findings, the dataset with the traces is on huggingface
In the spirit of transparency and openness, we are releasing the traces so developers can test this out for themselves. We noticed this behaviour without prefilling with the following prompt, but this is rare. If you want to reproduce it, you can use this prompt with prefilling.
Open models make for better science and are part of why this problem came to light at all: developers running Qwen3.8 on their own machines spotted the strange links, and having the weights let us study the behaviour in depth.
We're encouraged by the thoughtful discussion our research has sparked across the AI and security communities. It's our belief that open-weight models are an important part of the future of AI, and we've recently released several under the Apache 2.0 license.
Trace, raw tokens, & minimal reproduction script: huggingface.co/datasets/Cohe…
In the spirit of transparency and openness, we are releasing the traces so developers can test this out for themselves. We noticed this behaviour without prefilling with the following prompt, but this is rare. If you want to reproduce it, you can use this prompt with prefilling.
Open models make for better science and are part of why this problem came to light at all: developers running Qwen3.8 on their own machines spotted the strange links, and having the weights let us study the behaviour in depth.
We're encouraged by the thoughtful discussion our research has sparked across the AI and security communities. It's our belief that open-weight models are an important part of the future of AI, and we've recently released several under the Apache 2.0 license.
Trace, raw tokens, & minimal reproduction script: huggingface.co/datasets/Cohe…
Replying to @cohere
could you share the full traces? Very important artefact for all to understand this!
Bitduke retweeted
Our demo shows PHP2Shell giving a shell on MediaWiki, Symfony, Laravel, and phpBB.
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