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Einat waxman

@einatwaxman

2 months ago

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stimpy2077

Photo by Einat waxman on September 12, 2026. May be an image of chocolate milk and text.

Photo by Einat waxman on September 12, 2026. May be an image of poster and text that says 'to Agia welcome Napa Nap Na Park Sculpture Musan Mижcии Agia Napa Monastery Лb6RbиR ะ'.

Photo by Einat waxman on September 12, 2026. May be an image of text.

Photo by Einat waxman on September 12, 2026. May be an image of beach, coast and text.

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Posted On: September 12th 2026, 04:13 pm

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@stimpy2077

Aya Napa

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romy_galmor

2 months ago

romy_galmor

😍😍

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Linköping University professor Peter Jonsson is set to have his work presented in two weeks’ time at one of the most prestigious conferences in theoretical computer science. As of Monday, much of that work is “dead,” he says, after AI showed that a 27-year-old hypothesis underpinning it is wrong.

Both OpenAI and Anthropic claimed this week to have disproved the exponential time hypothesis (ETH). Vast swaths of theoretical computer science research on complexity and algorithm design have been built on the premise that the effort it takes to solve certain problems increases exponentially with the size of the problem.

Jonsson expects his predicament will be “very common” at the IEEE Symposium on Foundations of Computer Science, where his work will be presented by one of his coauthors. “People will start the talks: ‘ETH is false, half of my paper does not work,’” he says. It’s a vexing problem, Jonsson says, “but at the same time, we learn a lot from it. And this is what science is about.”

Researchers haven’t independently verified, let alone had time to fully understand, the AI-produced proofs, which both firms have published on GitHub in both natural language and its machine-checkable Lean format. OpenAI has asked the UC Santa Barbara computer scientists who originally proposed the hypothesis, plus an MIT academic who has refuted a version of ETH, to rally researchers to unpack the proofs. So far, this effort includes a Google form asking for volunteers and feedback, and a commitment from OpenAI to fund a conference on the topic.

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