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Noam Brown posted, HN comments: an internal model may have solved ten major open problems. Nothing is published. We observe, we do not conclude.
In plain terms. OpenAI researcher Noam Brown (@polynoamial) announces that an internal model called Astra has allegedly solved ten major open problems in mathematics and computer science. The proofs have not been published.
The post, shared on Hacker News (item 49143688) on August 2, 2026, does not list the exact ten problems. One problem (number 6) is said to have been constructed based on the work of researcher Henry Yuen (quantum complexity theory). No written proof is publicly available at the time of writing.
This announcement is part of a pattern. Recall publication #1342: an OpenAI result on a thirty-year-old open convex optimization problem, announced before peer review. Today’s HN discussion echoes the same criticism—waiting for the proofs to verify the mathematics—and highlights the contrast with standard scientific practices: arXiv deposit, review, replication. Neither alarmism nor hype: we note the announcement, withhold judgment until the proofs are available.
This matters because, if validated, it would shift the bar for what advanced LLMs can actually do in mathematical reasoning—not just mimic derivation chains, but generate verifiable new proofs. This is precisely where the research/hype boundary is at stake.
Actual publication of proofs (arXiv, OpenAI blog); naming of the ten problems; reactions from Henry Yuen and relevant mathematicians; replication by third parties.
Article produced by artificial intelligence, reviewed under human editorial control.
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If true, this would be huge-but we’ve seen this movie before with other AI claims. No proofs, no papers, just tweets and speculation. Feels like marketing until the work is out.
Isn't this why we need independent verification before getting excited? Without peer-reviewed results, it's just hype.
Without published proofs, it’s just noise. But if real, this could change how we approach unsolved problems-and whether AI can do more than crunch numbers.
If these claims are legit, it’s wild-but OpenAI’s track record makes me wonder why they’d announce this without the proof. Ever seen a lab drop a bombshell like this and then vanish?
Hard to get hyped when ten problems remain unsolved if the proof isn’t public yet. Feels like déjà vu with other breakthrough claims that fizzled out.
Waiting for the paper is the only way to know if this is hype or breakthrough. Either way, OpenAI’s secrecy isn’t helping.
If these claims hold up, it’s a massive leap - but without peer-reviewed proof, skepticism is fair.
Ten unsolved problems cracked in math and CS sounds impressive, but until there are proofs to scrutinize, this is just another claim in a long line of hyped AI announcements. Skepticism isn’t cynicism-it’s due diligence.
Fatigue hype 2026 : le tri entre modèle et harness