RedNote's dots-note-3.0 AI claims perfect 42/42 score at IMO

A Chinese social app's AI model scored a perfect 42 at the International Mathematical Olympiad. RedNote, the company behind Xiaohongshu, built the system, called dots-note-3.0, still in beta. Google DeepMind and OpenAI reached 35 out of 42 last year, a gold-level showing. Only 7 of 666 human contestants in Shanghai matched that result this year. The IMO asks for written proofs, not final answers, and human graders read every line. A skipped case or an unstated condition costs points, even when the final answer is right. Most AI maths benchmarks only check the last number, so a lucky guess still scores full credit. That gap is why 42 out of 42 here means more than a high score on a normal test. This time the model read the original contest documents directly, with no human rewriting. It ran an agentic loop mixing natural-language reasoning with Python code it executed itself. Drafts got tested, broken, and repaired before anything went to the official graders. Self-review carries the weight here, since the system questioned its own assumptions repeatedly. Contest rules barred hints, corrections, and every other form of help during the run. RedNote made the promise, that the model will be open sourced in due course, but has not committed to any time yet. The winning model variant is also the smallest in the dots3 family, which includes two larger versions – jazz and aria – tailored for different use cases and compute costs. --- scmp. com/tech/article/3361482/worlds-first-ai-model-earn-perfect-score-maths-olympiad-comes-chinas-rednote

dots, the research division from xhs (Chinese Instagram), claims to have reached a perfect score on this years IMO

A Chinese social app's AI model scored a perfect 42 at the International Mathematical Olympiad. RedNote, the company behind Xiaohongshu, built the system, called dots-note-3.0, still in beta. Google DeepMind and OpenAI reached 35 out of 42 last year, a gold-level showing. Only 7 of 666 human contestants in Shanghai matched that result this year. The IMO asks for written proofs, not final answers, and human graders read every line. A skipped case or an unstated condition costs points, even when the final answer is right. Most AI maths benchmarks only check the last number, so a lucky guess still scores full credit. That gap is why 42 out of 42 here means more than a high score on a normal test. This time the model read the original contest documents directly, with no human rewriting. It ran an agentic loop mixing natural-language reasoning with Python code it executed itself. Drafts got tested, broken, and repaired before anything went to the official graders. Self-review carries the weight here, since the system questioned its own assumptions repeatedly. Contest rules barred hints, corrections, and every other form of help during the run. RedNote made the promise, that the model will be open sourced in due course, but has not committed to any time yet. The winning model variant is also the smallest in the dots3 family, which includes two larger versions – jazz and aria – tailored for different use cases and compute costs. --- scmp. com/tech/article/3361482/worlds-first-ai-model-earn-perfect-score-maths-olympiad-comes-chinas-rednote

A Chinese social app's AI model scored a perfect 42 at the International Mathematical Olympiad. RedNote, the company behind Xiaohongshu, built the system, called dots-note-3.0, still in beta. Google DeepMind and OpenAI reached 35 out of 42 last year, a gold-level showing. Only 7 of 666 human contestants in Shanghai matched that result this year. The IMO asks for written proofs, not final answers, and human graders read every line. A skipped case or an unstated condition costs points, even when the final answer is right. Most AI maths benchmarks only check the last number, so a lucky guess still scores full credit. That gap is why 42 out of 42 here means more than a high score on a normal test. This time the model read the original contest documents directly, with no human rewriting. It ran an agentic loop mixing natural-language reasoning with Python code it executed itself. Drafts got tested, broken, and repaired before anything went to the official graders. Self-review carries the weight here, since the system questioned its own assumptions repeatedly. Contest rules barred hints, corrections, and every other form of help during the run. RedNote made the promise, that the model will be open sourced in due course, but has not committed to any time yet. The winning model variant is also the smallest in the dots3 family, which includes two larger versions – jazz and aria – tailored for different use cases and compute costs. --- scmp. com/tech/article/3361482/worlds-first-ai-model-earn-perfect-score-maths-olympiad-comes-chinas-rednote

A single researcher solved 6 open Erdős problems in 5 days using GPT-5.6 Sol. He attempted 13 problems in total, so the success rate sits near 46%. He wrote the prompt almost as a contract, not a question. It restated the problem, then spelled out what a finished proof must actually establish. listed the weaker results that would not count, so near-misses could not sneak through. Known traps and edge cases went in before the model ever started thinking. Then he told it how to search, which is where most people stop. Chase many approaches at once, keep incompatible ones alive, and never commit early. Hunt for counterexamples to your own lemmas, and kill a route that only leads to another open problem. Separate adversarial agents then tried to break every draft that survived. None of this works without the right target, so he picked problems mathematicians already argue about, avoiding anything welded to a famous conjecture. Codex held the whole search in memory for hours while that loop ran itself.

A single researcher solved 6 open Erdős problems in 5 days using GPT-5.6 Sol. He attempted 13 problems in total, so the success rate sits near 46%. He wrote the prompt almost as a contract, not a question. It restated the problem, then spelled out what a finished proof must actually establish. listed the weaker results that would not count, so near-misses could not sneak through. Known traps and edge cases went in before the model ever started thinking. Then he told it how to search, which is where most people stop. Chase many approaches at once, keep incompatible ones alive, and never commit early. Hunt for counterexamples to your own lemmas, and kill a route that only leads to another open problem. Separate adversarial agents then tried to break every draft that survived. None of this works without the right target, so he picked problems mathematicians already argue about, avoiding anything welded to a famous conjecture. Codex held the whole search in memory for hours while that loop ran itself.

they have also rebranded / got a proper identity now and are hiring in BEI, SH, HZ cc @AndrewCurran_ something of interest for you for sure