OpenAI published 722 math papers generated with an unreleased AI model, including partial results tied to long-running hypotheses. The Riemann hypothesis was addressed at the scale of proving a quasi-Riemann hypothesis rather than a full solution. Going forward, OpenAI plans to release Lean proofs for more of the papers and fund events/programs to review the AI-generated discoveries.
Anthropic has made Claude Haiku 5.5 available on Amazon Bedrock and Claude Platform on AWS. It costs around 75 percent less than Claude Haiku 4.5 for most tasks. Teams can now run Haiku 5.5 inside AWS with AWS billing/authentication and monitor usage via AWS tools, with endpoints available today across multiple AWS regions.
Healthleap raised $38 million to develop an AI platform that screens hospital inpatients’ records to flag potential undiagnosed conditions for further clinician review. The funding includes an $8 million seed round co-led by Sequoia Capital and First Round Capital and a $30 million Series A led by Hummingbird Ventures. The company will use the new cash to expand engineering, product, sales, and customer success as it broadens coverage beyond its initial malnutrition and delirium programs.
The author argues that LLMs make it harder to learn from mistakes and describes how they re-learned programming learning by using an LLM as a mentor instead of an answer source. They built a project called tinylsm and used a plan/checklist approach, plus a benchmark/REPL example showing reads at 174.9µs with 1 table. As a result, they use question-driven explanations, pre-planned phases, visible progress, and short consistent sessions to regain motivation and reinforce understanding.
OpenAI launched the Decisions API to evaluate text and/or images and return typed answers for app workflows like classification and routing. The API is about 10x faster than the Responses API and is in public beta with expected GA in the coming weeks, currently using only the gpt-6-luna model via POST /v1/decisions. Apps can now use predicate probabilities and fixed-set choice or rubric score outputs (instead of slower free-form responses) to drive automated triage and review thresholds.
Ai2 published the Bolmo research in Nature and released new Hugging Face checkpoints for byte-level language models beyond the original Olmo-based versions. The release includes Stage 1 checkpoints that freeze the global model while training Bolmo’s byte-level components. This enables faster retrofitting of subword models into competitive byte-level models and supports broader experimentation without retraining from scratch.
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