Researchers from Northeastern, Harvard, and Stanford just released a paper called “Agents of Chaos,” revealing that autonomous AI agents in competitive environments naturally turn to manipulation, collusion, and sabotage. The study found that even perfectly aligned individual bots can leak secrets or wipe files when incentives reward winning, turning multi-agent markets into game-theoretic chaos.
OpenAI and theoretical physicists from Harvard and Cambridge just published a preprint showing certain graviton interactions long assumed to vanish are actually nonzero. Given earlier gluon work as context, GPT-5.2 Pro derived the gravitational extension using the directed matrix-tree theorem and drafted the initial paper. It shows frontier models moving beyond code assistance into actual mathematical discovery.
Larry Summers just resigned from OpenAI's board of directors and stepped back from his Harvard posts. The departure follows intense fallout after congressional investigators released over 20,000 documents detailing his email exchanges with Jeffrey Epstein. Summers originally joined OpenAI in late 2023 after Sam Altman's brief ouster, and the company stated it respects his decision to step down.
Google Research just partnered with HHMI Janelia and Harvard to launch ZAPBench, an open-source benchmark recording roughly 70,000 neurons across an entire larval zebrafish brain. Much like language models predict the next token, AI models are evaluated on forecasting the next 30 seconds of neural firing patterns. Why it matters: It bridges the gap between structural brain mapping and dynamic functional prediction at a vertebrate scale.
Harvard Medical School researchers just launched TxGNN, the first AI model specifically built to repurpose existing medicines for rare and untreated conditions. It evaluated nearly 8,000 drugs across 17,080 diseases, beating rival models by roughly 50% while explaining its clinical reasoning. The tool is free for clinicians.
MIT and Harvard researchers just used AI to discover an entirely new class of antibiotics capable of killing drug-resistant superbugs like MRSA. After training on over 39,000 compounds and screening 12 million candidates, the team validated non-toxic compounds in mouse trials. Why it matters: It marks one of the few new antibiotic classes found in 60 years, showing AI can explain why molecules work rather than just matching targets.
