Researchers built an experiment where people interacted with an AI trained only on pre-1930 text to see if talking to a "historical mind" would shift their views about the past. It worked—people who chatted with the old-data model reported less of a bias that the past was more moral than today, compared to those using a current AI.

• The experiment reduced the "illusion of moral decline"—a documented bias where people assume past societies were more ethical than they actually were—by having participants interact with a historically-bounded language model instead of a contemporary one.
• Historically-bounded LLMs create an experimentally accessible way to approximate talking to someone from the past without modern knowledge contaminating their perspective, solving a real methodological problem in behavioral research.
• This framework treats AI systems as research instruments that can deliberately manipulate interaction conditions (in this case, temporal knowledge) to study and influence how people perceive, reason, and reflect.

Two mathematicians used large language models to discover counterexamples to long-standing conjectures, automating hypothesis generation and testing with minimal guidance. The article calls this “brute intelligence,” where AI runs iterative search loops to tackle any problem framed like a math exercise. It argues we’ll need to reshape tasks into testable, calculable formats for AI to industrialize discovery across fields.

• Tao got an LLM (Anthropic's Fable) to produce a counterexample to the Jacobian conjecture over a weekend, calling it "a massive miracle" unlikely via manual search.
• Rybin disproved another long-standing conjecture using ChatGPT alone with just four prompts and no expert steering.
• The pattern works because these problems admit short, verifiable counterexamples—AI can find them if it can check its own work.
• The real implication is reframing fields (code, drug discovery, finance) into testable, formalized "math-like" tasks so AI can brute-force solutions via fast, parallel iteration.

This article breaks research down into trainable habits: choose your own problems, broaden your reading beyond trends, write and log every idea, and tighten your experiment loop with solid tooling. It also stresses purposeful exploration, scrutinizing outputs by hand, and building a generous network to compound learning and productivity over time.

• Pick your own research problems by deciding the outcome you want and working backward, rather than copying trending topics—forces originality instead of endless tweaking of existing literature.
• Train "taste" like a muscle by predicting experiment results before running them and scoring those forecasts over time.
• Read beyond trending papers (arXiv hot lists, Slack) into older/cross-field work—mixture of experts (1991), LSTMs (1997), Shannon's 1952 talk—to spot dead ends and promising angles early.
• Keep a running written lab log (hypothesis, setup, expectation, result, belief update) and write public essays, since exposing assumptions in writing guards against self-deception and can shape a field more than dense papers.