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摘要: AIRA_2: Breaking Bottlenecks in AI Research Agents

Mornings With Markman - April 1st, 2026

Markman Capital Insight

Apr 01, 2026

AI agents crossed a major milestone last week, according to r...

正文

AIRA_2: Breaking Bottlenecks in AI Research Agents

Mornings With Markman - April 1st, 2026

Markman Capital Insight

Apr 01, 2026

AI agents crossed a major milestone last week, according to researchers at Facebook. Their AIRA_2 model bests humans at the toughest machine learning problems. It often wins by more than 2 points after a single day of work. This big advance lets these agents run experiments without constant human tweaks. It is a massive shift for the industry.

Previously, AI agents worked like junior scientists. They stalled when they encountered a bad test. They also could not work through compute glitches. AIRA_2 fixes these issues with multiple GPUs running in parallel. It also scales work linearly as hardware grows. No more waiting in line for one machine. Speed is now a built-in feature.

Tests used MLE-bench, a set of 30 real-world data science contests. These mimic challenges that human experts face when building machine learning models from scratch. AIRA_2 hit a 71.8% rank after 24 hours. That tops the old leader at 69.9%. By day three, it reached 76%. Humans rarely crack 70% on these difficult contests.

Smart evaluation drives these gains. Past agents failed because they relied on noisy data. AIRA_2 hides test data until the end. Clean feedback lets agents iterate like pros. They debug code over many steps instead of relying on one-shot guesses. This loop will be most beneficial in drug discovery. Agents screen molecules faster than traditional labs.

We favor Eli Lilly. Through its partnership with Isomorphic Labs, the pharma giant is running multi-target research. It models protein structures to predict trial outcomes. This approach cuts years from discovery timelines. It is backed by a $2.75B deal to fund a robust pipeline of AI-driven candidates. The firm is turning biology into a predictable engineering problem.

There is no need to worry about compute, either. Nvidia powers the agent labs at Lilly to scale production. DeepMind te

来源: Brave/

采集时间: 2026-04-02 02:39:35