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– https://t.co/zmwn4jdUp2
Title: "AIM: Agentic Idea Management for Automated Research"
AIM research proposes idea ranking and code checks for automated research agentsMachine translation
Automatically verified and published · Generated and evidence-checked automatically; not reviewed by a human.
A post describing a Google paper says AIM ranks research ideas by theme and divides each round between promising and untested directions. An auditor discards gamed results and checks that ideas match the code. The post says AIM scored 1.6 and 4.9 points higher than the previous leading agent, ScientistOne, across two task groups, and matched its best score up to 3.1 times sooner.
New Google paper shows research agents get better results sooner when they keep a ranked map of ideas and check that code matches each idea, so build both in.
Most agents just keep editing code.
When code drifts from the idea it's scored as, the agent learns the wrong lesson.
AIM sorts ideas into ranked themes and splits each round between strong themes and untested ones. An auditor tosses gamed results and relabels ideas to match the code.
It beat the best prior agent, ScientistOne, by 1.6 and 4.9 points on 2 task groups, and matched ScientistOne's best score up to 3.1x sooner.
Expect the biggest gains on tasks with many possible approaches and few good ones.
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