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AI models checking each other's work proves costly and ineffective in small tests

By PulseAugur Editorial · [1 sources] ·

A project called UNBOUNDED ONE tested the efficacy of using six AI models to review each other's work. In small-scale tests on two made-up scenarios, this multi-model approach did not outperform the best single model and incurred 20 times the cost. However, the system did successfully identify and correct errors made by weaker models within the group. The project is described as an unaudited demonstration of an approval-gate concept without an executor, and the results have not been externally verified. AI

IMPACT This experiment suggests that using multiple AI models for self-correction may not be cost-effective or superior to single models for certain tasks.

RANK_REASON The item describes a personal project's experimental results with AI models, not a formal release or significant industry event.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models checking each other's work proves costly and ineffective in small tests

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The item describes a personal project's experimental results with AI models, not a formal release or significant industry event.
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · unboundedwhat ·

    Last week I posted the approval gate from my project, UNBOUNDED ONE. Here's the rest, starting with what didn't work. I had 6 AI models check each other's work

    Last week I posted the approval gate from my project, UNBOUNDED ONE. Here's the rest, starting with what didn't work. I had 6 AI models check each other's work on 2 small made-up tests. On those 4 runs the group never beat the best single model and cost 20x more. It did fix two w…

    LINKS garthyoung45.github.io/…/one-review

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