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New benchmark tests AI agents' ability to discover scaling laws

By PulseAugur Editorial · [1 sources] ·

A new paper introduces SLDBench v2, an expanded benchmark designed to test language-model agents' ability to discover scaling laws for large AI models. The benchmark includes 127 scenarios and focuses on 30 challenging cases where a standard power-law fit is less than ideal. When tested, eight frontier agent systems showed a significant drop in prediction accuracy and an increase in error when actively choosing experiments, often prioritizing cheaper ones over those crucial for extrapolation. AI

IMPACT Highlights the challenge of experimental planning for AI agents assisting in model development.

RANK_REASON The cluster contains a research paper detailing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark tests AI agents' ability to discover scaling laws

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The cluster contains a research paper detailing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lingbei Meng, Rui Huang, Zhirong Shen, Jinxin Liu, Lingxiao Jing, Haorui Chen, Yaosong Du, Haowei Lin ·

    SLDBench v2: Active Scaling Law Discovery

    arXiv:2610.16297v1 Announce Type: cross Abstract: Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments. SLDBench tests whether language-model agents can discover scaling laws when all experimental resul…

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