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Foundation model perturbation prediction reliability linked to low-rank decoder bottleneck

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a low-rank bottleneck in the state-transition decoder of foundation-model predictors, specifically within the first attention layer. This bottleneck limits the reliability of perturbation response predictions, with accuracy declining as the distance from training data increases. A proposed metric, predicted-NN-distance, which measures the cosine distance from a predicted response to its nearest training response, serves as a per-prediction confidence score. This score demonstrates a strong correlation with actual prediction reliability and can be calibrated to transfer across different encoders and cell lines. AI

IMPACT Identifies a specific bottleneck in foundation models that impacts reliability for experimental prioritization.

RANK_REASON The cluster contains a research paper detailing findings about foundation models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Foundation model perturbation prediction reliability linked to low-rank decoder bottleneck

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The cluster contains a research paper detailing findings about foundation models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zheyu Zhang, Yuanhao Huang, Fan Feng, Jie Liu ·

    A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction

    arXiv:2611.05849v1 Announce Type: new Abstract: Foundation-model predictors of perturbation response are evaluated primarily by aggregate accuracy. Using their predictions to prioritize experiments requires assessing each prediction's reliability. We audit the state-transition (S…

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