How Does Attention Help? Insights from Random Matrices on Signal Recovery from Sequence Models
arXiv 2605.06826•376de2ef3fd1b845a68b3adedf65c93e988d1d40b2c2128283adbab803d9edf5
Bayesian-methodsLLM-evaluationMMDPAC-learningattentioncausal-inferenceconformal-predictiondiffusion-modelsepistemic-neural-networksinstrumental-variablesitem-response-theorykernel-selectionmachine-learningmax-affinemodel-selectionneural-operatorso-minimal-structurespartial-order-inferencepiecewise-linear-regressionprobabilistic-conditioningrandom-matricessignal-recoverystatistical-learningtheorytransport-alignment
Paper metadata
- arXiv ID
- 2605.06826
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 376de2ef3fd1b845a68b3adedf65c93e988d1d40b2c2128283adbab803d9edf5
- Enrichment time
- 2026-05-11T07:23:55Z
- AI-assisted enrichment
- Yes
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