Research/arXiv AI/ML/September 10, 2026 at 5:49 PM

arXiv paper: CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

A new arXiv AI paper by Yifan Yang, Zhaoyan Wang, and Zheng Gao, and 2 more studies CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search.

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arXiv AI/ML / Research

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1A new arXiv AI paper by Yifan Yang, Zhaoyan Wang, and Zheng Gao, and 2 more studies CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search.

arXiv arxiv research_paper cs_lg cs_cv

Source Lens

High-credibility research source with strong edition heat and sparse entity coverage.

High confidence

Source: arXiv AI/ML
Type: research
Credibility: 86/100
Heat: 73/100
Signals: 1 entity / 6 tags

Article Corroboration

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Corroborated

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Matches: 2
Signals: 2
Avg trust: 90

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Server Waiting

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Reader Fit

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Discovery Read

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Follow-ups: 3
Reader bias: Balanced

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Deep readWait / 80%
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Learning Impact

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Learning Ready

Article memory: 0
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Next candidates: 3

Read+1 signal / +0.3 bias

Read would start a new Research memory; entity and angle memory wait for a deeper read.

Save+4 signals / +0.6 bias

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Share+4 signals / +0.9 bias

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Next Reads

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Next Reads Ready

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Article Signals

Topic: Research
Source: arXiv AI/ML
Entities: arXiv
Tags: arxiv, research_paper, cs_lg, cs_cv
Reader bias: F1.0 / N1.0

Reading Path

5 follow-ups ranked by article overlap, shared entities, and reader signals.

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