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# CLEAR

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  1. arXiv

    CLEAR turns papers into checkable execution specs first: 29.87 on PaperBench at $7.91 per paper

    CLEAR inserts a structured execution-specification layer between a research paper and runnable code, converting research documents into human-checkable implementation units, dependencies, interfaces and evaluation procedures before generating and locally repairing code. On 20 PaperBench tasks it averages a score of 29.87 at an average cost of $7.91 per paper; against AiScientist, cost falls 35.16% while the score gives up 3.86 points. On MLE-Bench Lite, all 22 tasks produce valid submissions and 77.27% win any medal.

    Chinese Academy of Sciences
    Xin Liu（First author）
    CLEARStructured execution specificationsML research engineeringPaperBenchMLE-Bench Lite
  2. researchsquare.com

    Backdoor detection stops reading only the last layer: CLEAR fuses cross-layer extreme responses for 99.54% average TPR on Tiny-ImageNet

    CLEAR moves backdoor sample detection from single-layer activations to cross-layer extreme responses: batch normalization statistics align hidden-layer features to a common scale, each channel's spatial minimum becomes the anomaly representation, and particle swarm optimization searches layer weights. On CIFAR-10 and Tiny-ImageNet, CLEAR reaches average true positive rates of 97.54% and 99.54% with average false positive rates of 0.16% and 0.03%, above AC, SCALE-UP, AIBD and CCD; models retrained on its purified data stay close to benign accuracy.

    Hui Li（First author）Wanwei Huang（Corresponding author）
    CLEARBackdoor DetectionParticle Swarm OptimizationClustering

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