The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

• URL: http://arxiv.org/abs/2606.30875v1
• Date: Mon, 29 Jun 2026 20:06:58 GMT
• Title: The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning
• Abstract summary: We introduce the Turing-inspired Label Game (LIG), a framework that formalizes pseudo-label pruning as an adversarial interrogation.<n>Rather than filtering labels via isolated thresholds, we use the LIG to train a Turing Test Network (TTN), a task-agnostic "judge"<n> Experiments across four diverse datasets demonstrate the TTN's robustness.
• Score: 13.045700151561434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds. To eliminate these errors, we introduce the Turing-inspired Label Imitation Game (LIG), a framework that formalizes pseudo-label pruning as an adversarial interrogation. Rather than filtering labels via isolated thresholds, we use the LIG to train a Turing Test Network (TTN), a task-agnostic "judge" that evaluates candidate pseudo-labels within a dataset-wide context. Experiments across four diverse datasets demonstrate the TTN's robustness, consistently enhancing label accuracy for three state-of-the-art vision-language models without costly supervision or retraining. Crucially, we demonstrate that learned semantic-contextual logic is a robust alternative to spatial-geometric verification, enabling a unique zero-shot task transfer capability - a TTN trained strictly on image classification datasets can effectively prune complex object detection pseudo-labels. This pruning yields F1-score gains of 28% for the worst-performing baseline categories and 44% with task-specific fine-tuning. Significantly, we also observe Category Revival, where the TTN pruning "detoxifies" the training signal for downstream models and enables them to recover from zero recall on transfer-vulnerable classes. The pre-trained TTN models and code are available at https://github.com/voxel51/ttn.

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