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The paper proposes a lightweight method that reformulates regression-based INR training as a classification task by discretizing continuous targets into bins, enabling flexible distribution modeling for error-aware uncertainty estimation in scientific data compression.
Proposes LiSCP, a lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content, focusing on feature stability under adversarial manipulation. Achieves superior performance on in-domain and cross-domain detection with notable robustness.