Unverified50% confidenceBenchmarkExact time
Experimental results show that the two internal representation-based methods, LTD and ARP, consistently outperform calibration baseline methods based on surface generation and sequences.
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9/11/2026
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UnverifiedLTD and ARP directly utilize internal representations already produced during the model's normal inference process, requiring no additional sampling costs or modifications to input prompts, achieving zero-overhead reliability monitoring.71% similarUnverifiedA study from arXiv proposed a test-time optimization method that improves the completeness and reliability of LLM explanations by removing concepts not mentioned in the input.67% similarUnverifiedThe study proposes the Action Representation Probe (ARP) method, which focuses on representations formed at action decision moments to predict whether a task will succeed.65% similarUnverifiedThe model trained using Edu-QuRating-filtered mixed sets achieved a higher overall observed accuracy than the FineWeb-Edu baseline model across nine benchmark tests, with performance gains concentrated on specific tasks.63% similar
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