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Automated Reference Verification System

productactiveautomated-reference-verification-system-b6ad444d·1 events·first seen May 19, 2026

Aliases: Automated Reference Verification System

Co-occurring entities

Factual Recall Scaling Law Superposition Model (neural networks)Superposition-Inspired Signal-to-Noise Account

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Recent events (1)

7arXiv · cs.CL·May 19, 2026·source ↗

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

This paper establishes a quantitative scaling law linking LLM factual recall to both model parameter count and topic frequency in training data, evaluated across 38 models on 8,900+ scholarly references. Recall quality follows a sigmoid function in the log-linear combination of these two variables, explaining 60% of variance across 16 dense models from four families and 74-94% within individual families. The authors propose a superposition-inspired mechanism where recall is gated by a signal-to-noise ratio: concept frequency provides signal and model capacity sets the noise floor. This provides a predictive framework for understanding and anticipating LLM confabulation patterns.

Frontier Model Releases Evaluation and Benchmarking Automated Reference Verification System Factual Recall Scaling Law Superposition Model (neural networks)+2 more