External Falsifiable Measurement Data for Long-Chain Reasoning

Huang Zhiwei's dataset provides code and data for establishing a falsifiable measurement framework for long-chain reasoning in AI. It supports the paper's finding that the original continuous realization was rejected under the stated instrument conditions, with a held-out R² of -15.98. The dataset was uploaded to Figshare in April 2026.

Use Cases

• Analyze intermediate-state degradation metrics to test the falsifiability of reasoning models.
• Reproduce the statistical identification results, including the reported R² value of -15.98.
• Apply the provided protocol constraints to benchmark new long-chain reasoning systems.
• Study the relationship between declared instrument conditions and model performance outcomes.

Strengths

• Dataset is associated with a peer-reviewed research paper proposing a principled measurement framework.
• Includes both code and data for full reproducibility of the reported analysis.
• Released under a permissive MIT license for broad reuse.

Limitations

• The dataset is small at 2.5 MB, limiting the scope of contained data.
• Specific row counts, column names, and data structure details are not provided in the metadata.
• The recency of the data (2026) may indicate it is from a pre-publication or early-release stage.

Provenance

Source

Huang Zhiwei, uploaded to the Figshare repository.

Collection Method

Created as part of academic research for the paper 'External Falsifiable Measurement for Long-Chain Reasoning'.

Freshness

Last updated on 2026-04-06.