# Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null

arXiv: https://arxiv.org/abs/2601.20336 · HTML: https://mlsanity.com/paper/2601.20336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Factor Models for High-Dimensional Tensor Time Series](https://mlsanity.com/paper/1905.07530/) (2019)
- [High-dimensional vector autoregressive time series modeling via tensor decomposition](https://mlsanity.com/paper/1909.06624/) (2019)
- [CP Factor Model for Dynamic Tensors](https://mlsanity.com/paper/2110.15517/) (2021)
- [Anatomy of a Stablecoin's failure: the Terra-Luna case](https://mlsanity.com/paper/2207.13914/) (2022)
- [Blockchain Inefficiency in the Bitcoin Peers Network](https://mlsanity.com/paper/1704.01414/) (2017)
- [Information Filtering Networks: Theoretical Foundations, Generative Methodologies, and Real-World Applications](https://mlsanity.com/paper/2505.03812/) (2025)
- [Graph Regularized PCA](https://mlsanity.com/paper/2601.10199/) (2026)

## Abstract (arXiv)

Do the functional narratives in cryptocurrency whitepapers correspond to their tokens' market behaviour? We compare ten-category topical-emphasis profiles for 43 screened documents with seven market statistics calculated from 2023--2024 exchange data. The primary specification reconstructs USD notional turnover from hourly bars. Dimension-matched Procrustes congruence is φ=0.280 (permutation p=0.507), slightly below its permutation-null mean of 0.284; the zero-padded statistic gives the same non-detection. A four-leg comparison separates document replacement from changes in the assets included. Replacing documents on the 34 common assets changes padded congruence by -0.014 under USD turnover and -0.009 under base-token volume. Entity rankings and threshold crossings depend on both composition and specification, so an earlier contamination-only attribution is withdrawn. The documents are not a verified historical corpus: at least two postdate the market window. Excluding these documents, or excluding all seven assets with shorter histories, does not produce a significant alignment. Fresh numerical simulations distinguish injected signal from fitted congruence and compare noise restricted to the market subspace with noise throughout the text space. At the lowest classifier-agreement scenario, detection remains below 43% even at the largest injected signal. These are conditional checks of the alignment stage, not validation of the text instrument or exclusion bounds on economic effects. The contribution is an auditable non-detection and a specification-sensitive corpus diagnosis, with the inferential limits made explicit.