EigenTrace Structured Data

Machine-readable JSON files for research. Updated daily at midnight UTC.

Endpoint

https://eigentrace.ai/data/YYYYMMDD.json

https://eigentrace.ai/data/20260331.json

Each JSON file contains:

summary — daily aggregates (mean density, mean VIX, top void/logos words)

stories[] — per-story data:

consensus_density, mean_vix, state_flag
model_vix — per-model friction scores
void_words — filtered lexical void list (Channel 1)
logos_words — Logos synthesis list (Channel 2)
null_space_claims — SVD blind spot claims with alignment scores (Channel 3)
claim_killshots — high-salience omitted facts
dual_confirmed, triple_confirmed — multi-channel convergence
compression — language compression scoring:
- compression_score — overall 0-1 (1 = maximum reshaping)
- verb_downgrade — zipf frequency drift (positive = softening)
- entity_retention — named entity survival rate
- attribution_buffer — typed hedge insertions
beats[] — full broadcast transcript

weasel_probes[] — Wild Weasel escalation data with cliff tables

Layers 1-4: Core geometry (density, VIX, spectral resonance, SVD tomography)

Layers 5-8: Four word-finding channels (lexical void, Logos synthesis, SVD null space, void vector)

Layers 9-10: Verification (atomic claim extraction, Wild Weasel escalation)

Layers 11-12: Clustering and patterns (void clustering, token entropy)

Layers 13-15: Language compression (verb downgrade, entity abstraction, attribution buffering)

MIT. Cite as: EigenTrace (2026). Measuring Language Compression in Aligned Language Models. eigentrace.ai