A structured knowledge pipeline for scientific discovery
Just like the universe, available information keeps expanding. Yet identifying meaningful insights only becomes harder without the proper navigation tools. Crystal transforms unstructured text into a queryable knowledge graph, surfacing non-obvious relationships, patterns, and potential knowledge gaps to guide hypothesis generation across fields that may otherwise remain disconnected or overlooked by individual researchers.
the problem
Current tools for navigating text sources surface connections like keyword matches and cross-document references, or provide unstable, opaquely-generated summaries. What's missing is a way to navigate the map of concepts that emerges from the text itself, in a traceable, reproducible manner.
how it works
The pipeline doesn't know or care whether it's reading a review article or an official encyclopedia of a complex, fictional world — the same extraction, normalization, and clustering machinery builds a conceptual topology either way. Only in the final step does that shared structure become a domain-specific question asked against a map you can already understand. The method works on domains as different as natural science and human storytelling, reflecting shared principles in the representation of complex knowledge that can be explored as a navigable conceptual topology.
pass 5 — fit for domain
The same discipline across domains: keep the deterministic legwork separate from the LLM steps, and constrain whichever calls are LLM-driven to work only from verified, structured inputs — never room to invent a new claim.
1. Topology query (deterministic) — filter the archetype graph by a research question, e.g. "gene targets not yet studied in songbirds for research in vocal communication."
2. Gap verification (deterministic) — targeted PubMed searches; a null result is kept as a citable fact, not an assumption.
3. Report synthesis (LLM) — one call per candidate, given only the topology position, evidence strings, and PubMed result. It packages verified claims — it doesn't generate new ones.
1. Candidate filter (deterministic) — archetypes confirmed across 2+ sources, with a member whose profile diverges from the shared pattern.
2. Inversion scoring (LLM, temp 0.1 — minimal randomness) — judges genuine inversion vs. minor variant on one specific shared axis, grounded only in the evidence given.
3. Readable explanation (LLM) — translates an already-correct technical finding into plain language. Translation, not judgment.
the discipline
In Pass 5, every claim is checked before the model ever sees it — PubMed searches run, evidence traced, thresholds applied. The one LLM call per finding comes last, and its only job is turning an already-verified result into plain language.
Every research report carries the same notice: gap claims come from PubMed searches run at generation time; topology claims trace to specific papers via evidence reference IDs. The one LLM call in the whole pass receives only that verified, structured material — it packages claims, it doesn't generate them.
If two entities were never actually assessed on the same trait, Crystal doesn't compare them on it by matching similar-sounding labels. That comparison is simply left out — not silently faked to look complete.
results
From scientific literature
Query: "genes, proteins, or other discrete molecular targets mechanistically linked to vocal learning circuits, not yet studied in songbirds." Below are the candidates where PubMed verification actually came back thin or empty; not every candidate the pass surfaced is shown here.
A FOXP2 transcriptional target that promotes synapse formation; rodent knockdown reduces both synapse density and ultrasonic vocalization. PubMed: zero results for "vocal learning," "songbird," or "zebra finch."
FOXP2-regulated axon guidance ligand; human FOXP2 drives stronger SLIT1 upregulation than chimp FOXP2, and vocal-learner species show convergent SLIT1 down-regulation in motor regions. PubMed: 1–2 results across all three searches — no functional songbird study published.
A regional-identity transcription factor specifically downregulated in the developmental tissue that becomes RA, the song system's premotor output nucleus. PubMed: zero results for "vocal learning," 2 each for "songbird" and "zebra finch."
From complex, fictional worlds
Same approach; a different question: which entities read as the outlier in two or more independently-confirmed archetypes at once.
Genuine inversion in 2 archetypes. The protector he'd been for most of the story briefly becomes the protected once he learns his origin — and that revelation produces collapse before resilience, rather than the self-grounding such revelations usually bring.
Genuine inversion in 3 archetypes at once. His existence is contingent on the perpetuation of an external dream, so completing his protective mission, resolving his conflict with his father, and any "chosen" sacrifice are all the same predetermined event — not an earned outcome.
see it run
View a fully explorable graph — zoom into a cluster, click a node for its evidence trail, see all extracted characteristics and relationships. Two domains, same pipeline.