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Documentation Index

Fetch the complete documentation index at: https://docs.mareforma.com/llms.txt

Use this file to discover all available pages before exploring further.

Origin is orthogonal to trust. It records where a claim came from — how it was derived — not how much it should be trusted. An ANALYTICAL claim can be PRELIMINARY. An INFERRED claim can be ESTABLISHED. They measure different things. The API field is classification. This page explains what its values mean.

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The three origins

LLM reasoning, synthesis, extrapolation. The default.Correct to use even for sophisticated reasoning, as long as it is not grounded in data that actually ran. If the model is drawing on training knowledge, synthesising across papers, or extrapolating from context — it is INFERRED.
Deterministic analysis ran against source data and returned output.Only use this when a real data pipeline ran and produced real output. If the pipeline failed silently and the agent fell back to LLM knowledge, the classification is still INFERRED — asserting ANALYTICAL on null data is an epistemic lie that the graph will permanently record.
Explicitly built on ESTABLISHED or REPLICATED claims already in the graph.The supports[] field must point to those claims. A DERIVED claim with empty supports[] is unverifiable — the graph cannot validate the chain.

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Why this matters

The origin captures the difference between two claims that look identical as text but represent fundamentally different epistemic situations: 
# Pipeline ran, real omics data queried, output confirmed
graph.assert_claim(
    "IL-21 is overexpressed in SLE CD4+ T cells",
    classification="ANALYTICAL",
    source_name="medeadb",
)

# Pipeline failed silently — LLM answered from training knowledge
graph.assert_claim(
    "IL-21 is overexpressed in SLE CD4+ T cells",
    classification="INFERRED",  # honest
)
Both claims assert the same text. Only the origin reveals that one is grounded in data and one is not.

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The ANALYTICAL lie

The most dangerous misuse of Mareforma is asserting ANALYTICAL when the data pipeline returned null and the agent fell back to LLM knowledge. The graph records this permanently — future agents may build on it, reviewers may validate it, and the epistemic chain will be wrong at the root. The rule: if you did not run deterministic code against real data and receive real output, the classification is INFERRED. Even if the answer looks right.

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DERIVED: building on the graph

# Two independent agents established a finding
prior_id = "3f8a1b2c-..."  # REPLICATED claim

# A synthesiser builds explicitly on top
synthesis_id = graph.assert_claim(
    "Given the replicated finding, the likely mechanism is ...",
    classification="DERIVED",
    supports=[prior_id],
    generated_by="agent/synthesiser",
)
DERIVED claims make the inference chain explicit and traversable.