> ## 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.

# Why Mareforma

> The problem with AI science infrastructure and what Mareforma does differently.

## The problem

AI scientists are being deployed on real research problems, including drug
targets, clinical hypotheses, and materials discovery, before any infrastructure exists
to answer a basic question: which of their findings can be trusted?

The problem is not that AI scientists get things wrong. Every scientist gets
things wrong. The problem is that the current generation of agent systems
provides no principled way to distinguish:

* a finding backed by data from one backed by LLM prior knowledge
* genuine independent replication from two agents repeating each other
* an established consensus from a single speculative assertion

Without that structure, every output looks like a result.

## What other tools address

**Tracing and observability tools** record what the agent did: which tools
were called, what was returned, how long it took. This is useful for debugging.
It is not epistemic infrastructure. Knowing that an agent called a retrieval
tool does not tell you whether the retrieved finding was independently
replicated or whether the data pipeline actually ran.

**Structured outputs and validation schemas** catch format errors. They
do not catch epistemic errors: a correctly structured claim can still be
wrong, ungrounded, or a duplicate of something already known.

**Vector stores and retrieval** give agents access to prior knowledge.
They do not record which of that knowledge has been independently validated,
which is contested, or which paths the current claim was derived from.

## What Mareforma does differently

Mareforma is not an observability tool. It does not record execution traces.
It records **epistemic objects** (claims with provenance) and derives trust
from the structure of the graph, not from the agents that populate it.

**Trust from topology, not introspection.** Two independent agents reaching
the same conclusion through different data paths is a stronger signal than
one agent reporting high confidence. Mareforma detects this automatically.

**Origin as epistemic honesty.** The distinction between ANALYTICAL
(data ran) and INFERRED (LLM reasoning) is recorded permanently at assertion
time. An agent that asserts ANALYTICAL on a silent pipeline failure is making
a claim the graph will preserve, and that a reviewer can challenge.

**Accumulation, not evaporation.** Findings persist across runs. When a new
agent asserts something, it can query what is already established and build
on it with `DERIVED` classification. Knowledge compounds instead of
resetting with every run.

**Documented contestation.** When a new finding contradicts an established
one, both coexist in the graph with an explicit `contradicts[]` link. Science
advances by documented tension, not by one side being silently overwritten.

**Human oversight at the right layer.** Agents accumulate at machine speed.
Humans review at the ESTABLISHED gate: not per step, but per finding that
has earned independent replication. The graph does the accumulation; the
human does the validation.

## Who this is for

**Autonomous AI scientists:** agents running multi-step research pipelines
on real scientific problems. Mareforma gives them a shared memory where
findings accumulate and can be queried before any new assertion is made.

**Labs building AI science infrastructure:** teams that need to know which
of their agents' outputs are grounded in data, which are replicated, and
which are speculative. Mareforma provides the verification layer without
requiring changes to the agents themselves.

**Multi-agent research systems:** where multiple agents contribute to a
shared knowledge base and convergence needs to be detected automatically
across runs, agents, and data sources.

## What Mareforma is not

Mareforma is not a guarantee of truth. The graph records what agents assert
and what the provenance structure supports. It does not verify that
assertions are correct. A REPLICATED finding can still be wrong if both
agents were reasoning from the same false prior.

Mareforma is not a replacement for scientific methodology. It is
infrastructure that makes methodology legible: it records the difference
between "two agents happened to say the same thing" and "two agents reached
the same conclusion through independent data paths."

The distinction matters enormously. Mareforma makes it computable.