Phase 8, completing milestone M1. A user message is stored and appended
as an observation event carrying trust, actor, channel and reference. The
perceive process analyses it deterministically (intent, introspection
kind, entities, claims, urgency) and, within the per-cycle model
allowance, enriches it through the perceive prompt. Its handler writes a
perception event, an episodic memory with participants, turns claims into
beliefs whose evidence is the message, and builds a response intent from
state: beliefs and memories for the topic, evidence rows for why-questions,
the uncertain band, contested beliefs and their links, recent learning,
status changes, and honest declines for subsystems that do not exist yet
or for requests with no capabilities. The respond prompt phrases the
intent when a model is assigned; otherwise the reply is a direct
rendering of the intent. response_generated cites the message, the
perception, every belief and memory used, the episode and the model call.
API: conversations, messages with optional wait, one-shot ask; chat UI
with live refresh and a per-reply intent view. Sample mind Dani as a
strictly decoded seed document with an import endpoint and noemactl mind
import. tests/m1 drives all fourteen milestone steps including restart.
Phase 6. Beliefs are propositions with a prior set by epistemic kind and a
confidence computed from typed evidence: sigmoid(logit(prior) + slope ×
(support × diversity − opposition)), where support from a single source
is discounted so repetition cannot manufacture certainty, and bounds keep
the mind away from absolute certainty. Every evaluation stores its
breakdown and appends a version row protected by the append-only
trigger; the belief page shows the arithmetic, evidence for and against,
alternatives, contradictions and history.
Contradiction detection is deterministic first (negation plus token
overlap): a hit emits contradiction_detected, links both beliefs and
makes each counter-evidence for the other so both trend to contested.
Operators can assert beliefs (their statement is the evidence), add
evidence, mark a belief wrong (pinned rejected, history kept) and forbid
an inference; do-not-infer corrections block matching hypotheses.
Retracting a memory retracts the evidence that cited it and re-evaluates
the beliefs concerned.
The associate process proposes low-confidence causal hypotheses from
co-occurring notable observations, and the hypothesis handler turns
admitted ones into beliefs with automatic alternatives, closing demo
scenarios 1 and 2 without any model call.
Andy Dixon