A source's track record is kept per (evidence family, belief subject) and
updated only by outcomes: predictions derived from a belief resolving, and
operators marking a belief wrong. With three or more outcomes in a domain
the record adjusts the credibility of that source's new evidence in that
domain, and the adjustment is written on the evidence row. Standing in one
domain never moves standing in another, and the trust-level credibility
constants are never rewritten. Migration 0021 is additive; belief pages
show each cited family's record in the belief's domain.
Beliefs gain a temporal scope (persistent, transient or unknown, classified
deterministically from the proposition), an observation time and, for
transient states, a validity interval. When a newer belief conflicts with
an older transient one observed at least a minute earlier, the older belief
is closed (valid_until set, status superseded, a supersedes link, a version
and an event saying 'state changed') with its score untouched: 'the door
was open at 10:00' stays true of 10:00 when it is closed at 10:05.
Simultaneous conflicting reports and persistent propositions contradict as
before. Introspection lists superseded states with their validity.
Migration 0020 is additive.
Every evidence row now records its derivation (direct, assertion, operator,
external, prediction, consolidation, inference, reflection, dream,
retrieval), its independent-source family (an actor, an integration, an
operator, a prediction) and its root event or memory ids. Confidence
groups evidence by family: the strongest primary item counts in full, each
further primary item adds a quarter, capped at 1.5x, and derived items
count only when the family has no primary member. Three derived
re-expressions of one utterance therefore weigh exactly what the utterance
weighs, while three independent families still add up.
Producers (conversation, association, consolidation, predictions,
metacognition, dreaming, operator and seed input, contradictions) supply
their ancestry. Existing rows are marked legacy_unknown with family =
source; nothing is invented. The belief page shows per-family
contributions and the replay test checks that provenance is reconstructed
identically in the sandbox. Migration 0017 is additive.
Phase 10. Metacognition is a cognitive process with deterministic
self-checks (insufficient evidence, single-source reliance, model output
without evidence, repeated failed hypotheses, stale contradictions,
calibration overconfidence); its warnings compete for attention and,
when admitted, add counter-evidence or open an investigation goal.
Reflection reviews the period since the last run (prediction errors,
rejected beliefs, corrections, status changes, completed goals,
contradictions, calibration), turns repeated failures into procedural
lessons written once, and records a reflection event; a model may only
word the prose within the mind's budget.
Consolidation clusters unconsolidated episodes by shared stemmed terms,
creates a belief whose confidence is computed from the episodes as
evidence and a semantic memory linked to each episode by derived_from,
and records jobs with inputs and outputs. Wording may come from the
consolidate prompt within a per-run model budget; the job says which.
Operators can run, pause and reject; originals are never touched.
The narrative hook keeps a five-part first-person narrative built from
state and stored with narrative_updated events. The notification hook
applies explicit thresholded rules, aggregates repeats within a cooldown
and records suppressions with reasons. A scheduled job runs
consolidation and reflection per mind, switching the runtime mode while
it works. API, Background tab, notifications page with unread badge,
overview narrative and warnings, CLI commands, docs/cognition.md.
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