Research · Python stdlib only · zero dependencies

A layered architecture for reasoning that admits what it doesn't know.

nescio-mind explores how an artificial reasoner can build categories from experience, discover the boundaries between them, and — crucially — report honest uncertainty instead of a confident guess.

Structural honesty over confident guessing. The system prefers to surface competing candidates and say "I don't know" as a first-class answer, rather than commit to one arbitrary category.

Open the developmental-study dashboard → See the studies

Five reasoning layers · an eight-step processing pipeline · a consolidation "sleep" cycle · behavioral coherence checks over reproducible run data.

Five reasoning layers

Each layer adds a capability the one below it cannot express — from raw correlation up to meta-cognitive self-doubt.

1Correlation

Pattern matching and statistical co-occurrence — the raw associative substrate.

2Context / Polysemy

The same input can mean different things in different contexts; meaning-splits are discovered from contradictions.

3Causation

Inferring causal relationships — processes that transform inputs into outputs — not just what co-occurs.

4Abstraction / Analogy

Structural alignment across domains: an abstract pattern learned in one place predicts by analogy in another.

5Devil's Advocate

Meta-cognitive structural self-doubt — it challenges, tests, and refines its own abstractions instead of trusting them.

The through-line

Challenging an abstraction produces more precise knowledge, not destruction. Growth through challenge, not rules-based safety.

How a neuron thinks

Every input runs through an eight-step pipeline in RichNeuron.process(). Confidence is a gate, not an assumption — low confidence routes to a more competent neuron or returns honest uncertainty.

  1. Fast-path cache — catalog lookup; scepticism raises the bar for trusting a cached answer.
  2. Inference rules — semantic enrichment before classification (has_leash → domestication).
  3. Exclusion criteria — encode what something cannot be (boundary enforcement).
  4. Sub-systems — Bayesian, pattern-matching, and rule-engine paradigms run in parallel.
  5. Cataloger integration — normalize, apply exclusions, hierarchy-aware voting, ambiguity score.
  6. Confidence gate — return the answer only if composite uncertainty clears the threshold.
  7. Routing — delegate to a more competent neuron, guarded by three anti-hallucination gates.
  8. Honest uncertainty — a best-effort answer flagged ambiguous, with the competing candidates.

Between exposures, a consolidation "sleep" cycle turns novel observations into refined knowledge:

AAccumulate

Unclassified observations are recorded as provisional prototypes during normal processing.

BForm categories

Consistent patterns are clustered across neurons and promoted to new categories.

CReview & challenge

Confidence decays for untested abstractions; the challenger amplifies exceptions and scopes over-broad patterns.

Design principles

The commitments that shape every part of the system.

  • "I don't know" is a first-class outputHonest uncertainty with explicit ambiguity, not a forced pick.
  • Anti-hallucination gatesQuality, domain, and ambiguity gates reject bad or out-of-domain answers.
  • Hierarchy learningAnswers form plant → tree → pine chains; broad and specific coexist.
  • Scepticism as a dynamic modulatorA per-neuron property that raises thresholds and decays with consistent evidence.
  • Anti-VIKI architectureStructural self-doubt prevents valid logic + unchallenged premises → monstrous conclusions.
  • Inference before classificationMeaning is enriched before any category is assigned.

Studies

Exploratory research framed as measurement — persisted, reproducible run data rather than asserted pass/fail. A FAIL or a surprising number is a recorded finding, not a bug.

measurement study headline: does not generalize F10 · developmental curriculum + domain enrichment

Developmental refinement & domain enrichment

A blank-neuron network learns dog, then absorbs cat, and must discover — from cross-population feature importance — that a single feature is the category boundary. The enrichment study then asks whether that discovery mechanism survives richer domains: multiple redundant discriminators, a size-confounded decoy, intra-concept variance, and partial correlation.

Finding. The mechanism does not survive clean enrichment. Adding clean, redundant, perfectly-correlated discriminators breaks it — a coverage-blind, unweighted record-time merge collapses richer concepts into a single blob before the importance layer ever runs. Paradoxically, the only enriched domain that stays coherent is the one with noise. Explore the full comparison — coherence matrix, mutual-information rankings, and category formation — in the dashboard.
Open the dashboard → See the journey → Read the findings ↗ Gap report ↗