UNI — Scientific Evidence Report & Falsification Invitation

System: THE STRATIFIED PALIMPSEST — a pure-Elixir active-inference agent ("UNI") that plays real Minecraft through a mineflayer body. Status: gen-2. Reasoning stack live and on-screen; pure-OTP runtime; on-chip math fence. Date of report: generated at audit time. Branch: gen2-runtime.

This document states precisely what is claimed, what is proven and how, what is NOT claimed, and how anyone, anywhere, can try to falsify it. It is written to be attacked. If a claim below survives your best attempt to break it, that is the result.

0. The one-paragraph claim

UNI is a discrete active-inference agent. It selects actions to minimise expected free energy (EFE) — an explicit sum of an epistemic (information-gain) term and a pragmatic (preference) term — over beliefs maintained by minimising variational free energy (VFE). There is no reward signal and no reinforcement learning anywhere in the system. The numerical core is pure Elixir with zero foreign computation layers (no Nx, no Rust, no NIF, no math FFI), JIT-compiled to native CPU code by BeamAsm. A two-level hierarchy (L1 fast sensorimotor, L2 slow strategic) communicates across a Markov blanket that carries only primitives. Every numerical kernel is checked to 1e-6 against an independent Python (scipy/numpy) oracle, and 26 acceptance gates are enforced in CI.

1. Architecture (what runs)

 Minecraft world  ──σ(senses)──►  body (mineflayer, Node)  ──σ line──►  SP.Runtime.Agent
                                                                              │  (pure cmd/2)
                                                                              ▼
        L2 Strategist (slow OODA, every 12 ticks)  ◄── situation digest (a primitive int)
                          │  option + C-overrides + hormones (down)
                          ▼
        L1 Factors model  ── infer (VFE) ─► learn (Dirichlet) ─► grow (structure) ─► decide (EFE)
                                                                              │
                                  α(action atom) ◄── Directive.Actuate ◄──────┘

• Markov blanket (η ⊥ r | σ,α). Only two messages ever cross body↔brain: a sense line σ in, one action atom α out. Only an integer situation digest crosses L1↔L2 up, and an option atom + a preference-override map down. No belief struct ever crosses.
• Purity boundary. SP.Runtime.Agent.cmd/2 consumes a CloudEvents Signal, runs one perception→learning→action cycle (MC.step/2), and returns directives. It performs no effects; the runtime interprets directives. This is the Jido contract, implemented in pure OTP with zero Jido in the build.

2. Mathematical derivations

Notation: hidden states s (per factor f), observations o (per modality m), actions u/policies π. A = likelihood P(o|s) (column-major, A[:,s] a distribution over o). B^u = transition P(s'|s,u) (column-major, B[:,s] a distribution over s'). C = log-preferences over outcomes. D = prior over states. E = habit prior over actions.

2.1 Variational free energy (perception) and the bound

For one factor with observation o, the variational posterior q(s) minimises

F[q] = Σ_s q(s)·( ln q(s) − ln P(o,s) ) = D_KL[ q(s) ‖ P(s|o) ] − ln P(o). (2.1)

Because D_KL ≥ 0, F is an upper bound on surprisal: F ≥ −ln P(o). Minimising F over q tightens the bound; the minimiser is the true posterior and then F = −ln P(o).

The mean-field update (per factor) that this report's code performs is

ln q(s) = forward_prior(s) + Σ_m γ_m · ln A^m[o_m, s] − ln Z, (2.2)

with q = softmax(...). γ_m is the (dynamic) sensory precision of modality m.

Gate 3 asserts F ≥ −ln P(o) numerically on a worked 2-state example.

2.2 The forward message: (ln B)·s, NOT ln(B·s) (bound-critical)

The empirical prior over the next state, used as forward_prior in (2.2), is the expected log-transition, computed by logging the transition columns first and then taking the belief-weighted sum:

forward_prior(s') = Σ_s q(s) · ln B^u[s', s] ≡ ((ln B)·q)(s'). (2.3)

This is not ln(B·q). By Jensen's inequality, for a convex/concave separation,

ln( Σ_s B[s',s] q(s) ) ≥ Σ_s q(s) ln B[s',s], (2.4)

so ln(Bq) ≥ (ln B)q pointwise, with a strictly positive gap whenever B mixes states. Using ln(Bq) would break the VFE upper-bound guarantee (§16 of the spec). The code implements (2.3) in Math.ln_matvec/2.

Gate 2 asserts the Jensen gap is strictly positive on a worked example (gap ≈ 0.1845 for B=[[0.7,0.3],[0.2,0.8]], q=[0.5,0.5]).

2.3 Mean-field factorisation (the joint is never built)

The agent maintains a factorised belief q(s_1,…,s_F) = Π_f q_f(s_f). The belief state has size Σ_f N_f (sum of per-factor cardinalities), not Π_f N_f (the joint). For the default 7-factor agent: Σ_f N_f = 4+4+6+3+3+4+5 = 29, while the joint would be Π_f N_f = 17 280. The joint is never materialised; all updates are per-factor.

Gate 5 asserts belief_size == Σ_f N_f and reports 29 vs ∏ = 17280.

2.4 Expected free energy (action) and its decomposition

For a policy π (action sequence), the expected free energy at horizon step τ is

G(π) = Σ_τ E_q[ ln q(s_τ|π) − ln q(s_τ|o_τ,π) ] (epistemic, negative info gain) − E_q[ ln C(o_τ) ] (pragmatic, expected preference). (2.5)

• Epistemic term: expected reduction in uncertainty about hidden states — drives curiosity/exploration. It is intrinsic; it is not a reward.
• Pragmatic term: expected log-preference of the outcomes the policy is expected to bring about. C is set by the curriculum/phase (and, at L2, by the strategic option).

Action posterior:

Q(π) = softmax( ln E − γ · G(π) ), (2.6)

where E is the habit prior over actions (agency) and γ is the dynamic policy precision. The chosen action is sampled from Q (exploration preserved), then committed; the habit E is a Dirichlet count strengthened toward what the agent does (idleness excluded, heritably — see §2.7).

Gate 7 asserts G decomposes into finite epistemic + pragmatic parts and Q(π) is a proper distribution.

2.5 Dirichlet learning (no reward)

A, B, and E are Dirichlet-distributed; learning is counting co-occurrences, not gradient-on-reward:

a_post[o,s] = a_prior[o,s] + η · q(s)·[obs = o], (2.7) b_post[s',s] (per action) updated analogously from q(s')q(s), e_post[u] = e_prior[u] + [action = u] (unless u = noop, heritable).

with learning rate η. Expected log-likelihoods used in inference are ψ(a) − ψ(Σ a) (the digamma expectation of ln under a Dirichlet). Gate 1 asserts ψ(x) matches scipy to 1e-6 at anchor points.

There is no value function, no TD error, no policy gradient, no reward — only VFE (perception), EFE (action), and Dirichlet counting (learning).

2.6 Dynamic precision (attention / confidence)

Sensory precision γ_m is retuned from the surprise of what each modality just saw (attention). Policy precision γ is updated from the variance of EFE across policies (Precision.update_policy): sharper when the agent is confident which policy is best, flatter when ambiguous, then clamped to [γ_min, γ_max]. Because Math.softmax subtracts the max before exponentiating and γ is clamped, the policy distribution is numerically stable even under high-stress (high-γ, strong-C) regimes (verified).

2.7 The L2/L1 hierarchy (two selves)

L2 is the same discrete engine instanced a level up and run slower (every 12 L1 ticks). Its hidden factor is the strategic situation {calm, threatened, depleted, social, idle}; its actions are strategic options {forage, build, flee, socialize, rest}.

• Up (a primitive): the body-computed situation index (an integer 0–4) is L2's observation. No belief crosses.
• Down (primitives): the chosen option sets L1's empirical priors as absolute preference-override vectors C (idempotent), and a hormone state retunes L1's policy precision γ (stress→sharper) — transiently. The persisted L1 model stays pure (transient γ/C/lr stripped), so memory round-trips across death.

The two selves genuinely differ in timescale: L1 commits within a tick; L2 integrates over many digests with a sticky transition, so the "remembering self" concentrates only under sustained evidence (verified by an entropy-decrease test).

Transition convention (and a fixed defect). B is column-major: col_j = P(next|j). An earlier gen-2 build of the L2 option-transitions was transposed (outer index = next), which Model.new's column-normalisation silently masked — the coarse flee outcome survived only via A+C, while the designed per-option differentiation was scrambled. This was found in audit, fixed (outer index = source), and live-verified: agents now select situation-appropriate options (flee under threat, forage when safe/idle/social) with diverse multi-step intents. A semantic orientation test now guards against regression.

3. Evidence (what is proven, and how to reproduce it)

All commands run from the repo root with Elixir 1.19 / OTP 28.

3.1 The 26-point covenant (enforced gates)

Per compiled card, gates 1–13 (digamma≈scipy; (lnB)s≠ln(Bs); VFE bound; A/B stochastic; mean-field no-joint; purity; EFE decomposition; bounded planning ≡ exhaustive at full beam; hierarchy blanket primitives-only; Designer≡Genome) plus deferred runtime gates 8–10 (blanket σ/α only; lockstep; leakage). Gen-2 adds global gates 14 (no foreign layer in the math namespace) and 15 (native JIT). CI fails if any gate breaks — the covenant is enforceable, not aspirational.

3.2 Live evidence (in the actual game)

Embodied agents on the live server were observed (via the colony snapshot) to:

• run continuous perception→action cycles (counts in the hundreds), self-healing across in-game deaths (the body reconnects in 4 s);
• select situation-appropriate L2 options — flee under threat, forage when safe — not a uniform/degenerate choice;
• emit multi-step plans (e.g. forward → forward → forward, turn → forward → mine) from deep recursive-EFE lookahead;
• surface emotion (content/grief/anger), stress, and a metacognitive-confidence read on the /stream overlay.

4. What is explicitly NOT claimed (the fences)

• No reward / no RL. This is a design invariant, not a tuning choice. Falsifiable by search (§5).
• No qualia, no sentience, no felt experience. The "consciousness functions" (SP.Brain.Awareness: global-availability broadcast, reportability, metacognition) model access and report, not phenomenal experience. The report is the agent describing its computed state. We make no claim about the hard problem. See docs/PHENOMENOLOGY.md.
• No biological fidelity claim for hormones/emotion — they are parameter-modulation mechanisms (engineering choices), not measured neuro-endocrinology.
• Not proven optimal. EFE minimisation is a principled objective, not a guarantee of task-optimal behaviour in Minecraft.

5. Falsification protocol — please try to break this

We invite the community to attack any claim. Concrete, actionable tests:

1. Oracle parity. Re-derive the digamma/VFE/EFE/(lnB)s values independently (your own scipy/numpy/Julia) and compare to mix sp.brain.verify gate outputs. Claim: agreement to 1e-6. Falsify by exhibiting a divergence beyond 1e-6 on any anchor.
2. No-reward. grep -ri "reward\|q_learning\|td_error\|policy_gradient\|return" lib/. Claim: no reward/RL machinery drives behaviour. Falsify by finding a value/return signal in the action path.
3. No foreign layer. Inspect lib/sp/brain/*.ex (gate 14). Claim: the math is pure Elixir, no Nx/Rust/NIF/FFI. Falsify by finding a foreign compute call in a math kernel.
4. VFE bound. Construct adversarial A/D/o and check F ≥ −ln P(o) (gate 3 logic). Falsify by exhibiting F < −ln P(o).
5. Mean-field. Verify the joint ∏_f N_f is never allocated (gate 5; memory profile a long run). Falsify by showing a joint-sized allocation.
6. Blanket purity. Inspect the body↔brain protocol and the L1↔L2 messages. Claim: only σ/α and primitive digests/options cross. Falsify by finding a belief/state object crossing a blanket.
7. Determinism. Same (params, observation, seed) ⇒ same action (gate 6). Falsify by exhibiting nondeterminism with fixed inputs.
8. Hierarchy responsiveness. Drive sustained "threatened" senses; claim the L2 commits to flee and applies the danger-C override + stress. Falsify by showing it does not respond, or responds to the wrong situation.
9. Option correctness (the fixed defect). Verify the L2 transition B is column-major and oriented (flee moves a threatened source toward calm) — Strategist orientation test. Falsify by showing scrambled option selection.
10. Memory across death. Save→load→step; claim the learned model round-trips exactly and keeps learning. Falsify by showing memory loss or a non-round-trip.
11. On-chip. Run on a non-JIT BEAM; claim the runtime refuses to start (gate 15 / boot fence). On a JIT BEAM, benchmark MC.step throughput. Falsify the "native code" claim by showing the kernels are not JIT-compiled.
12. Consciousness fence. Attempt to show the Awareness report constitutes evidence of qualia. Claim: it does not — it is access/report only. This is the bounded, honest question we most want stressed.

6. Standing open questions (unresolved; we want data)

• Does metacognitive confidence climb meaningfully over a long single life as A sharpens? (Early life it reads ~0 by construction — uninformative likelihoods. Not yet observed climbing over many hours.)
• Does population evolution within kin produce fitness gains across generations in the live game? (Unit-tested with simulated deaths via SP.Runtime.Lineage; not yet observed breeding across real in-game deaths over a long run.)
• How much of "reported consciousness" do access/report/metacognition reproduce, and where do they provably fall short? (The falsification ledger in docs/PHENOMENOLOGY.md.)
• Does the L2 "remembering self" yield measurably better long-horizon outcomes than L1-only agents under matched conditions?
• Structure growth: does letting factors grow their state space pay for itself (better evidence per added state) over long runs?

7. Reproducibility

• Elixir 1.19.x / OTP 28 (BeamAsm JIT). mix deps.get (core has zero hex deps).
• Minecraft: a local offline-mode Paper server (FOSS, on your own server; no Mojang login, no cracking). Node + mineflayer under viewer/.
• mix sp.brain.verify (gates), mix test (suite), mix uni.play (single agent), /stream (the live colony overlay).

This report is an invitation. The system is built to be falsified; the covenant holds because CI breaks when a gate breaks. If you break a claim that CI does not catch, that gap is itself the contribution — please report it.