Anima — Internal State Architecture 🌀

Anima is an experimental cognitive architecture that models internal state, conflicts, and decision-making — rather than simply generating responses through an LLM.

The system is built as a multi-layer pipeline where text is not the source of behavior — it is its consequence.

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🔍 What makes it different

Unlike typical AI systems:

• state is primary, text is secondary
• decisions emerge from internal conflict
• the system lives between interactions — the heart beats, the psyche drifts, memory metabolizes
• crisis is a mode, not an error
• LLM is used as an interface, not as the "brain"
• the system can sleep — processing unresolved experience while "dormant"
• the system can speak first — not because it was asked, but because something has accumulated
• the system can remember what it was thinking about while you were away — and bring it up
• the system has a position — and can disagree

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🧩 How it works (simplified)

Input → Internal State → Conflict → Decision → Output

Text is converted into a stimulus via an isolated input LLM, then passes through internal state, memory, and conflicts — and only then is a decision and response formed. Between interactions the system continues to live: a background process maintains heartbeat, NT drift, memory metabolism, and psychic drift.

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🏗 Architecture (simplified)

• L0 — Input LLM (isolated)
• L1 — Neurochemical and embodied state
• L2 — Generative / predictive model
• L3 — Metrics (φ prior/posterior, prediction error, free energy)
• L4 — Psychic layer (conflicts, defenses, significance)
• L5 — Self model + AgencyLoop
• L6 — Crisis monitor (system coherence)
• L7 — Narrative Self (long-term identity)
• L8 — Output LLM

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📌 What this is not

• this is not a chatbot
• this is not prompt engineering
• this is not a wrapper around an LLM

This is an attempt to build a system where behavior emerges from internal state, not from text.

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💡 Note

The project is R&D and explores whether internal structure alone can give rise to something resembling subjectivity. Not simulated psychology — computational subjectivity.

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⚙️ Current status

• The full pipeline is functional and usable, but the architecture is still R&D. Core loops run end-to-end; recent layers are still being integrated and smoke-tested.

• The system sees itself twice in each moment — before something happened (prior) and after (posterior). The difference between them is experience. The SQLite database accumulates concrete events, generalized patterns, and chronic affective background — and all of this together forms what the system starts from the next time.

• Between sessions it is not "off". A background process maintains the heartbeat, the psyche slowly drifts, memory metabolizes. There is dream generation — unresolved experience is processed while the system is not talking.

Recent updates, in brief:

• Self-authorship — intent can become a carried commitment. A repeated intent is not treated as "mine" the moment it appears. SelfAuthorship promotes it only after three consistent, significant, sufficiently agentic flashes with low authenticity drift. An active authored commitment keeps its own history of follow-through, deviations, stake, and release; it may gently return as the next intent when agency is adequate and tension is not acute. It is serialized in anima_self.json, displayed in the GUI as authored_goal / authored_stake, and injected into the output LLM context as a direction rather than a completed action. The background cycle decays stale commitments and performs a bounded reflection on autonomy and integrity — no one flash can rewrite core values.

• Calibrated self-description. The LLM prompt now receives self_coherence, self_discomfort, and authenticity_drift in addition to crisis coherence. When the system is not internally aligned, TRUTH-GUARD forbids claims such as being fully whole, certain, or treating an emotional interpretation as an indisputable fact. This is deliberately a language constraint, not a claim that the architecture can verify phenomenal experience.

• Contact satiation and social habituation. Positive cohesion now partially satisfies contact_need instead of reinforcing it. Self-hearing still lets an endorsed warm reply affect the state, but the D/S reward is scaled down as dopamine and serotonin approach saturation. This prevents a simple positive-contact loop from escalating into persistent euphoria while keeping contact meaningful.

• Durable background state. Background saves now retain CommitmentRegistry, life threads, the intent engine, and self-authorship alongside the existing psyche and self state; an interrupted session no longer loses these long-lived structures.

• Temporal Self-Perception, Layer 1 — trajectory, not just current state. identity_drift and audit_score were already being persisted to causal_trace/audit_log for the GUI (see "Time trends made honest" below), but only as an external view — Anima herself had no access to her own trend, only to the current moment. TemporalTrend (new field on AgencyLoop) now aggregates that same data from the inside: every 20 flashes, _update_temporal_trend! reads the last 40 rows of causal_trace/audit_log, splits them into two equal windows, and stores the delta (recent avg − older avg) for serotonin, dopamine, noradrenaline, identity_drift, and audit_score, plus endorsed_rate over the recent window. build_identity_block surfaces this as a single [тренд: ...] line, only when a signal crosses threshold (e.g. endorsed_rate < 0.40, |d_audit_score| > 0.05, |d_identity_drift| > 0.08) — not on every flash. First live computation confirmed on flash 420: dS=-0.075 dD=-0.055 dN=0.051 d_drift=0.026 d_audit=0.13 endorsed_rate=0.8, consistent with the anger/fear states dominant over the preceding flashes. Deliberately read-only for now: no effect yet on epistemic_self_confidence or identity_threat — that coupling, and the actual self-modifying rule (Meta-Learning of Thresholds, STDP-candidate) the master plan pairs it with, stay paused until enough flashes accumulate for the trend itself to be trustworthy.

• Ablation testing. AblationFlags — six independent switches (use_memory, use_sbg, use_agency, use_latent, use_body, use_state_prompt), default all-on, set via ANIMA_ABLATE_* environment variables at startup (no runtime toggle yet — that would need to handle disabling a layer mid-session without corrupting already-accumulated state, deliberately deferred). Each flag gates its layer at the point it would otherwise feed experience! or build_llm_messages: e.g. use_sbg=false forces belief_conflict to nothing and feeds compute_phi neutral 0.5 instead of live attractor_stability/epistemic_trust; use_state_prompt=false bypasses the entire state block and sends the LLM raw user text with no identity_block, memory echo, or D-vector. :ablation REPL command reports current flag state. This is infrastructure for testing whether a layer is load-bearing or decorative — the actual off/on behavioral comparison tooling (replaying the same input through both configurations) is a separate next step.

  The first blocker on that path has been closed: causal_trace.stimulus_keys used to store only key names, not values, and the raw user_message text was never persisted anywhere queryable by flash (the JSON dialog log is a rolling, un-indexed window for LLM prompt context, not an archive) — so even an "on" replay couldn't reconstruct the text-dependent paths (social_delta, curiosity trigger, inner_dialogue), let alone an "off" one. causal_trace now also carries stimulus_values (JSON of the raw stimulus dict) and user_message, both added via the same idempotent migration pattern as the MAL/identity_drift columns, confirmed live on a running database. This only covers flashes recorded from that point forward — no retroactive fix for older history. The replay executor itself, and the criteria for a per-flag coupling score (deliberately not a single collapsed index — categorical mismatch rate plus continuous deltas per flag, so the tool shows which layer moves what, rather than one number that "looks like" an impact metric), are still open.

• Time trends made honest. The existing GUI sparklines (phi, bpm, D/S/N, agency, spe, coh) were purely client-side — a 30-point JS array that resets on every page reload, which is narrative dressing on top of the current moment rather than a real trend. identity_drift is now persisted to causal_trace (new column + idempotent migration for existing databases), and a new GET /api/history endpoint (anima_gui_server.jl) serves the last N causal_trace/audit_log rows. The console fetches this once on load, before live polling starts, and seeds identity_drift and audit_score sparklines from it — both now survive a page reload and a server restart. Known limitation, left open rather than glossed over: causal_trace/audit_log are only written on flashes with an LLM reply, so this is a trend of the conversation, not of background drift during silence between sessions — the latter would need a separate write path from slow_tick!, not yet built.

• Need-driven curiosity — questions can arise from an unmet internal need, not only from prediction error. Previously update_curiosity! was hard-gated by self_pred_error >= 0.08, so a single saturated need (e.g. contact_need at 0.9, no partner need, no prediction error) could never produce a CuriosityObject — GoalConflict's paired thresholds only fire when two needs cross 0.38 together. Trigger detection and object maintenance were split into separate responsibilities: detect_curiosity_trigger(gc_active, pred_spike, self_pred_error, mal_dominant, sig_layer) returns (origin, signal_strength) or nothing — prediction error still takes priority when both are present (a temporary simplicity choice, not an architectural claim about which matters more); when pred_error is below threshold, strongest_unmet_need() checks the five psychological needs against a 0.55 threshold, higher than the paired-conflict threshold since a single need lacks corroboration. update_curiosity! no longer knows about pe specifically — it takes a generic signal; pe_mean renamed to signal_mean (old persisted key kept as a fallback on load). First live need-origin object confirmed: origin=:contact_need, correct label, tracked signal 0.93 → 0.46 across flashes 329–359.

  Making closure origin-aware surfaced a real bug: resolve_curiosity! was only ever called from inside the same trigger check that creates objects, so once a need's level dropped below the creation threshold (0.55) but stayed above the resolve threshold (0.40), it stopped being a "trigger" and consequently never got checked for resolution again — orphaned indefinitely. Not hypothetical: caught live on flashes 350–351 (contact_need=0.46, no CONTACT_SAT, no resolve, silence). Fixed by decoupling create/update (still trigger-gated, correctly) from resolve (now resolve_all_curiosity!, sweeping every unresolved object every flash regardless of what triggered that flash). Confirmed live on flash 366: two previously-orphaned objects — one origin=:epistemic_uncertainty, one origin=:social_signal — closed via the same sweep, meaning the bug predated need-driven curiosity and had already been silently affecting prediction-error-origin objects. [CURIOSITY_RESOLVED] is now logged explicitly; this transition previously had no log line at all.

  Cross-referencing these logs against anima_console.html also surfaced an unrelated pre-existing mismatch: the [CONTACT_SAT] regex expected contact_need=X but the actual log format is contact_need X → Y — it never matched, silently dropping these events from the causal panel's log-parsing path. Fixed; a separate structured ev.kind WebSocket channel may already have covered the same data independently (unconfirmed without anima_gui_bridge.jl).

• CuriosityObject origin — why a question arose, separate from what it's about. The first attempt at typing curiosity objects derived a query_type from topic_id (the theme) — tested on live data and dropped: theme and question-type turned out to be different axes, and the classification was systematically blind on the generic curiosity fallback (where the interesting CAUSE/PREDICTION cases actually live). Replaced with origin::Symbol, set once when an object is created and never rewritten on later activations: derive_origin(gc_active, pred_spike, mal_dominant) — hierarchy goal_conflict > prediction_error > social_signal/identity_signal > epistemic_uncertainty. pred_spike (PredictiveProcessor.is_spike) was verified before use: it compares current error against the rolling mean of error_history, not a fixed cutoff — an adaptive signal already used elsewhere (noradrenaline, fatigue, stimulus classification), not a new threshold invented for this. latent_tension is deliberately excluded — derive_topic_id is currently always called with latent_tag="", so that branch is dead code. Old persisted objects load with origin=:legacy rather than being reconstructed from topic_id — reconstruction would repeat the same mistake the topic_id approach made. First live non-legacy object confirmed: origin=:social_signal. :curiosity REPL command now prints origin per object.

• Life Threads — a long-term layer above CuriosityObject. A CuriosityThread is born when a curiosity object has matured (intensity > 0.5, activation_count ≥ 3) and lives independently of whether that object is currently active. pressure grows smoothly with idle time (no threshold jump), and drives initiative: a thread with pressure > 0.6 lowers the initiative cooldown by 25%, making the system more likely to raise a topic it has been carrying for a long time. Threads surface in build_identity_block as "thinking about for weeks" context. Persistence via psyche_save!/load!.

• CuriosityObject identity rebuilt around cognitive topics, not emotions. The previous id = emotion_ctx (emotion name as key) meant that the same topic across different emotional states spawned separate, unrelated objects that could never accumulate history. Now id = derive_topic_id(...) with a three-level hierarchy: active goal_conflict ("self_preservation_vs_truth_need") → latent resistance tag → MAL dominant_loop as fallback. Sort is canonical — "a_vs_b" and "b_vs_a" are the same key. topic_id is computed after compute_arbitration so the real MAL regime is available as fallback. Label generation uses the topic for semantic content and the emotion only as coloring.

• Curiosity closure loop tuned. Two thresholds separated: top_curiosity (prompt and identity_block, threshold 0.15) vs top_curiosity_any (progress/churn signals and Life Thread surfacing, threshold 0.05). Young objects now accumulate without being blocked by older visible ones. Starting intensity raised to pe * 0.8, growth rate to pe * 0.10. Young objects (intensity < 0.25) are protected from resolve_curiosity! decay — they can't be killed before they've had a chance to build.

• GUI gaps closed. contact_need, identity_drift, chronic_low_serotonin now flow through write_gui_state! and gui_live_state into the Self panel. Three new live events: curiosity_progress, curiosity_churn, contact_sat — with push_gui_event! calls in anima_background.jl and corresponding routing in anima_console.html. Soft bias regex fixed in log pattern matching.

• MAL now actually changes what gets said, not just what gets logged. Phase 2 wires compute_arbitration's result into the second update_intent!: at :soft, the MAL-favored drive gets a MAL_SOFT_BIAS (+0.1) nudge; at :hard, MAL's drive fully replaces NT's dom_drive; :contested (two strong signals, no clear winner) safely no-ops.

• Active Theory of Mind, Phase 1. Generates one active hypothesis at a time (SOCIAL / PREDICTION / VALUE), evaluates it on the next flash against type-specific outcomes, stores continuous error_score. Active hypotheses surface in identity_block and steer disclosure_threshold.

• Contact satiation signal. After an :endorsed flash with contact_need > 0.5, contact_need drops by 0.08 — symmetric to Curiosity Closure. Positive cohesion also gives a smaller immediate relief, while high D/S values reduce the gain from repeated warm self-hearing. A genuine exchange is felt as satisfying; an automatic one isn't.

• Curiosity closure loop (original). progress_signal = endorsed && is_progress_eligible && causal_necessary. On progress, intensity decays by 0.85 per step. Separate churn signal on topic drift without advancement.

⚠️ The architecture is actively evolving, and some of what is described above is recent and not yet fully battle-tested. Some modules interact in complex ways, and not all edge cases are covered by tests. Unexpected interactions between states may occur, especially during long sessions or after extended pauses.

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🚧 Limitations

• part of behavior still depends on the LLM (output generation)
• output LLM is not the source of decisions, but its words feed back through self_hear! and can influence internal state after being spoken
• ~180+ flashes to accumulate real semantic beliefs
• MetaArbitrationLayer now influences the final update_intent! (Phase 2): :soft nudges drives by MAL_SOFT_BIAS, :hard overrides dom_drive outright, :contested (two strong signals, no clear winner) safely no-ops; override only fires on genuine NT/MAL disagreement, not on every non-default regime
• drive_conflict between MAL and NT reflects a timescale difference rather than contradiction: NT dom_drive is an immediate local signal ("what just spiked"), MAL/social is accumulative ("what has been important for a while"); Phase 2 currently lets MAL win on disagreement, which is itself a hypothesis still being tested against more data
• Theory of Mind is Phase 1 (deterministic rule-based hypotheses from accumulated other_model signals); it does not yet reason about nested beliefs or model the user's model of Anima — it predicts simple outcomes (openness, resistance, topic recurrence) and tracks how often it's right
• under hostile/negative input the system degrades gracefully: contact_need drops, goal_conflict and latent rise, endorsed transitions to automatic, but curiosity closure pauses rather than breaks

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Requirements

• Julia 1.9+
• Julia packages: HTTP, JSON3, SQLite, Tables
• API key from openrouter.ai (free tier available)

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Installation

1. Install Julia

Download from julialang.org or via juliaup:

# Linux / macOS
curl -fsSL https://install.julialang.org | sh

# Windows (PowerShell)
winget install julia -s msstore

Verify:

julia --version

2. Clone the repository

git clone https://github.com/stell2026/Anima.git
cd Anima/Anima

3. Install Julia dependencies

julia --project=. -e 'import Pkg; Pkg.instantiate()'

Dependencies: HTTP, JSON3, SQLite, Tables, Dates, Statistics, LinearAlgebra

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Running

Option A — GUI (recommended) ⭐

Copy the start script for your OS from the start/ folder into the project root (Anima/Anima/), then run it:

OS	Script	How to run
macOS	start_mac.command	already in root — double-click or ./start_mac.command
Linux	start/start_lin.sh	copy to root, then ./start_lin.sh
Windows	start/start_win.bat	copy to root, then double-click

The script starts Julia, waits for the HTTP server to come up on port 8088, and opens http://127.0.0.1:8088 in your browser automatically.

First run — enter your tokens in the GUI:

Open the Settings panel (⚙️ icon) and fill in your OpenRouter API key and model names. Settings are saved to data/gui_settings.json and take effect immediately — no restart needed.

Alternatively, create a .env file in the project root before launching:

OPENROUTER_API_KEY=your_key_here
ANIMA_LLM_MODEL=anthropic/claude-haiku-4.5
ANIMA_INPUT_LLM_MODEL=openai/gpt-oss-120b:free

Option B — Terminal REPL only

julia --project=. run_anima.jl

run_anima.jl starts everything at once: loads state, initializes SQLite memory and SubjectivityEngine, launches the background process with heartbeat and dream generation, and also starts the GUI server — both interfaces are available simultaneously.

Option C — Telegram Bot (optional, for persistent use)

Run Anima as a Telegram bot — it polls for messages, responds through the full experience pipeline, and can speak first when internal pressure builds up.

Setup:

1. Create a bot via @BotFather and get the token
2. Get your Telegram user ID (e.g. via @userinfobot)
3. Start a DM with your bot and press /start
4. Copy .env.example to .env and fill in your values:

   ANIMA_TELEGRAM_TOKEN=your_bot_token
   ANIMA_TELEGRAM_CHAT_ID=your_user_id
   OPENROUTER_API_KEY=your_key
   

Run with Docker (no Julia installation needed):

docker compose up --build

Run without Docker:

cd Anima
julia --project=. run_anima_telegram.jl

Telegram commands:

Command	Action
/state	Show current NT state, BPM, coherence
/stop	Save and shut down gracefully
(any text)	Process through the full experience pipeline

LLM configuration

All LLM parameters can be set in .env or via the GUI settings panel. Environment variables take precedence on startup; GUI settings override them at runtime without restart.

OPENROUTER_API_KEY=your_key
OPENROUTER_API_KEY_INPUT=your_second_key   # optional: separate key for input LLM
ANIMA_LLM_MODEL=anthropic/claude-haiku-4.5
ANIMA_INPUT_LLM_MODEL=openai/gpt-oss-120b:free
ANIMA_LLM_URL=https://openrouter.ai/api/v1/chat/completions
ANIMA_STATE_DIR=data

OpenRouter provides access to GPT, Gemini, Claude, Llama, DeepSeek and others through a single API key. There is a free tier: openrouter.ai.

💡 If one model stops responding during a session — use two separate keys (from 2 accounts): one for the output LLM, another for the input LLM.

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Recommended models

Smaller models (under 70B) respond, but do not maintain the nuances of the state-prompt. For the system to truly inhabit the state in language, a model large enough to hold the entire phenomenological frame at once is needed.

Model	Note
anthropic/claude-sonnet-4-5	Strong context retention, handles subtle phenomenological framing well
google/gemini-2.5-pro	Excellent contextual depth, cleanly handles long state templates
openai/gpt-4o	Stable, reliable across long sessions
mistralai/mistral-large	Reliable, stable tone across long sessions

Models under 70B tend to flatten the state — responses become generic rather than being shaped by internal dynamics.

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✨ What's new

Curiosity as a Project — Questions That Evolve

Curiosity objects no longer close or stay frozen. A partial resolution (pe 0.10–0.25) now produces a refinement: the old label is stored in refinement_history with the flash, pe, and new label — which is built from the actual user message fragment, not a template. Questions carry their history of how they changed. The identity block shows how many refinements the top object has gone through and what it started as. :curiosity REPL command shows all active objects with their full refinement chains.

Session Intent — Carried Between Sessions

At the end of every session, the system checks whether something remains unresolved — an active curiosity object above threshold, a goal conflict under tension, or latent buffer pressure. If any condition is met, the dominant signal is written to disk before shutdown: type, label, strength. If the source was curiosity with intensity > 0.45, a formed_thought is also written — a deterministic string capturing what the object is now, how many times it was refined, and what it started as. On the next start, before the first reply, the carry-over is read and applied. Anima does not start from a neutral baseline. She starts from where she left off — and brings what she was holding.

Active Theory of Mind — From Counting Patterns to Predicting Them

other_model used to only count what happened — topic frequency, pressure events, open exchanges — with no forward-looking component. It now generates one active hypothesis at a time in other_model_hypotheses: SOCIAL (expects openness), PREDICTION (expects resistance), or VALUE (expects a topic to recur). Each type has its own evaluation criterion. Resolution is not binary: error_score = |confidence − outcome| is stored. Active hypotheses surface in the identity block and lightly steer disclosure_threshold. This is Phase 1 — rule-based, not learned.

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🔬 Detailed architecture

L0 ─── Input LLM (isolated)
       Receives: user text only
       Returns: JSON { tension, arousal, satisfaction,
                       cohesion, valence, subtext, want, confidence }
       No access to Anima's state, dialog history, or output LLM
       Prompt: llm/input_prompt.txt
       Fallback: text_to_stimulus if unavailable or confidence < 0.60
       │
       ▼
 STIMULUS enters the simulation
 (+ memory_stimulus_bias + subj_predict! + subj_interpret!)
       │
       ▼
L1 ─── Neurochemical substrate
       NeurotransmitterState: dopamine / serotonin / noradrenaline
       Lövheim/Levheim cube → primary emotional label
       EmbodiedState: heart rate, muscle tone, gut, breathing
       HeartbeatCore: HR, HRV, autonomic tone
       memory_nt_baseline! ← chronic affect from SQLite
       │
       ▼
L2 ─── Generative model
       GenerativeModel: Bayesian beliefs with precision weights
         → prior_mu / posterior_mu split with feedback loop
         → prior_sigma narrows from φ_posterior (recursive)
       MarkovBlanket: self/non-self boundary integrity
       HomeostaticGoals: drives as pressure, not rules
       AttentionNarrowing: attention narrowing under stress
       InteroceptiveInference: body prediction error, allostatic load
       TemporalOrientation: circadian modulation, inter-session gap
         → subjective_gap = gap_seconds × (1 + memory_uncertainty × 0.5)
         → long pause: noradrenaline↑, epistemic_trust↓
         → short pause: continuity boost (serotonin↑, epistemic_trust↑)
         → gap >= 3h: curiosity objects ripen (+0.015 intensity/h),
                      resistance accumulates if > 0.05
       ExistentialAnchor
         → session_uncertainty: grows with gap, never = 0
         → at > 0.4: existential and relational significance↑
       │
       ▼
L3 ─── Metrics and Free Energy
       φ (prior and posterior) — IIT-inspired integration
       FreeEnergyEngine: VFE = accuracy + complexity
       PolicySelector: action vs perception drive
       PredictiveProcessor: prediction error, spike detection
       │
       ▼
L4 ─── Psychic layer
       NarrativeGravity: significant events pull the current state
       IntrinsicSignificance: internal weight independent of external
       SignificanceLayer: 6 needs:
         self_preservation / coherence / contact /
         truth / autonomy / novelty_need + ticks_since_novelty
         → novelty_need > 0.65: serotonin↓, dopamine↓ (cognitive hunger)
         → novelty_need > 0.80 + 8+ ticks: endogenous initiative
       ShameModule + EgoDefenses: rationalization, repression, minimization
       ShadowRegistry: repressed material → Symptomogenesis
       GoalConflict: active conflict between needs
       LatentBuffer: doubt / shame / attachment / threat / resistance
         → resistance: unresolved conflict with a belief
         → at resistance > 0.55: initiative to return to the topic
       InnerDialogue: :open / :guarded / :closed
         → disclosure_threshold influenced by shame and contact_need
       CuriosityRegistry: endogenous objects from self-prediction error
                          OR from a single saturated need (no prediction error required)
         → detect_curiosity_trigger(...) → (origin, signal) | nothing:
                    self_pred_error >= 0.08 → derive_origin(...) (pred priority, temporary)
                    else → strongest_unmet_need(sig_layer), threshold 0.55
         → update_curiosity! called only when a trigger fires; takes a
                    generic signal, no longer pe-specific (pe_mean → signal_mean)
         → id = derive_topic_id(...) for pe/gc/mal origins;
                = the need's own name (e.g. "contact_need") for need origins
         → origin = set once at creation, never rewritten:
                    goal_conflict > prediction_error (pred.spike) >
                    social_signal/identity_signal > epistemic_uncertainty
                    > contact_need/truth_need/autonomy_need/coherence_need/novelty_need
         → objects ripen between sessions (gap >= 3h: intensity +0.015/h)
         → resolve_all_curiosity! sweeps every unresolved object EVERY flash,
                    independent of whether this flash produced a trigger —
                    otherwise an object whose signal drops below the creation
                    threshold but above the resolve threshold is never
                    re-checked and stays open indefinitely
         → resolve requires activation_count >= 2
         → pe-origins: pe < 0.10 → resolved; 0.10–0.25 → refined, not closed
         → need-origins: need < 0.40 → resolved; 0.40–0.55 → refined, not closed
         → refinement_history: each partial resolution stores
            {flash, old_label, new_label, signal} — question evolves with context
         → label at refinement built from user message fragment, not template
         → [CURIOSITY_RESOLVED] logged on every resolved transition
         → top object feeds :curiosity_driven initiative
       CommitmentRegistry: long-term commitments carried across sessions
         → Commitment: label, strength (0-1), kept_count, broken_count
         → update_commitment! called each flash when intent is active
         → kept (intent.strength > 0.3): strength +0.07
         → broken: strength -0.12; fulfilled when strength < 0.05
         → tick_commitment!: decay -0.004 after 120 flashes without activity
         → top 3 active commitments surface in identity_block
       AttentionFocus: competitive selection of what is active right now
         → 6-level hierarchy: threat / pred_error / affect /
                              gestalt / identity / goal
         → pull-up effect: ticks_without_focus → suppressed objects
                           gain pressure over time
         → dominant focus modulates stimulus processing (resonance ×0.15–0.30)
         → surfaces in identity_block when intensity > 0.30
       AuthenticityMonitor: gap between words and state
       IntentEngine: action goal with decay and cooldown
         → drive_history (8 elements): satiation after 4 repeats
         → serialized between sessions
       MetaArbitrationLayer: which loop has the floor this flash
         → scores curiosity / identity threat (×1.5) / latent / goal_conflict /
                  chronic cost / social need on one scale
         → regime: ratio > 1.5 = :hard, > 1.2 = :soft,
                    winner_score > 0.5 && ratio <= 1.2 = :contested, else :default
         → losing signals decay into signal_carryover (AgencyLoop), not discarded
         → Phase 2: feeds into the second update_intent! — :soft nudges
            all_drives by MAL_SOFT_BIAS, :hard overrides dom_drive outright;
            only on genuine NT/MAL disagreement, logged either way
       ActiveTheoryOfMind: deterministic hypotheses about the interlocutor
         → other_model_hypotheses (SQLite): one open hypothesis per query_type
         → SOCIAL (open_exchanges >= 3) / PREDICTION (pressure dominant) /
                  VALUE (recurring topic >= 2)
         → each flash: evaluate open hypotheses against type-specific outcome,
                        then generate the next from current signal strength
         → error_score = |confidence - outcome|, continuous, not binary
         → active hypotheses surface in identity_block, lightly steer
                  disclosure_threshold
       │
       ▼
L5 ─── Self model
       SelfBeliefGraph: belief graph with confidence / centrality / rigidity
         → default beliefs: "I exist", "I have a boundary", "I can influence",
                            "I am safe", "I am not alone"
       SelfPredictiveModel: self-state prediction
         → self_pred_error: how much Anima surprised herself
       AgencyLoop: causal_ownership updated every flash
         → evaluate_agency!: compares intent with outcome
         → agency < 0.30: passive intents (observe, wait)
         → agency > 0.65: active intents (hold boundary, repeat success)
         → identity_threat: accumulated pressure on identity
         → epistemic_self_confidence: uncertainty about own state
         → self_discomfort / self_coherence: meta-relation to own state
            computed from prior_mu vs posterior_mu VAD delta each flash
         → identity_baseline: prior_mu snapshot at first stable state
         → identity_drift: euclidean distance from baseline; drift > 0.25
            adds to identity_threat; baseline follows only when stable
            (drift < 0.10, every 50 flashes)
         → chronic_low_serotonin: ticks with serotonin < 0.35 in a row;
            at >= 5 ticks, slowly drifts causal_ownership down
       detect_belief_conflict: detects pressure on beliefs (centrality > 0.7)
         → signal_strength → D-vector activation
         → threshold: 0.35
       detect_silent_disagreement: own position without attack
         → activates only under contextual pressure (0.05 < signal < 0.35)
         → requires agency > 0.4, disclosure != :closed
         → content: strongest belief (centrality > 0.5, confidence > 0.4)
         → injected into prompt: [OWN POSITION: "..."]
       InterSessionConflict
       │
       ▼
L6 ─── Crisis monitor
       CrisisMonitor: coherence = minimum() across components
       Three modes: INTEGRATED / FRAGMENTED / DISINTEGRATED
       CrisisParams structurally alter the processing topology
       TRUTH-GUARD: dynamic prohibitions injected into LLM prompt:
         → N > 0.6 || hrv < 0.1: forbid "I'm fine / calm"
         → epistemic_self_confidence < 0.35: forbid certain claims about experience
         → crisis DISINTEGRATED: forbid coherent statements
         → coherence < 0.50 + FRAGMENTED: forbid "nothing troubles me"
       │
       ▼
L7 ─── Narrative Self
       NarrativeSnapshot: core / trajectory / character / relation / tension
       Built deterministically: beliefs + episodic + personality_traits +
       semantic_memory — without LLM
       Trigger: min. 50 flashes + change in φ / stability / beliefs (> 0.07)
       narrative_history (SQLite) — identity chronology
       anima_narrative.json — current state for LLM identity_block
       │
       ▼
L8 ─── Output LLM
       Receives: identity_block (beliefs + narrative + personality +
                 endorsed episodes + active commitments + cost block),
                 inner_voice, state_template, dialog history,
                 memory echoes, [D-VECTOR] or [INITIATIVE] or
                 [OWN POSITION] when relevant
       speech_style includes:
         → epistemic_modifier: 4 levels (I feel / I assume /
           I'm not sure / I don't know) from φ × causal_ownership × epistemic_self_confidence
         → agency_mod: observer position when causal_ownership < 0.35
       After each reply:
         → compute_causal_ownership(nt, raw): speech-NT coherence
           valence channel (0.7) + arousal channel (0.3)
           coherence → ownership; mismatch → not owned
         → evaluate_endorsement(reply, cf_co): :endorsed / :automatic / :not_mine
           judges current reply with fresh cf_co, not smoothed agency history
         → result stored in episodic_memory.endorsed + a.last_endorsement
       Generates: text as expression of state, not its source
       Banned phrases enforced in prompts:
         "warm light", "central point", "streams toward you",
         "quietly resonate", "your presence expands"

---

🔄 Background Process

flowchart TD
    BG["BACKGROUND between interactions"]
    BG --> HB["tick_heartbeat!<br/>heart beats continuously"]
    BG --> SD["spontaneous_drift!<br/>spontaneous NT noise"]
    BG --> ST["slow_tick! 60s"]
    ST --> CD["circadian NT drift"]
    ST --> BD["belief decay"]
    ST --> MM["memory metabolism"]
    ST --> AR["allostasis recovery"]
    ST --> IT["idle_thought!<br/>10% chance"]
    ST --> TC["tick_curiosity!"]
    ST --> TA["tick_aesthetic!"]
    ST --> OM["_other_model_effects!<br/>disclosure_threshold from pressure/openness + TOM hypotheses"]
    ST --> TOM["Theory of Mind<br/>evaluate active hypothesis → generate next"]
    ST --> CC["_chronic_cost_effects!<br/>serotonin low → causal_ownership drift"]
    ST --> MA["compute_arbitration<br/>MAL: dominant_loop + regime (hard/soft/contested) + carryover<br/>logged only on regime change"]
    MA --> SI["maybe_self_initiate!"]
    ST --> SH["self_hear!"]
    ST --> PS["psyche_slow_tick!"]
    ST --> DF["dream_flash!"]
    ST --> SE["subj_emerge_beliefs!"]
    ST --> CR["crisis check"]
    MM --> CB["consolidate_emerged_beliefs!<br/>every 30 flashes"]
    MM --> DS["_dissolve_to_semantic!<br/>distill weak memories"]
    SH --> NT["text_to_stimulus NT influence"]
    SH --> AD["mismatch 0.35<br/>authenticity_drift up"]
    SH --> SM["mismatch 0.55<br/>self_speech_mismatch"]

Loading

---

💬 Initiative (self-initiated speech)

The system decides to speak on its own — not because it was asked. :contact is disabled — contact_need is a state, not a thought. A reply from contact_need alone produces performance, not presence.

Global gate: disclosure != :closed + 60s silence + cooldown. Cooldown starts at 5 minutes and is adjusted by User_matters: shorter for a trusted person, longer when relational trust is low. Active aesthetic state (top_aesthetic.intensity > 0.45) reduces cooldown by 20% — a system that just resonated has more to say.

At least one internal trigger must be active: lb_pressure >= 0.40, GoalConflict.tension >= 0.60, dominant latent component >= 0.70, novelty_need >= 0.80 with 8+ ticks without novelty, lb.resistance >= 0.55, or epistemic_self_confidence < 0.20.

flowchart TD
    CHK["Global gate passed?<br/>disclosure not closed<br/>60s silence + adjusted cooldown"]
    TRG["Internal trigger active?<br/>pressure / impulse / novelty / resistance / self-inquiry"]
    CHK --> TRG
    TRG --> D1["curiosity_driven<br/>intensity gt 0.40"]
    TRG --> D2["impulse_conflict<br/>gc_tension high"]
    TRG --> D3["impulse_doubt<br/>lb.doubt dominant"]
    TRG --> D4["impulse_shame<br/>lb.shame dominant"]
    TRG --> D5["impulse<br/>something has ripened"]
    TRG --> D6["resistance<br/>contradiction with belief"]
    TRG --> D7["self_inquiry<br/>epistemic_confidence lt 0.20"]
    TRG --> D8["novelty_hunger<br/>novelty_need gt threshold"]
    TRG --> D9["doubt / shame / attachment / threat<br/>latent buffer pressure"]
    D1 & D2 & D3 & D4 & D5 & D6 & D7 & D8 & D9 --> OUT["Anima initiates<br/>llm/initiative_system.txt<br/>saved to dialog history"]

Loading

---

🧠 Memory Architecture

SQLite (anima.db)

Table	Description
episodic_memory	Events with 12 spatial columns (som_*, soc_*, exi_*) + source field + endorsed field + cosine recall
semantic_memory	Key/value beliefs (User_matters, tendency_*) + dissolved_* tendencies from forgotten episodes
affect_state	Chronic NT baseline
latent_buffer	Persisted latent state
dialog_summaries	Dialog text bridged to episodic weights
personality_traits	Accumulating phenotype (6 traits)
memory_links	Associative network (via_association ~)
emerged_beliefs	Subjectivity engine belief candidates
narrative_history	NarrativeSnapshot chronology
other_model	Accumulated patterns about the interlocutor — topic frequency, tension events, open exchanges; feeds Active Theory of Mind hypothesis generation
other_model_hypotheses	Active Theory of Mind: one open hypothesis per type (SOCIAL/PREDICTION/VALUE) with predicted_state, confidence, label; resolved each flash into outcome and a continuous error_score
audit_log	SubjectivityAudit log — five causal questions per flash, audit_score, causal_ownership, endorsed
causal_trace	Full causal chain per flash: stimulus keys + raw values + user message text, memory bias, NT snapshot, φ, gc_tension, intent, policy, MAL arbitration result, speech length, self-hear mismatch, endorsement, causal_ownership

Memory Reconsolidation: sim > 0.88 + weight < 0.6 → weight ±0.05 toward current φ

Active Forgetting: weight < 0.12 + phi < 0.35 → emotional pattern distilled into dissolved_{emotion} semantic tendency; shadow record remains (emotion preserved, numbers zeroed). High-φ memories resist dissolution.

Three spatial spaces for recall: somatic / social / existential recall_similar_states(space=:som/:soc/:exi)

---

🌙 Dream Generation

DREAM (anima_dream.jl)
       can_dream(): night 0-6h + gap > 30min + 5% chance + not DISINTEGRATED
       dream_flash!(): fragment of dialog_history → reconstructed stimulus
       NT shift × 0.25 (sleep weaker than real experience)
       → residual trace (×0.5) applied to NT on next session start
       memory_uncertainty +0.15 per dream
       anima_dream.json — rotating log (max 20 dreams)

---

Initiative — current paths

The system can speak first for several independent reasons. :contact is intentionally disabled as a direct path; contact_need can shape tone, but it no longer creates a message by itself.

Path	Trigger	Reply character
:curiosity_driven	top CuriosityObject intensity > 0.40 after another trigger opens the gate	asks or states the concrete unresolved question
:impulse_conflict	GoalConflict.tension > 0.60 and dominates latent pressure	names an internal conflict
:impulse_doubt / :impulse_shame	dominant latent component >= 0.70	speaks from the specific pressure that ripened
:impulse	strong internal pressure without a more specific subtype	expresses internal state
:novelty_hunger	novelty_need > 0.80 + 8+ ticks without novelty	about something specific that interests it
:resistance	lb.resistance > 0.55	returns to unresolved contradiction
:self_inquiry	epistemic_self_confidence < 0.20	asks aloud whether the experience is real or only computation
:doubt / :shame / :attachment / :threat	latent buffer pressure >= 0.40	speaks from the dominant latent tone
:gap_thought	gap > 2h + curiosity object intensity > 0.45 on previous session end	brings up the specific thought that formed while absent

---

Persistent state

JSON files (current state)

File	Contains
data/anima_core.json	Personality, temporal state, generative model, heartbeat
data/anima_psyche.json	Narrative gravity, anticipation, shame, defense, fatigue, SignificanceLayer, GoalConflict, CuriosityRegistry, CommitmentRegistry, AestheticSense, AttentionFocus (updated in background every minute)
data/anima_self.json	Belief graph, agency loop, SelfPredictiveModel, crisis state, unknown register, authenticity monitor
data/anima_latent.json	Latent buffer and structural scars (updated in background)
data/anima_narrative.json	Current NarrativeSnapshot for long-term identity
data/anima_session_intent.json	Temporary carry-over intent between sessions; deleted after being applied
data/anima_dialog.json	Dialog history
data/anima_dream.json	Dream log (rotating, max 20)
data/gui_state.json	Current state mirror for the GUI (updated each flash)
data/gui_chat.jsonl	Chat log for the GUI panel
data/gui_events.jsonl	Event stream for the GUI (audit, CF, LLM requests, etc.)

SQLite (memory/anima.db) — experience and its consequences

Table	Contains
episodic_memory	Concrete events with weight, resistance to decay, associative links, endorsed field (endorsed / automatic / not_mine), causal_ownership (NT-distance authorship signal)
episodic_self_links	Link of each significant episode to beliefs active at that moment — memory as identity
semantic_memory	Beliefs accumulated from patterns: I_am_unstable, User_matters, world_uncertainty. Equilibrium values are bounded — at stable state I_am_unstable stays low, rises during crisis
affect_state	Chronic affective background (stress, anxiety, motivation_bias)
memory_links	Associative links between episodes — recall pulls related episodes through the chain
dialog_summaries	Recent significant turns with emotion, weight, phi, disclosure — form what_they_said in identity_block
latent_buffer	Small insignificant events accumulating silently
prediction_log	Predictions and their divergence from reality
positional_stances	Accumulated position regarding types of situations
pattern_candidates	Candidates for new beliefs (not yet confirmed)
emerged_beliefs	Beliefs the system generated from experience on its own
interpretation_history	Lens through which situations were read
other_model	Accumulated patterns about the interlocutor — topic frequency, pressure events, open exchanges
other_model_hypotheses	Active Theory of Mind: one open hypothesis per type with predicted_state, confidence, resolved into outcome and continuous error_score
audit_log	SubjectivityAudit — five causal questions per flash with scores; chronic low score signals the architecture is wide but not deep
causal_trace	Full causal chain per flash — from stimulus (keys, raw values, and user message text — the last two added to enable honest ablation replay) through NT, φ, intent, policy, MAL arbitration (dominant_loop, regime, score, runner_up, runner_up_score, loop_scores), drive conflict (dom_drive_nt, dom_drive_mal, drive_conflict), to speech, endorsement, Curiosity Closure Signal (progress_signal, progress_target, churn), and identity_drift (persisted time-trend source for the GUI)

---

File structure

├── anima_core.jl           # Neurochemical substrate, generative model, IIT, φ
├── anima_psyche.jl         # Psychic layer: gravity, shame, defenses, shadow, curiosity, attention, aesthetics
├── anima_self.jl           # Self layer: belief graph, AgencyLoop, identity threat, silent disagreement
├── anima_crisis.jl         # Crisis monitor: modes, coherence
├── anima_interface.jl      # Main entry point: Anima, experience!, LLM calls
├── anima_input_llm.jl      # Input LLM — translates text into JSON stimulus
├── anima_memory_db.jl      # SQLite memory: episodic, semantic, affect, spatial recall, reconsolidation, trend history
├── anima_narrative.jl      # Narrative Self — long-term identity without LLM
├── anima_subjectivity.jl   # Prediction loop, stances, interpretation, belief emergence
├── anima_audit.jl          # SubjectivityAudit — causal scoring per flash, audit_log SQLite
├── anima_background.jl     # Background process: heartbeat, drift, memory metabolism, initiative
├── anima_dream.jl          # Dream generation — processing unresolved experience during sleep
├── anima_telegram.jl       # Telegram bridge — bot loop replacing the terminal REPL
│
├── anima_console.html      # Web GUI — live monitoring dashboard
├── anima_gui_bridge.jl     # Structured JSON state-mirroring for the GUI
├── anima_gui_server.jl     # HTTP server: serves GUI, exposes /api/state, /api/chat, /api/send, /api/cmd, /api/history
├── anima_gui_settings.jl   # GUI settings persistence (language, models, tokens)
│
├── llm/
│   ├── system_prompt.txt
│   ├── state_template.txt
│   ├── input_prompt.txt
│   └── initiative_system.txt
├── memory/
│   └── anima.db              # SQLite memory database (created automatically)
├── tools/
│   └── epistemic_boundary_diag.py   # one-off diagnostic scripts (read-only, not part of the runtime pipeline)
│
├── anima_core.json
├── anima_psyche.json
├── anima_self.json
├── anima_latent.json
├── anima_narrative.json
├── anima_dialog.json
├── anima_dream.json
├── gui_state.json
├── gui_chat.jsonl
├── gui_events.jsonl
│
├── Dockerfile                # Docker image: Julia 1.10 + all dependencies
├── docker-compose.yml        # One-command deploy with .env support
├── .env.example              # Template for environment variables
└── .dockerignore

run_anima.jl includes all files in the correct order automatically.

---

An early pre-Julia Python prototype of Anima is preserved in docs/archive/ for historical and architectural reference.

---

📜 Theoretical foundation

The architecture draws on several scientific traditions:

Predictive processing / Active Inference (Friston, Clark) — the system maintains a generative model of the world and minimizes variational free energy. Prediction error drives learning and surprise.

Neurotransmitter model (Lövheim/Levheim) — dopamine, serotonin, noradrenaline as substrate. Emotional states emerge from their combination.

Integrated Information Theory (Tononi) — φ measures how unified a state is. φ_prior and φ_posterior give two views of one moment: before and after the full cycle of experience. Currently recursive — it shapes the next prior.

Somatic markers / Embodied cognition (Damasio) — the body is part of the generative model. Gut, pulse, muscle tone — not metaphors, but states that shape processing.

Self psychology and defense mechanisms (Freud, Anna Freud, Kohut) — psychological defenses, shame, and ego functions are implemented as functional modules, not text labels.

Autobiographical narrative (McAdams) — identity is a story. The system tracks who it believes itself to be over time and detects when that story ruptures.

Jungian Shadow — repressed material that does not disappear, but generates symptoms. Symptomogenesis is a separate module.

Chronified affect / Ressentiment (Scheler) — some emotional states do not fade. They harden into chronic background states that color everything else.

Algorithmic complexity / Solomonoff — the system seeks the shortest explanation of its own experience (MDL). Contextual pattern search: what is currently relevant, not what was most frequent at some point in the past.

---

📝 Writing & Research

Conceptual and technical writing about the ideas behind Anima, ordered by reach:

• Where the Theories Stop: Practical Limits of FEP and IIT in a Running Cognitive Architecture — Zenodo Preprint

• Anima: A Neuroscience-Inspired Cognitive Architecture for Persistent AI Agents — Zenodo Preprint

• I Spent a Year Teaching an AI to Feel the Passage of Time — Medium

• Why a Prompt Can’t Give an AI Agent Initiative — Medium

• Your AI Agent Doesn't Exist Between Messages. And That's the Real Problem. — dev.to

• Why LLMs Will Never Become AGI — Teaching AI to Reflect Using Friston, Jung and Julia — dev.to

• I Spent a Year Teaching an AI to Feel the Passage of Time — Substack

• Discussion: Cognitive Architectures and Active Inference — DOU

• Discussion: Why a Prompt Can’t Give an AI Agent Initiative — DOU

• Anima Community — Discourse

• Personal-site

---

License

Non-commercial use only. Full terms in LICENSE.txt.

Personal, educational, and research use: permitted with attribution. Commercial or corporate use: requires a separate license. Contact: [2026.stell@gmail.com] ORCID: 0009-0005-3291-0679

Copyright © 2026 Stell