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Noema documentation

Cognitive configuration

The per-mind settings that shape a mind: workspace capacity, attention weights, budgets and background cognition.

Each mind carries its own cognitive configuration: how many thoughts it can hold, how it weighs what to attend to, how fast it cycles, how much it may spend on models, and which background processes run. These settings are data, edited from the interface or API, normalised on every write and reloaded into the running mind without a restart. They are the knobs an experiment turns.

On this page

  1. The settings
  2. Runtime settings
  3. Daemon-wide limits

The settings

Out-of-range values fall back to defaults rather than being rejected silently or accepted dangerously; every change is an operator action in the mind's history, and the mind encodes it as an autobiographical memory.

Field Default Effect
workspace_capacity 7 thoughts admitted per cycle; the weakest are displaced when full
working_memory_capacity 9 decaying buffer of recently active items
attention_weights see below per-feature multipliers used by attention scoring
cycle_min_interval 500ms debounce between stimulus-triggered cycles
idle_cycle_interval 60s timer for idle cycles when nothing arrives
max_cycles_per_minute 60 rate budget; excess triggers are skipped and logged
llm_calls_per_cycle 3 shared cap for processes and handlers in one cycle
daily_token_budget 500 000 per-mind token budget, enforced by the router
working_memory_decay 0.85 activation multiplier per cycle
loop_detect_window / loop_detect_threshold 20 / 5 repeated candidate fingerprints that trigger a metacognition warning
consolidation_enabled / _interval / _min_episodes / _llm_calls on / 6h / 3 / 5 scheduled generalisation of episodes
reflection_enabled / _interval / _llm_calls on / 12h / 2 scheduled review of the period's mistakes and lessons
narrative_llm off let a model word the internal narrative (deterministic otherwise)
notify_min_confidence / notify_min_salience / notify_cooldown 0.85 / 0.7 / 1h thresholds and aggregation for operator notifications
dreaming_enabled / dream_hours / dream_interval / dream_llm_calls / dream_max_memories / dream_max_hypotheses off / 01-06 / 24h / 2 / 40 / 5 the dreaming mode; see dreaming

Attention weights

Attention scoring uses a fixed set of ten features, each computed deterministically and multiplied by the mind's weight: salience, urgency, novelty, affect, goal_relevance, consequence, confidence, contradiction, operator_priority and context_relevance. Defaults favour urgency (1.2), goal relevance (1.1) and operator priority (1.5). Drives and affect modulate the effective weights within bounds each cycle, and the effective weights used are recorded on every thought_selected event, so you can always see why one candidate beat another. The cognition page describes each feature.

Runtime settings

A small set of tunables that are safe to change while running (the global consolidation and dreaming pause switches, notification behaviour) are stored in the database and edited in the interface; they take effect on the next scheduler tick. Everything else requires a restart or, for per-mind settings, a reload command that the interface issues automatically after an edit.

Daemon-wide limits

Above the per-mind settings sit a few daemon-wide limits: a global daily token budget that no mind can exceed, a cap on concurrent model calls, and the size of the worker pool that runs cognitive processes for every mind. Per-mind budgets are enforced under the global one, so one talkative mind cannot starve the others. Secrets used by any part of the system (provider keys, integration secrets, second-factor secrets) exist only as references encrypted under a master key, never as values in configuration.

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