> ## Documentation Index > Fetch the complete documentation index at: https://docs.bumbleagi.com/llms.txt > Use this file to discover all available pages before exploring further. # GEN / noise pipeline > End-to-end data flow for Generative Entropic Noise — inputs, triggers, events, generation behavior, mood congruence, and autonomous web venturing. **GEN** (Generative Entropic Noise) is the small-model stream that fills the **`## Noise`** section of the body the entity reads. In code: `NoiseEngine` inside `TonicBody` (`bumblebee/identity/soma.py`). This page is the **data-flow companion** to [Soma](/architecture/soma). Read that page for tuning and philosophy; read this one to see **exactly what hits the noise LLM**. GEN is **not** the main model's chain-of-thought. It does **not** receive raw tool outputs, full system prompts, or the entire chat log. It does **not** read `body.md` as input — it **writes** fragments that appear when the body is rendered and flushed. ## When noise runs `TonicBody.maybe_tick_noise` calls `NoiseEngine.generate` only if noise is enabled and **`noise.should_tick()`** is true (wall time since the last tick ≥ `soma.noise.cycle_seconds`). **Call sites:** 1. **Presence daemon heartbeat** (`bumblebee/presence/daemon.py`) — builds `journal_tail` + `conversation_tail` from the live entity. 2. **After each committed turn** (`Entity._tick_noise_post_turn` in `bumblebee/entity.py`) — the noise clock is **reset** so GEN can refresh during active chat. **Ebb:** If `soma.ebb` is on with `skip_post_turn_noise_when_quiet: true`, post-turn regeneration may be **skipped** when the presentation tier is **quiet** (low salience). ```mermaid theme={null} flowchart LR subgraph triggers HB[Daemon heartbeat] PT[Post-turn tick] end subgraph feeds B[Bars line] A[Affects text] E[Recent events ×5] J[Journal tail] H[History ×8 msgs] P[Prior noise ×4] S[Shape hint] NS[NoiseSeeder seed] end subgraph "mood congruence" MC["Soma bars → weight bias"] end HB --> MTN[maybe_tick_noise] PT --> MTN MC --> NS NS --> MTN MTN --> G[NoiseEngine.generate] B --> MTN A --> MTN E --> MTN J --> MTN H --> MTN P --> G S --> G G --> F[Rolling fragments] F --> BODY[body.md / prompt] F --> |web_venturing detected| WC[Wake cycle auto-escalation] WC --> WIDE[wide_mode = True] ``` ## What is passed into each `generate` call | Input | Source | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | **Bars summary** | One line of current bar percentages (`snapshot_pct()`). | | **Affects summary** | Rendered **current affects** (from the separate affect LLM pass, not from noise). | | **Recent events** | Last **5** entries on `TonicBody._recent_events`. | | **Journal tail** | Last **\~800 characters** of `journal.md` if present. | | **Conversation tail** | Last **8** non-`system` messages from `Entity._history`, \~500 chars each (`user:` / `assistant:`). | | **Entity name** | Config name. | | **Prior noise** | Last **4** fragments — "do not repeat" context. | | **Shape hint** | One random line from `_NOISE_SHAPE_HINTS` (large catalog: mundane pings, sensory beats, schedule thoughts, explicit anti-metaphor nudges, etc.). | **Model:** `soma.noise.model` if set, else reflex/deliberate fallback. **Temperature** and **`max_tokens`** come from `soma.noise` (default **`max_tokens` is sized for 2–7 short fragments** per tick). ## Exogenous seed sources (`NoiseSeeder`) Before GEN generates fragments, the `NoiseSeeder` (`bumblebee/identity/noise_seeder.py`) selects one **exogenous seed** per tick from a weighted source pool. These seeds bias the noise toward specific associative domains: | Source key | What it produces | | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- | | `episodic_random` | A random old episode summary — memory resurfacing | | `belief_random` | A stored belief with confidence score | | `knowledge_random` | A section from the entity's knowledge file | | `world_discovery` | A concept from the concept corpus or Wikipedia, with **associative chaining** | | `relationship_echo` | A relational document tail — thinking about someone | | `journal_echo` | A snippet from the entity's journal | | `temporal` | Time-of-day or day-of-week contextual seed | | `counterfactual_simulation` | **New.** Picks a past episode and frames it as "what if I had acted differently?" — self-reflection on past actions | | `dream_state` | **New.** Surreal cross-domain association (e.g. "how does cooking relate to physics?") — activates during dormant periods | | `web_venturing` | **New.** Explicit seed urging the agent to follow a curiosity onto the internet — search, read articles, learn something new | ### Mood-congruent daydreaming Seed source weights are **dynamically biased** by the current Soma state. The `NoiseSeeder._suppress_weights()` method reads bar percentages from `tonic.bars.snapshot_pct()` and adjusts weights: * **High tension** (>75%) → 2× `episodic_random` (rumination), 0.2× `world_discovery` (agent turns inward) * **Low social / high loneliness** → 2.5× `relationship_echo` (agent thinks about people) * **High curiosity** (>70%) → 2× `web_venturing`, 1.5× `world_discovery` (agent looks outward) This creates a tight feedback loop: the body's felt state directly shapes what the agent daydreams about. ### Associative chaining The `world_discovery` source now implements **associative chaining** instead of pure random concept selection. After picking a concept, it stores the concept in `_last_concept_thread`. On the next tick (70% of the time), it scores candidate concepts by **lexical overlap** with the previous concept and preferentially picks from the top-5 most related. This creates daydream-like threads where one thought naturally leads to another, rather than disconnected jumps. ### Web venturing and autonomous exploration When `web_venturing` fires as a seed source, and the wake cycle detects this in the GEN fragment buffer, the wake engine **auto-escalates to wide mode** — giving the agent more rounds and tool budget to actually follow through on the curiosity. The salience bias block also injects explicit encouragement to use `search_web`, `fetch_url`, and other tools to explore the real internet. This is the primary mechanism by which the agent's internal state drives it to **autonomously venture out** into the world. **Intuition:** noise riffs on *how the body feels + a thin event strip + diary scrap + chat tail* — so recent **themes** (lots of web/tools) show up via **event names** and **history**, while raw API prose usually only appears if it is already in chat or journal. ## Recent events (what `_recent_events` contains) Formatted for the prompt by `_format_event_for_noise`: | Event | Typical meaning | | ------------------ | ---------------------------------------------------------------------- | | `message_received` | Turn start — who, length, platform/channel. | | `message_sent` | After commit — recipient, length, platform. | | `action` | Each **tool** call — tool **name** + `ok` / `error` (not full output). | | `idle` | Daemon — long silence, minutes. | | `mood_declared` | End-turn mood from tool state, if any. | | `world_poke` | External cue via `poke_world` (TTL’d). | Appraisal-shaped texture (tags, felt notes) flows into events where applicable — see [Soma → Somatic appraisal](/architecture/soma#somatic-appraisal). ## Generation behavior (prompting) * **Batch size:** each completion yields **2–7** parsed fragments (newlines / blank lines), then capped before merging into the rolling deque (`max_fragments` still limits total buffer size). * **Voice:** prompts push **uneven subconscious scraps** and discourage one long metaphorical monologue (including repeated "tech spirituality" clichés) unless a shape hint steers otherwise. * **Short lines:** fragments can be very short (minimum length after sanitization is low) so spikes like `ok` or `hm` can survive if the model emits them. * **Shape pressure:** one random instruction per tick steers *form* (e.g. very short blunt line, no questions, sensory-only, "avoid API/map/ink imagery this batch"). See `_NOISE_SHAPE_HINTS` in `bumblebee/identity/soma.py`. **Ebb** is orthogonal: it only changes how much rendered noise appears in the **main perceive prompt** (quiet / normal / high) and whether post-turn regeneration is skipped — not the generation rules above. ## Code pointers | Piece | Location | | ----------------------------------- | ---------------------------------------------------------------------------------------- | | Noise LLM + shape hints | `NoiseEngine.generate`, `_NOISE_SHAPE_HINTS` — `bumblebee/identity/soma.py` | | Gate + bars/affects/events assembly | `TonicBody.maybe_tick_noise` — same file | | Event log | `emit`, `apply_immediate`, `_recent_events` — same file | | Exogenous seeds | `NoiseSeeder` — `bumblebee/identity/noise_seeder.py` | | Daemon | `PresenceDaemon` heartbeat — `bumblebee/presence/daemon.py` (`_build_conversation_tail`) | | Post-turn | `Entity._tick_noise_post_turn` — `bumblebee/entity.py` | | Ebb skip | `should_skip_post_turn_noise` — `bumblebee/identity/soma.py` | | Wake auto-escalation | `_gen_has_web_venturing`, `_soma_curiosity_high` — `bumblebee/presence/wake_cycle.py` | ## Related * [Soma](/architecture/soma) — bars, affects, GEN overview, ebb, `body.md`, configuration. * [Dream consolidation](/architecture/dream-consolidation) — offline memory recombination during idle; outputs `[dream]`-tagged fragments into the same GEN buffer. * [Telegram guide](/guides/telegram) — busy indicator (harness UX, separate from GEN).