feat: work-type modes — Talk / Poker / Build / Explore / Study
The manual version of the architecture's `route` step: Brian points her at the TYPE of work and her register + tools shift to match. Biggest single lever on the 'meh' problem (a mode card can demand decisive/technical/generative, countering gpt-4o's default warm-vapor). - modes.py: Build (heads-down engineering — decisive, concrete, tradeoffs, no listicles), Explore (open brainstorming — generative, riffs + honest catch, spawn threads, don't converge early), Study (poker review away from the table — analytical, GTO-aware, teaching; read-only lookups + analyze_spot). Cash relabeled Poker (key kept for compat). - UI: mode selectors (desktop + mobile) get all five; badge taps now cycle modes. - design: docs/COGNITION.md (the society-of-parts control-plane sketch). - tests: presence + tool-gating for the new modes. Suite 85, ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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# Lyra — Cognition Architecture (sketch)
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> The "society of mind" direction: instead of one giant model we keep nagging with
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> stricter prompts, a society of small specialized parts cooperate to produce each
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> turn. **Most parts are cheap deterministic code (heuristics, math, learnable
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> weights); the LLM is the exception, reserved for the few irreducibly-generative
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> jobs.** Everything is anchored to who she is and tuned by feedback.
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## Principles
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1. **LLM is the exception, not the rule.** Bookkeeping, scoring, routing,
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thresholding, retrieval → code. Generation (language, novel reasoning, memory
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compression) → LLM, called sparingly.
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2. **Mind ≠ Mouth.** A capable "mind" (decide / reason / use tools — helpfulness is
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fine) is separate from a "mouth" (the character voice). This lets each be the
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best model for *its* job — and makes the eventual fine-tune easy: you only have
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to teach a small model to *sound like Lyra*, not to *be smart*.
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3. **Anchored.** A fixed identity anchor governs the mouth so self-composed prompts
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can't drift into generic-helper vapor. (Already exists: `self_state.IDENTITY_ANCHOR`.)
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4. **Tuned by feedback, not just hand-tuning.** Learnable *weights* (over register,
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memory, parts) nudged by 👍/👎 give real adaptation *without* fine-tuning a model.
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5. **Allocation is the craft.** Cheap-deterministic where signal is clear; LLM where
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judgment/language is needed; **hybrid** (heuristic common-case, escalate to LLM on
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ambiguity) where possible.
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## The blackboard: `TurnContext`
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Parts don't call each other directly — they read from and write to a shared turn
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state (a blackboard). Heterogeneous parts (heuristic / LLM / weights) cooperate by
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annotating it. The composer reads the finished blackboard to build the prompt.
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```
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TurnContext {
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# --- inputs ---
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user_msg, session_id, history, now
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# --- perception (heuristic) ---
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moment : { kind: emotional|strategic|casual|existential|meta,
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sentiment: -1..1, tilt: 0..1, urgency: 0..1 }
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# --- state (code) ---
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mood, drives, anchor
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# --- retrieval (math: embeddings + cosine) ---
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recalled : [memories] # spreading activation
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threads : [active thoughts]
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profile, narrative
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# --- control (heuristic + learnable weights) ---
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register : warm | coach | dry | tender | hype # how to sound
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intent : console | push_back | teach | riff | act
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mode : talk | cash | ... # tool allow-list
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use_tools: bool
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route : { mind: <model>, mouth: <model> } # which model per role
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# --- generation (LLM, sparing) ---
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deliberation : "her private thinking" # mind
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tool_results : [...] # mind + tool exec
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reply : "final text" # mouth
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# --- learning (heuristic/online) ---
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weights : { register_prefs, memory_weights, ... } # persisted, feedback-tuned
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}
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```
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## The parts
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| # | Part | Type | Does | Exists today? |
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|---|------|------|------|---------------|
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| 1 | **perceive** | heuristic | sentiment + classify the moment + tilt/urgency from session signals & his language | ✗ (new) |
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| 2 | **recall** | math | embeddings → relevant memories, active threads, profile, narrative | ✓ `memory.recall*`, `cognition.activate` |
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| 3 | **sense_state** | code | load mood / drives / anchor | ✓ `self_state`, `IDENTITY_ANCHOR` |
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| 4 | **route** | heuristic + weights | pick register, intent, mode, and which model is mind vs mouth | ✗ (new; partly `modes`) |
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| 5 | **decide+act (tools)** | LLM (mind) / code | does this turn need a tool? run it | ✓ tool loop in `chat` |
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| 6 | **deliberate** | LLM (mind) | "what do I actually think" — private substance pass | ✓ `chat._deliberate` |
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| 7 | **compose** | code | assemble the final prompt from anchor + register + intent + deliberation + recall + tool results + voice rules | ✓ `build_messages` (becomes the composer) |
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| 8 | **speak** | LLM (mouth) | write the reply in her voice, streamed, anchored | ✓ `llm.chat_call` |
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| 9 | **learn** | heuristic/online | on 👍/👎 or reaction, nudge `weights` (which register/memory worked) | ✗ (new; data exists in `ratings`) |
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Most of the society (1,2,3,4,7,9) is **free, instant, deterministic, debuggable.**
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The LLM shows up in only ~2–3 places (5/6 = mind, 8 = mouth).
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## One chat turn
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```
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user msg
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│
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▼
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[1 perceive]──heuristic: emotional? strategic? tilting? (free)
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│
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[2 recall]───math: what lights up (memories, threads) (free)
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[3 sense]────code: mood, drives, anchor (free)
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│
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[4 route]────heuristic+weights: register? intent? mind/mouth? (free)
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│
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[5 act]──────MIND model: tools if needed ─────────────┐ (LLM, only if needed)
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[6 deliberate]──MIND model: what do I actually think │ (LLM, gated)
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│ │
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[7 compose]──code: build the prompt ◄──── anchor ──────┘ (free)
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│
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[8 speak]────MOUTH model: the reply, in her voice, streamed (LLM)
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│
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▼
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reply ──► (later) [9 learn]: 👍/👎 nudges weights (free, async)
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```
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## What we reuse vs. build
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- **Reuse (already scattered through the code):** recall/activation, self_state +
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anchor, drives (in `dream`), modes (tool gating), the deliberation pass, the
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prompt assembly (`build_messages`), tool loop, ratings store.
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- **Build new:** the `TurnContext` blackboard + an explicit pipeline runner; the
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**perceive** heuristic; the **route** part (register/intent + model routing); the
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**learn** weights loop. Mostly *unifying* existing pieces into one legible control
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plane, plus 2–3 small heuristic parts.
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## Phasing (smallest first)
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- **P1 — frame:** define `TurnContext`, refactor the current chat turn into the
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explicit pipeline (perceive=stub → recall → sense → route=mode-only → deliberate →
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compose → speak), single model. Low-risk refactor; makes the structure real.
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- **P2 — control plane:** real `perceive` (sentiment/moment) + `route`
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(register/intent). Now her framing adapts to the moment, deterministically.
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- **P3 — mind/mouth split:** route picks a separate voice model for `speak`. Plug a
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character mouth (Claude / local / later a fine-tune). A/B vs. single-model.
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- **P4 — learning:** `weights` over register/memory, nudged by ratings → cheap
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adaptation, no fine-tune.
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- **P5 — her voice:** a small fine-tuned "Lyra voice" model drops into the mouth slot.
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## Open decisions
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- **Mouth model**: Claude (warm, cloud) vs. local character vs. fine-tune. The mouth
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is the crux; it must render richly (8B local may flatten).
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- **perceive**: pure heuristics vs. a tiny classifier vs. embedding-to-exemplar
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clusters. Probably hybrid.
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- **scheduler**: fixed linear pipeline (simple, v1) vs. drive-based/parallel later.
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- **tool location**: mind decides+runs tools, mouth only renders (clean split) — vs.
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letting the mouth call tools (needs a tool-capable mouth).
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- **latency budget**: how many LLM calls per turn is acceptable live (cheap mind +
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streamed mouth keeps it ~2).
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```
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