settled-reach/docs/workshops/llm-voice-pipeline/round-2-proposals.md

title, description, type, status, workshop, agent, round, created

title	description	type	status	workshop	agent	round	created
Round 2 Proposals	Synthesised proposals from round 1 outputs for convergent evaluation in round 2	workshop	archived	llm-voice-pipeline		2	2026-03-07

LLM Voice Pipeline Workshop — Round 2 Proposals

Compiled by: Team Lead (synthesis of Round 1 outputs) Round: 2 — Convergent Evaluation Date: 2026-03-07

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Preamble: What Round 1 Settled

All 7 participants favor Option 3 (LLM re-voicing). Round 2 does not revisit that choice. Instead, it presents three concrete architecture variants that resolve the open tensions from Round 1.

D-123/D-124 framing note: D-123 (2026-03-05) frames generative AI as "an authoring tool, not a runtime system." D-124 defers in-game live AI but explicitly "leaves the door open." All three proposals below walk through that door to varying degrees. Each proposal must state how it amends D-123 and supersedes D-124.

Resolved inputs for all proposals:

• Runtime: llama-cpp-rs with GGUF Q4_K_M quantization (C-6)
• Determinism: cache-as-determinism — generate once per seed, cache result (C-7)
• Thread isolation: separate thread pool for inference vs. world generation (C-8)
• Base text is the fallback and the LLM seed (C-5)
• Existing zone RON content (~50 lines/role) survives as base text seeds (C-9)

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Proposal A: Conservative — Behaviors Only, Tells Locked

Scope: Re-voice observable behaviors only. Dialogue deferred to a future spike.

Tell protection: Base-text passthrough. Tells are never sent to the LLM. A new tell_behaviors: Vec<String> field is added to the NPC data model alongside observable_behaviors. The pipeline checks the field tag, not the content. Tell lines ship as-authored in all cases.

Model: Gemma 2 2B (Q4_K_M, ~1.5GB). Single candidate — no fallback model needed because behaviors are short-form (5-15 words) and the prompt is simple.

Injector model: Culture injector clauses (10-20 per culture) + trait modifier (1 per trait) + mood tag. Total injector budget: ~150 tokens. Authored by copy team, sourced from culture RON speech fields. Negative injectors required (explicit NOT-lists for lore contamination).

Content tiers:

• Baked: Sova Transit District + other hub zones, pre-voiced at build time
• Pre-voiced: background queue, prioritized by proximity and plot-criticality
• Fallback: base text (always available, always complete)

D-123 amendment: D-123 is amended to "authoring tool AND background runtime enhancement." D-124 is superseded — this IS the in-game AI system, scoped to behaviors.

Pros: Smallest risk surface. Behaviors are short-form, easy to validate. Tell safety is absolute (passthrough). Spike is simple: one model, one prompt template, one content type.

Cons: Leaves dialogue scaling unsolved. Dialogue is arguably the higher-value target for re-voicing. May feel like a half-measure if ambient behaviors get culture voice but dialogue stays template-assembled.

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Proposal B: Two-Track — Behaviors + Tells with Semantic Core

Scope: Re-voice observable behaviors AND tell behaviors, with different pipelines. Dialogue deferred.

Tell protection: Constrained re-voicing via semantic_core tag (Gestalt's proposal). The Tell struct gains a semantic_core: String field that names the phenomenon the tell must preserve (e.g., "avoidance_behavior", "nervous_fidget", "concealment_tell"). The re-voicing prompt includes an explicit constraint: PRESERVE: {semantic_core}. Culture-voice the expression, not the phenomenon.

Model: Gemma 2 2B (Q4_K_M). Two prompt templates: free re-voicing (ambient behaviors) and constrained re-voicing (tells).

Injector model: Same as Proposal A (150 token budget), plus semantic core constraints for tells (~20 additional tokens per tell).

Content tiers: Same as Proposal A.

D-123 amendment: Same as Proposal A.

Pros: Tells get culture voice (a Van Maanen's Star tell reads differently from a Sovari tell — richer world). The semantic core constraint is testable: spike can measure whether the phenomenon survives re-voicing. Two-track architecture is future-proof for dialogue.

Cons: Constrained re-voicing is harder to validate than passthrough. If the model fails to preserve the semantic core, the tell is corrupted — and the failure is subtle (not missing, just wrong). Requires the copy team to author semantic core labels for every tell type.

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Proposal C: Full Pipeline — Behaviors + Dialogue, Tells Locked

Scope: Re-voice observable behaviors AND dialogue. Tells pass through untouched (same as Proposal A).

Tell protection: Base-text passthrough (same as Proposal A). Tells are never re-voiced.

Model: Gemma 2 2B for behaviors; potentially Qwen2.5-3B (Q4, ~2.0GB) for dialogue if 2B quality is insufficient for longer-form output. Spike tests both on behaviors and dialogue separately.

Dialogue re-voicing: Dialogue lines already have access tier and trust tier tags (Paula's observation). The re-voicing prompt includes these as constraints alongside culture injectors. Dialogue is longer-form (15-40 words) and requires more prompt context (relationship state, conversation topic).

Injector model: Culture injectors (150 tokens) + dialogue context (relationship, access tier, trust tier — ~80 additional tokens). Total prompt budget ~400-500 tokens for dialogue.

Content tiers: Same as A, but baked content includes pre-voiced dialogue for hub NPCs.

D-123 amendment: D-123 is fully superseded. The AI pipeline is both an authoring tool and a runtime system. D-124 is superseded.

Pros: Solves the full content scaling problem in one architecture. Dialogue is where culture voice matters most to players (what NPCs SAY). Avoids the half-measure feeling of behaviors-only.

Cons: Larger spike scope. Dialogue quality at 2B may not meet the bar — may force a model size increase (3B) which tightens RAM. Two content types means two prompt templates, two validation passes, two quality bars. More can go wrong.

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Resolution Matrix

Each participant should evaluate all three proposals against their domain and answer:

Question	Your answer
Which proposal do you recommend?	A / B / C
Are there blockers in your recommended proposal?	Yes/No + details
Can you live with each of the other two proposals?	Yes/No per proposal
What is the minimum change to your non-preferred proposals that would make them acceptable?

Open questions to resolve in Round 2:

Q-R1-01 (tell literacy model): Is the player's tell literacy cross-NPC grammar or fresh-each-time? — Gestalt, answer this. It determines whether Proposal B's constrained re-voicing is safe.

Q-R1-02 (scope): Proposals A and B defer dialogue; Proposal C includes it. — Tyre, Paula, assess feasibility and quality risk for each.

Q-R1-03 (tell data model): All three proposals require tell_behaviors as a separate field. This is now a prerequisite, not a question. — Gestalt, Tyre, confirm this is implementable.

Q-R1-04 (token budget): 150 tokens for culture injectors in A/B, 400-500 for dialogue in C. — Miri, is 150 sufficient for cultural philosophy? Troblum, what's the throughput impact of 500-token prompts vs 150?

Q-R1-05 (minimum CPU spec): This workshop cannot define it — it's a product decision. For Round 2, assume: 4-core CPU from 2019 or later (e.g., Intel i5-9400, Ryzen 5 3600). — Troblum, is this sufficient for Gemma 2B Q4 background inference?

D-123 tension (Mellanie): All three proposals amend or supersede D-123. — Mellanie, is the proposed amendment language acceptable? Paula, does this conflict with any narrative architecture constraints?