whetstone_RSA/HANDOFF-2026-04-21-presentation.md

Handoff: Presentation Materials — 2026-04-21

Context

This session was primarily infrastructure and communication work, not pipeline or training work. The purpose was to prepare presentation materials for a technical audience (data science background) covering the actual transformer training approach in whetstone_RSA.

The session also established a new workstation (spindoctor) as a Gitea client for this repo. Gitea SSH runs on port 2222, not 22. An SSH config entry for borgswarm with Port 2222 is in place at ~/.ssh/config on spindoctor.

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What Was Created

presentations/slide_deck_brief.md

Full briefing document for a slide deck agent. Describes a 35-slide deck in two acts:

• Act I (slides 1–17): Refers to the existing rsa_tiny_models_talk.md as-is. No changes to that file.

• Act II (slides 18–35): New material covering the actual training work. Each slide is described with: title, content, visual reference, speaker notes.

The brief references each HTML visual by filename and includes honest framing of the weak baseline results and their structural cause.

presentations/visual_rsa_pipeline.html

The core RSA pattern as a four-box pipeline:

Fuzzy input → RSA Gate → Typed Contract → Deterministic Execution

Includes two worked examples: a developer tools query and a whetstone_DSL prereq gate decision. Dark theme, standalone HTML.

presentations/visual_training_pipeline.html

Six-step pipeline from run artifacts to trained specialist:

1. whetstone_DSL run corpus (1000 runs)
2. Gate extraction (extract_gate_rows.py)
3. Gate decomposition (1 gate → 2 binary classifiers)
4. TSV generation (gen_prereq_op_rsa_data.py)
5. Fabricate training (~213K params, 4000 steps, GPU desktop)
6. Specialist checkpoint (~800KB)

Includes the corpus caveat (17 unique templates) as an inline note.

presentations/visual_gate_decomposition.html

Shows how prereq_op_selector decomposed into two independent binary classifiers after discovering needs_validate_intake is always True across all 1297 accepted rows.

Displays:

• Gate A: needs_resolve_dependencies — 76.9% positive, 69.4% baseline accuracy
• Gate B: needs_architect_review — 19.8% positive, 75.0% baseline accuracy
• Accuracy bars with honest majority-classifier context
• Three bottom notes: why two models, what the corpus lacks, next step pointer

presentations/visual_dataset_anatomy.html

Dataset statistics for the prereq_op_selector extraction:

• Top-line numbers: 1000 runs, 1297 accepted rows, 24 schema-drift rejected, ~17 unique templates
• Class balance bar chart for both binary gates
• Corpus limitation callout (why 1297 rows ≠ 1297 diverse examples)
• Sample rows in Fabricate TSV format (label <TAB> hop <TAB> text)

presentations/visual_model_tiers.html

The entropy → model tier hypothesis as a five-tier diagram:

Tier	Description	Size
0	Deterministic rules	0 params
1	Tiny specialist (Fabricate)	~213K params / ~800KB
2	Medium specialist (planned)	~10× tier 1
3	Large specialist (planned)	~16× tier 1
4	SLM / LLM (escalation only)	billions

Includes an entropy axis, the current sizing hypothesis as pseudocode, and the "check schema before moving up a tier" caveat.

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What Was Not Changed

• rsa_tiny_models_talk.md — untouched. Act I of the deck uses it as-is.
• All docs/, src/, include/, semantic/, tools/, schemas/ — untouched.
• All sprint documents — untouched.

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Known State

The Fabricate training project has been deleted. The trained specialist checkpoints (/mnt/storage/fabricate_runs/) are on the GPU desktop, not accessible from spindoctor. The training results cited in the visuals come from HANDOFF-2026-04-12-session2.md.

The HTML visuals are standalone — no external dependencies, no build step required. Open in any browser.