ActionEngram · Paper Pipeline Console

Run configuration

Paper workspace

Mode

Workspace directory

Engine (shared, in place)

Input papers

Select one or more source papers from your machine, or type a path below.

Output format

HTML (primary) DOCX PDF

Ensemble (TAMU gateway)

Model pool / jury

Free multi-model separation of powers: Laguna drafts → Devstral-2-123B reviews (critic) → Gemma-4-31B breaks ties (jury). Laguna-only falls back to single-judge.

Draft refine rounds ?--max-rounds (build_research.py). The DRAFT self-improvement loop that builds 10-PAPER.txt: evaluate → guideline → revise → gate, using the ensemble; converges when the gain stays below ε for K rounds. This is loop ① — separate from render QC.

Render QC rounds ?--qc-rounds is a PER-APPLICATION cap, NOT cumulative. The same value independently bounds ② the body-QC prose loop (once over the paper) and ③ each figure's geometry-QC fix loop. Worst-case figure-fix calls ≈ figures × this value. See the Architecture tab.

Daily $ cap

Min figures

Max figures

QC viewpoint temperatures

Rotated per round for "different points of view".

Figure temperatures

Toggles

Skip body QC ?Body QC is the judge→correct loop that runs on a working copy of the paper's PROSE: the critic scores it against the rubric, the synthesist rewrites the weakest sections, and a revision is accepted ONLY if it improves without regressing (never adds notation, never drops a section). Skipping it renders the draft as-is — faster, but no prose polishing. (--no-qc) Skip figures (--no-figures) Dry run ?Dry run parses the paper and renders a text-only skeleton with NO model calls and NO spend — it just reports the checks (sections parsed, citations resolved, HTML skeleton size). Use it to validate the config before a real run. (no spend)

Equivalent command

The console posts this to the backend POST /api/runs; the same command runs from a terminal.

Idle Elapsed 0.0s · ETA to finish — · healthy

0% · idle — the bar/ETA track the live SSE heartbeat; if no progress arrives for >4s it flags a possible stall/crash.

Parse

read 10-PAPER.txt

✓

Resolve cites

source map

✓

Body QC loop

loop ② · multi-viewpoint

✓

Figures

loop ③ · author + geometry QC

✓

Assemble HTML

cite-style + gate

✓

Ensemble

Laguna·Devstral·Gemma

free jury · 3 models

Sections

enforced from config

Figures kept

— / 19

large fonts, 0 hard defects

Citations

—

author-year, STM-only

GPT spend

$0.00

Laguna = free

Status

Idle

—

Press Run pipeline to simulate a full pass. Stages animate through the same A→E flow the backend executes; the QC, Figures, Citations and Audit tabs fill in as it runs.

Full data flow for advanced users: two engines, one shared ensemble, and four bounded self-improvement / QC loops (amber ⟲). Each loop stops early on convergence or an anti-thrash freeze.

Where QC is applied — 4 bounded loops

#	Loop	Engine · script	Round cap	What it does
①	Draft REFINE	build_research.py	--max-rounds (3)	ensemble judge→correct on prose sections; converges when gain < ε for K rounds
②	Body QC	render_paper.py · run_qc	--qc-rounds (5)	ensemble judge→correct on the whole paper's prose; temperature-rotated (multi-viewpoint)
③	Figure geometry QC	render_paper.py · author_figures	--qc-rounds, per figure	deterministic geometry lint → LLM fix → re-lint, for EACH figure independently
④	Format conversion	to_format.py	--passes, per format	convert HTML→docx/pdf, diff against the HTML, fix, repeat

Is “QC rounds” cumulative? No. --qc-rounds is a per-application cap, not a total. The same value independently bounds ② the body-QC loop (once over the whole paper) and ③ each figure's geometry-QC loop. Worst-case figure-fix calls ≈ figures × qc-rounds (e.g. 19 × 5), though every loop stops early on convergence or an anti-thrash freeze, so actual rounds are usually fewer. Loop ① (draft) uses a separate --max-rounds; loop ④ (conversion) uses a separate --passes. These are three different knobs in the config.

Judge→correct loop on a working copy. The synthesist temperature rotates each round (0.2, 0.5, 0.8, 0.4, 0.7) for diverse revisions; a revision is rejected if it changes the section set or reintroduces symbolic notation.

QC log will stream here on run…

Each figure: draft (varied temperature) → safety/well-formedness gate → deterministic geometry lint (out-of-bounds = hard drop; small font <14px / text-collision = soft, auto-fixed).

Rendered inline as (Short name, year), external STM only (internal author corpus dropped). Keys are knowledge-driven from LTM-citation-style.txt [CITATION-KEYS].

Key	STM id	Year	In-text

These are the ACTUAL long-term knowledge nodes injected into the pipeline's LLM prompts (drafter, reviser, figure author, citation renderer). The backend serves the FULL file via GET /api/knowledge/:node; the boxes below show representative excerpts. Edits + PUT take effect next run and are replicated when you create a new workspace.

Context injected into every draft / revise prompt 27,695 / 28,000 chars

5 LTM nodes are concatenated (hard cap 28,000). Each large-context prompt repeats the task ≥4× (task anchoring) so the model stays focused across this context. These files are the distilled result of the whole 10-PAPER-new.html optimization — not shallow summaries; the excerpts here are abridged for display only.

LTM-writing-style.txt 198 lines · 12.9 KB — voice, zero-notation, term discipline, section hygiene

Full file loaded via GET /api/knowledge/LTM-writing-style. Excerpt:

[UNIVERSAL] BANNED PHRASES — remove on sight, replace with the concrete thing ("leverage", "delve", "in today's world", … 25 entries incl. math tokens $$ \( \[ \pi \delta \rho \lambda \theta \frac \sum \times)
[UNIVERSAL] NO SYMBOLIC NOTATION, EVER — no LaTeX, $…$, greek, or backslash macros anywhere; name every quantity in plain English (Trace, Metaknowledge, Provenance, Decay, Policy, Utility). Confine nothing to "one place": there is no escape hatch.
[UNIVERSAL] TERM DISCIPLINE — reasoning trace = raw recorded path; action engram = a trace governance VALIDATED + PROMOTED into a reusable, routable unit. "Not every trace becomes an engram." Tie the engram to the metabolism of knowledge in the Abstract.
[UNIVERSAL] ABSTRACT CARRIES THE FRAME — fold thesis / key-distinction / contribution into the Abstract (no separate callouts).
[UNIVERSAL] SIMPLE SENTENCES — no long comma-run lists; one idea per sentence (anti-pattern example given).
[UNIVERSAL] LISTS AS BULLETS — bold lead + an explaining clause.
[UNIVERSAL] PARAGRAPH FLOW — connect with transitions; no paragraph-dumps.
[UNIVERSAL] MECHANISM HONESTY — a structure / topology / store cannot "decide" or "enforce"; name the ACTOR + the OPERATION.
[UNIVERSAL] DEFINE KEY MECHANISMS WHERE INTRODUCED (+ a figure if it is an algorithm).
[UNIVERSAL] HIGH-LEVEL → SCIENTIFIC gradient; WORKED EXAMPLES ("Example N" + a monospace record).
[UNIVERSAL] SELF-CONTAINED OUTPUT — no external CDNs / fonts / scripts (no MathJax); all notation renders as plain text; the renderer's OUTPUT GATE strips any residual notation.
[UNIVERSAL] BEFORE FINALIZING — 12-item checklist (…)

LTM-figure-style.txt 135 lines · 8.5 KB — visual-first, LARGE text, geometry QC, artifact hygiene

Full file via GET /api/knowledge/LTM-figure-style. Excerpt:

[UNIVERSAL] VISUAL-FIRST — prefer diagrams / architectures / worked examples over symbolic notation.
[UNIVERSAL] PLACEMENT-ORDER NUMBERING — figures numbered by document position; no section ends on a figure; none back-to-back.
[UNIVERSAL] DEPICT THE MECHANISM — inputs → operation → outputs; label every non-obvious element (X / blocked / colour named in text).
[UNIVERSAL] AVOID SIMPLISTIC PANELS — include the gate / mask / signal types / decay branch.
[UNIVERSAL] LARGE, READABLE TEXT — title >=22px, box labels >=18px, edge labels >=16px, never <14px; set font-size on every text element; enlarge the viewBox (~820-1040 wide) rather than shrink text; >=10px box padding; >=1.6x row spacing. The renderer's geometry lint flags any text <14px.
[UNIVERSAL] SELF-CONTAINED SVG — inline only; no external images/fonts/scripts; explicit viewBox; restrained palette (green=admit, amber=decay, red=quarantine).
[UNIVERSAL] NO BARE ARTIFACTS — prose around every figure/example/table.
[UNIVERSAL] ARTIFACT / RENDERING HYGIENE — no stray tokens (e.g. "D4-T1D5-T1"); no "RReferences".
[UNIVERSAL] GEOMETRY + SAFETY QC GATE — HARD (out-of-bounds / unsafe) → fix or DROP; SOFT (text-collision, <14px font) → tolerate + auto-fix.
Diagram vocabulary: hub / pipeline / FSM / flow / layered / matrix / timeline / contrast.

LTM-citation-style.txt 112 lines · 6.1 KB — author-year, STM-only, [CITATION-KEYS] (44)

Full file via GET /api/knowledge/LTM-citation-style. Excerpt:

[UNIVERSAL] IN-TEXT STYLE — (Short name, year); multiple (A, 2025; B, 2026). NO numeric superscripts.
[UNIVERSAL] WHICH SOURCES ARE CITEABLE — cite verifiable EXTERNAL scholarship only (the STM-### tier). Do NOT cite the internal author corpus (S-## / P-slug). Where a foundational concept comes from the corpus, cite its published counterpart.
[UNIVERSAL] REFERENCE LIST — unnumbered, alphabetical by key; only entries still cited after trimming.
[UNIVERSAL] BUILDING CITATION KEYS — never fabricate authors; key = system/short name from title + year from the arXiv id (25xx→2025, 26xx→2026); merge duplicate versions.
[CITATION-KEYS]  (machine-readable; the renderer parses these)
  STM-013 = MemOS | 2025      STM-014 = Memory as Metabolism | 2026
  STM-012 = ProcMEM | 2026    STM-031 = MemArchitect | 2026
  STM-032 = Governance by Design | 2026   STM-026 = TRACE | 2026
  … 44 entries total …
[UNIVERSAL] BEFORE FINALIZING — checklist (no numeric sups; no S-/P- cites; unnumbered alphabetical; no fabricated authors).

LTM-author-voice.txt 80 lines · 8.4 KB — thesis + voice from the internal corpus (informs, not cited)

Full file via GET /api/knowledge/LTM-author-voice. Excerpt:

LTM-paper-profile.txt 79 lines · 5.3 KB — descriptive target template (measured from 10-PAPER-new.html)

Full file via GET /api/knowledge/LTM-paper-profile. Excerpt:

rubrics/RUB-paper.txt 37 lines · 2.0 KB — K1-K6 criteria + house-style gates (regenerated each run)

Seeded from build_research._seed_criteria() each run. Excerpt:

Rendered output preview — the HTML is the primary, high-fidelity source. DOCX/PDF are derived from it over self-improving conversion passes.

Deterministic invariants checked on every render, compared against the hand-tuned target 10-PAPER-new.html.

Invariant / metric	This run	Target
Symbolic notation ($ / greek / LaTeX)	—	0
MathJax script present	—	no
"RReferences" artifact	—	none
Numeric superscript citations	—	0
Internal-corpus refs (P-/S-)	—	0
Author-year citations	—	~19
Figures	—	19
Section headings (h2)	—	8
Word count	—	~5276
Well-formed HTML	—	yes

REST endpoints (thin wrapper)

Method + path	Purpose
GET /api/health	WARP + gateway reachability, ensemble attribution
GET /api/config	read search_config.json (ensemble, paper, gate)
PUT /api/config	update pool / temperatures / rounds / min+max figures / budget
POST /api/workspaces	create a NEW paper dir; replicate KnowledgeRepresentation/ + augment; engine stays shared
POST /api/uploads	upload one or more local source papers into a workspace
POST /api/runs	start build_research and/or render_paper over the input list
GET /api/runs/:id/events	SSE stream: stage, %, ETA, heartbeat, QC rounds, figure results
POST /api/convert	HTML → docx/pdf, self-improving over N passes
GET /api/runs/:id/output?fmt=	rendered html / docx / pdf
GET /api/runs/:id/audit	deterministic invariant report
GET /api/knowledge/:node	FULL knowledge node text (writing/figure/citation/author/profile + rubric)
PUT /api/knowledge/:node	edit a knowledge node (takes effect next run; replicated to new workspaces)

POST /api/runs — request body

{
  "workspace": "papers-vince/ActionEngram",
  "new_workspace": false,
  "replicate_knowledge": true,
  "augment_knowledge": "",
  "inputs": ["KnowledgeRepresentation/10-PAPER.txt"],
  "formats": ["html","docx"],
  "conversion_passes": 3,
  "pool":   ["poolside/Laguna-S-2.1"],
  "max_rounds": 3,        // ① draft REFINE (build_research)
  "qc_rounds": 5,         // ② body QC + ③ figure QC, per-application
  "min_figures": 12, "max_figures": 19,
  "qc_temperatures":   [0.2,0.5,0.8,0.4,0.7],
  "figure_temperatures":[0.3,0.7],
  "daily_usd_cap": 15,
  "no_qc": false, "no_figures": false, "dry_run": false
}

SSE event shape (drives the progress bar / ETA / stall)

event: stage    data: {"stage":"qc","pct":42,"eta_s":95}
event: heartbeat data: {"t":1699,"alive":true}
event: qc       data: {"round":2,"temp":0.5,"accept":true,"gain":3}
event: figure   data: {"n":7,"title":"Anatomy…","kept":true,"soft":1}
event: convert  data: {"fmt":"docx","pass":2,"of":3}
event: done     data: {"audit":{"dollar":0,"figs":19,...}}

The client watches heartbeat; if none arrives for >4s it flags a stall/crash. The wrapper shells out to build_research.py + render_paper.py (and a to_format.py converter) and tails stdout + 12-QC-LOG.txt; no pipeline logic is duplicated.