Diagnosis (audited gate5 full-mode transcripts, PNGs, and holdout payloads):
the dominant failure mode was NOT rubro/content coverage but a tool-call
formatting bug -- after diagnosing an exception, the model often writes its
retry as literal <tool_call> XML text embedded in reasoning instead of a
structured call, so it's silently dropped (9/10 gate5 full-mode prompts hit
this on their final captured turn). Reasoning length before a tool call
correlates with the failure (malformed-turn mean 1895 chars vs 364 for
well-formed turns), while the training mix's own tool-calling turns never
exceed 1630 chars and export_shape turns never exceed 485.
Corrective seeds (group D, grupo 'D', short 1-3 sentence diagnosis +
immediate well-formed retry, to avoid reinforcing long-reasoning risk):
- 9 seeds across fresh rubros (lavanderia, zapateria, optica, jugueteria,
papeleria, peluqueria, floreria, heladeria, cerrajeria) covering distinct
real API errors: .color on Text, addFlexLayout on non-Board, findShapeById
2-arg, createText() no-arg, textAlign, typography.setFont, createBoolean
null, appendChild no-arg, uploadMediaUrl network rejection.
- 4 seeds reinforcing uploadMediaUrl over the exact penpot.importImage
variant the model actually invents (distinct from the one existing seed,
which only taught the penpotUtils.importImage variant) -- rubros:
relojeria, guarderia infantil, agencia de viajes, kiosco.
Deliberately avoid ferreteria/gimnasio/veterinaria/panaderia: reserved for
the held-out generalization probe (separate from the frozen gate 5).
Added the newly-verified error strings (t.color, penpot.importImage,
addFlexLayout-on-non-Board) to penpot_errors.md -- all captured live from
this phase's own gate 5 full-mode run, not fabricated.
Rebuilt train_lora2.jsonl (901)/eval_lora2.jsonl (99)/calibration_v2.jsonl
from the 138-seed corpus (07_build_lora2_mix.py) and validated
(06_validate_dataset.py, 0 exceptions, 0 secrets, max 3271 tokens).
The 41 existing Penpot seeds contain hand-fabricated penpot_api_info and
high_level_overview tool results that assert facts the server never said,
which is how the model learned an API that does not exist. This adds four
schema files that make the seed corpus mechanically verifiable against the
real server instead.
- penpot_api_docs.md: 34 verbatim captures of high_level_overview and
penpot_api_info, each headed by the exact request that produced it. Every
penpot_api_info tool result in a seed must be a subset of lines of this
file, in original order. Records three places where the served docs
contradict the runtime (addFlexLayout/addGridLayout copy-paste in the Grid
section, flex.appendChild for grid children, withChildren vs
includeChildren), plus the createText() example that is the direct cause
of the production failure.
- penpot_system_prompt.md: the server's instructions block verbatim. Goes as
a system message into ~30% of the new seeds; it is the countermeasure to
the "don't pick your own colours" rule that produces the grey boxes.
- penpot_errors.md: the real error strings, including a section on silent
failures that raise nothing at all and are why the read-back invariant
exists.
- PENPOT_API_VERIFIED.md: the allow-list. No seed may reference a member
absent from it. Documents the four root causes (findShapeById arity 1,
no shape.layout, createText() returning null, the #B1B2B5 default fill),
the twelve anti-grey-box invariants, and the forbidden-pattern list the
linter checks.
Live re-verification of the error strings is still pending: the Penpot
plugin is not currently connected, so it is deferred to the gate 5 baseline
step, which needs the live connection anyway.
data/schemas/*.json: dump fiel de las tool definitions reales de
Penpot (4), Gitea (53), GitHub-personal (43), Docmost (17) y
Atlassian (37), obtenidas directo de las definiciones ya cargadas
en la sesion de Claude Code (no se escribio un cliente MCP nuevo,
para no arriesgar desviarse del esquema real).
PENPOT_DEPLOYMENT_NOTES.md: investigacion del codigo fuente oficial
de @penpot/mcp confirma que import_image y export_shape.filePath
estan ausentes porque este deployment corre en modo remoto/multi-
usuario (isFileSystemAccessEnabled() = !isRemoteMode()) -- documentado
tambien en una subpagina nueva de Docmost.
scripts/03_build_replay.py: genera ~700 ejemplos de replay (anti-
forgetting) contra el vLLM de produccion, 80 prompts semilla
(conversacion general/codigo/razonamiento) x 9 pasadas variando
temperatura. Mapea el campo "reasoning" de la API de vLLM a
"reasoning_content" para la convencion de chat template.
Scaffolding inicial del repo: carpetas scripts/, data/schemas/,
data/raw/, out/, .gitignore para binarios/checkpoints, y el
docker-compose.yml del contenedor de training (imagen NGC pytorch
25.12-py3, GPU reservada, bind mounts a ai-projects vía NFS y a
~/ft-models en disco local rápido de spark) sin tocar jupyter-pyt
ni el vllm de producción.
aleleba