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L1/L2: cheap pre-filter for provably routine calls (gated on measurement)

Cerrado
  • ¿Algo no está bien?
#34 2 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
48/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
bash, typescript
Área
cli, security

Línea de trabajo

Start with the measurements from #17 and the current 92-case adversarial report to determine whether the stated gate is met. Then locate the deterministic recognizer and full harness, and verify the listed acceptance conditions: pure-function tests for verbs and disqualifiers, zero under-flag delta, and under-1ms added latency.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

enhancement ready-for-agent

This was generated by AI during triage.

Cheap pre-filter for provably routine calls

Layer: L1 (recognition) + L2 (judgment). Gated on measurement: do not build until #17 numbers justify it.

Cost structure today: every novel command costs one full model round-trip. The session cache removes repeats, but a heavy session still makes dozens of novel calls, and the harness shows the model spends most of them agreeing.

The peer project's architecture: stage one is a 16-token filter that can only say "clearly routine" or "look closer", and is never allowed to refuse; stage two is the full review. Splitting them keeps the common case cheap without letting the cheap case decide anything consequential.

Our version has a better stage zero available: the deterministic layer already knows things the model cannot. A structurally routine recognizer (single-segment command, verb on a read-only verb list, no redirects, no substitutions, no writes, no network tokens) can clear the long tail with zero model cost and zero risk of a wrong allow, because the shape is provably inert. Only the remainder reaches the model. A model-side stage-1 filter is the fallback for shapes the recognizer cannot prove.

Ordering to preserve: critical patterns and the eval spawn scan outrank any fast path. The recognizer can only clear, never refuse. Fail-open there is safe because everything it clears is provably inert by construction, and everything else classifies exactly as today.

Measured gate for building it: from the current adversarial report, compute what fraction of the 92 cases the recognizer would clear and how many of those are labeled allow. Build only if it clears a meaningful share (target: >30% of corpus volume) with zero allow-labeled misses in a full harness run.

Acceptance:

  • Recognizer is a pure function with unit tests over the verb list and every disqualifier.
  • A full harness run with the fast path enabled shows zero under-flag delta.
  • Latency: fast-path decisions add no measurable per-call overhead (<1ms).
Lenguaje dominante
TypeScript
Estrellas
0
Forks
1
Merge medio
3 h 35 min
PR fusionados (30 d)
50

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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