# Configuração pré-registrada do benchmark.
# Decisões e justificativas: docs/04-decisoes-de-projeto.md
# Este arquivo é congelado com hash no marco da tarefa 62.

contract:
  version: jev-compat-v1                     # Q1
  endpoint: /v1/systemone
  max_options: 16                            # Q2: inclui sem_correspondencia; limite do SemIf (A–P)
  fallback_option: sem_correspondencia
  require_zero_output_tokens: true

candidates:
  laya:
    model: convaiinnovations/laya-multilingual
    revision: null                           # preenchido pela aquisição (tarefa 13)
    declared_context: 1024
  semif:
    model: Qwen/Qwen3.5-4B
    runtime: SemIf-OpenJev
    mode: direct
    revision: null
    declared_context: 8192
  gliner:
    model: fastino/GLiNER2.5-multi-Decide
    interface: classify_text
    revision: null
    declared_context: null                   # medido (Q4)
  rizzo_flow:
    model: rizzoaiacademy/rizzo-flow
    prompt_version: spark-decisions-v3
    revision: null
    declared_context: 2048

baseline:                                    # Q9: fora do ranking principal
  enabled: true
  model: null                                # fixado no manifesto
  network: gateway_only

licenses:                                    # Q4
  allowlist: [apache-2.0, mit]

network:                                     # Q3
  egress_probe_hosts: [huggingface.co, pypi.org]
  require_probe_failure: true

input:                                       # Q8
  state_turns_candidates: [0, 1, 2, 4]
  fit_ratio_min: 0.95
  token_counter: most_verbose_tokenizer
  truncation: drop_oldest_turns_then_tail_truncate_utterance

execution:
  seed: 20261007
  production_batch: 1                        # Q6: contrato por requisição, decisão turno a turno
  repetitions: 3
  cpu:                                       # Q11
    timeout_ms: 10000
    max_timeout_rate: 0.05
  gpu:
    timeout_ms: 2000
  unavailable_retries: 3

calibration:                                 # Q10
  method: temperature_scaling
  per: [candidate, task]
  cv_folds: 5
  accept_if: ece_decreases

criteria:                                    # Q5
  latency_gpu_batch1:
    p95_ms_per_decision: 150
  accuracy:
    tasks: [D1, D2]
    non_inferiority_margin_pp: 3.0
    ci: 0.95
    bootstrap_unit: group_id
    fallback_macro_f1_min: 0.80
  risk:
    max_confident_wrong_route_rate: 0.02
    min_coverage: 0.80

sample_size:                                 # Q12
  task: D1
  mde_pp: 3.0
  alpha: 0.05
  power: 0.80
  expected_discordance: 0.15
  prior_icc: 0.05
  min_test_decisions: 1300

data:                                        # Q7
  sources: [real, synthetic]
  human_audit_rate: 0.05
  dedup: minhash_lsh

budget:                                      # Q13: em aberto (cota interna); não provisionado
  enabled: false
  contingency_factor: 1.3
  alerts_pct: [50, 80, 100]