# config/default.yaml
# 全量默认参数，所有非敏感配置的唯一默认值来源。
# 优先级: CLI args > .env > 此文件。敏感信息在 .env 中管理。

# ── 建树模块 ──
tree:
  max_paragraphs_per_l2: 5
  l1_segment_duration: 600.0       # L1 段时长（秒）
  l2_clip_duration: 60.0           # L2 clip 时长（秒）
  l3_fps: 0.5                      # L3 帧提取频率（帧/秒）
  l2_representative_frames: 6      # L2 VLM 描述用的代表帧数
  cache_dir: "cache/trees"
  concurrency: 16                  # asyncio Semaphore 上限
  subtitle_inject: true            # 建树时是否注入 SRT 字幕
  srt_window_sec: 5.0              # 字幕匹配时间窗口（前后各 N 秒）

# ── Embedding ──
embed:
  backend: "local"
  model_name: "BAAI/bge-base-zh-v1.5"
  embed_dim: 768
  device: "cpu"

# ── 可训练检索器 ──
retriever:
  embed_dim: 768
  num_heads: 4
  L_layers: 2
  L_cycles: 4
  max_rounds: 5
  ffn_expansion: 2.0
  checkpoint: null
  k_l1: 1
  k_l2: 1
  k_l3: 1
  max_paths: 5

# ── 检索器训练 ──
train:
  lr: 1.0e-4
  weight_decay: 1.0e-5
  batch_size: 1
  max_epochs_phase1: 30
  max_epochs_phase2: 20
  nav_loss_weight: 1.0
  act_loss_weight: 0.1
  margin_loss_weight: 0.5
  act_lambda_step: 0.1
  act_gamma: 0.9
  eval_interval: 5
  save_dir: "checkpoints"
  dataset: "videomme"
  dataset_path: "data/videomme/splits/train.jsonl"

# ── Harness 自进化循环 ──
harness:
  workspace_dir: "workspaces/default"
  store_dir: store
  mode: infer
  concurrency: 12
  max_steps: 15
  skill_mode: auto
  n_samples: 0
  questions: "benchmarks/Video-MME"
  skills_version: v1
  prompts_version: v1
  epochs: 1
  # CE-Gate 参数
  gate_e_confirm: 20.0
  gate_e_provisional: 3.0
  gate_w_net_min: 2
  gate_delta_min: 0.02
  gate_lambda_dir: -0.642
  gate_e_rollback: 10.0
  gate_block: 8
  gate_n_max: 40
  gate_p_low: 0.05
  gate_p_high: 0.95
  gate_probe_quota: 0.2
  gate_gamma_decay: 0.9
  gate_cooldown_steps: 2
  gate_guard_err: 0.10
  # 进化参数
  edit_budget_start: 5
  edit_budget_end: 2
  skill_update_mode: patch
  appendix_consolidate_threshold: 6
  # 数据池
  diag_size: 200
  diag_correct_ratio: 0.5
  val_size: 30
  val_correct_ratio: 0.5
  test_size: 60
  # mini-batch
  batch_size: 15
  min_class_per_batch: 2
  batch_correct_ratio: 0.5
  momentum_samples: 20
  eval_min_per_class: 2
  early_stop_patience: 8
  use_slow_momentum: true