自动寻参：

- 把 batch timeout 改成“可无限长跑”： - [tune_fusion.py](/data/saas-search/scripts/evaluation/tune_fusion.py:400) - 现在 `--batch-eval-timeout-sec <= 0` 时，不再给 `subprocess.run` 设置 Python 层超时 - 新增 resilient wrapper，负责自动续跑： - [run_coarse_fusion_tuning_resilient.sh](/data/saas-search/scripts/evaluation/run_coarse_fusion_tuning_resilient.sh) - 逻辑是：检查 `trials.jsonl` 里已完成的 live eval 数量，没到 `max_evals` 就继续 `resume-run` - 即使异常退出，也会 sleep 后自动从已有 `run_dir` 继续 - 启动/续跑脚本都切到 resilient 模式： - [start_coarse_fusion_tuning_long.sh](/data/saas-search/scripts/evaluation/start_coarse_fusion_tuning_long.sh) - [resume_coarse_fusion_tuning_long.sh](/data/saas-search/scripts/evaluation/resume_coarse_fusion_tuning_long.sh) **当前任务** - `run_name`: `coarse_fusion_clothing_top771_resilient_20260422T091650Z` - `run_dir`: [coarse_fusion_clothing_top771_resilient_20260422T091650Z](/data/saas-search/artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z) - `launch log`: [coarse_fusion_clothing_top771_resilient_20260422T091650Z.log](/data/saas-search/artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_20260422T091650Z.log) **已确认** - wrapper 已启动并进入 `attempt=1` - 真正传入的是 `--batch-eval-timeout-sec 0` - `tune_fusion.py` 正在运行 - `build_annotation_set.py batch` 已经在运行 - `eval.log` 已经打出这轮的前几条 query 评测进度，说明不是空转 **监控方式** - `tail -f artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_20260422T091650Z.log` - `tail -f logs/eval.log` - `tail -f artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z/trials.jsonl` - `cat artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z/leaderboard.csv` **这次和上次的关键区别** - 上次是“单轮 batch 被 Python 超时截断” - 这次是“单轮不设 Python 超时 + 外层 wrapper 自动续跑” - 所以长时间运行、中途中断、再恢复，都会沿着同一个 `run_dir` 往下推进

自动寻参：
- 把 batch timeout 改成“可无限长跑”： - [tune_fusion.py](/data/saas-search/scripts/evaluation/tune_fusion.py:400) - 现在 `--batch-eval-timeout-sec <= 0` 时，不再给 `subprocess.run` 设置 Python 层超时 - 新增 resilient wrapper，负责自动续跑： - [run_coarse_fusion_tuning_resilient.sh](/data/saas-search/scripts/evaluation/run_coarse_fusion_tuning_resilient.sh) - 逻辑是：检查 `trials.jsonl` 里已完成的 live eval 数量，没到 `max_evals` 就继续 `resume-run` - 即使异常退出，也会 sleep 后自动从已有 `run_dir` 继续 - 启动/续跑脚本都切到 resilient 模式： - [start_coarse_fusion_tuning_long.sh](/data/saas-search/scripts/evaluation/start_coarse_fusion_tuning_long.sh) - [resume_coarse_fusion_tuning_long.sh](/data/saas-search/scripts/evaluation/resume_coarse_fusion_tuning_long.sh) **当前任务** - `run_name`: `coarse_fusion_clothing_top771_resilient_20260422T091650Z` - `run_dir`: [coarse_fusion_clothing_top771_resilient_20260422T091650Z](/data/saas-search/artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z) - `launch log`: [coarse_fusion_clothing_top771_resilient_20260422T091650Z.log](/data/saas-search/artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_20260422T091650Z.log) **已确认** - wrapper 已启动并进入 `attempt=1` - 真正传入的是 `--batch-eval-timeout-sec 0` - `tune_fusion.py` 正在运行 - `build_annotation_set.py batch` 已经在运行 - `eval.log` 已经打出这轮的前几条 query 评测进度，说明不是空转 **监控方式** - `tail -f artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_20260422T091650Z.log` - `tail -f logs/eval.log` - `tail -f artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z/trials.jsonl` - `cat artifacts/search_evaluation/tuning_runs/coarse_fusion_clothing_top771_resilient_20260422T091650Z/leaderboard.csv` **这次和上次的关键区别** - 上次是“单轮 batch 被 Python 超时截断” - 这次是“单轮不设 Python 超时 + 外层 wrapper 自动续跑” - 所以长时间运行、中途中断、再恢复，都会沿着同一个 `run_dir` 往下推进
tangwang
1 parent 628ff04f
Showing 19 changed files with 473 additions and 184 deletions Show diff stats
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_20260422T023815Z.cmd
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_20260422T023815Z.pid
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_20260422T023951Z.cmd
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_20260422T023951Z.pid
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_dryrun.cmd
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_dryrun.pid
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_dryrun2.cmd
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_dryrun2.pid
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_dryrun.cmd
artifacts/search_evaluation/tuning_launches/coarse_fusion_clothing_top771_resilient_dryrun.pid
config/config-with-reranker.yaml
config/config.yaml
docs/issues/issue-2026-04-16-bayes寻参-clothing_top771数据集上寻参.md
docs/issues/issue-2026-04-16-数据集扩增&bayes寻参-TODO.md
scripts/evaluation/resume_coarse_fusion_tuning_long.sh
scripts/evaluation/run_coarse_fusion_tuning_resilient.sh
scripts/evaluation/start_coarse_fusion_tuning_long.sh
scripts/evaluation/tune_fusion.py
scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml
@@ -0,0 +1 @@
+python scripts/evaluation/tune_fusion.py --mode optimize --run-name coarse_fusion_clothing_top771_20260422T023815Z --search-space scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml --seed-report artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md --tenant-id 163 --dataset-id clothing_top771 --queries-file scripts/evaluation/queries/queries.txt --top-k 100 --language en --search-base-url http://127.0.0.1:6002 --eval-web-base-url http://127.0.0.1:6010 --max-evals 18 --batch-size 2 --candidate-pool-size 160 --random-seed 20260422 
@@ -0,0 +1 @@
+3843738
@@ -0,0 +1 @@
+python scripts/evaluation/tune_fusion.py --mode optimize --run-name coarse_fusion_clothing_top771_20260422T023951Z --search-space scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml --seed-report artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md --tenant-id 163 --dataset-id clothing_top771 --queries-file scripts/evaluation/queries/queries.txt --top-k 100 --language en --search-base-url http://127.0.0.1:6002 --eval-web-base-url http://127.0.0.1:6010 --max-evals 18 --batch-size 2 --candidate-pool-size 160 --random-seed 20260422 
@@ -0,0 +1 @@
+3845416
@@ -0,0 +1 @@
+python scripts/evaluation/tune_fusion.py --mode optimize --run-name coarse_fusion_clothing_top771_dryrun --search-space scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml --seed-report artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T021002Z_5426bba1a6/report.md --tenant-id 163 --dataset-id clothing_top771 --queries-file scripts/evaluation/queries/queries.txt --top-k 100 --language en --search-base-url http://127.0.0.1:6002 --eval-web-base-url http://127.0.0.1:6010 --max-evals 18 --batch-size 2 --candidate-pool-size 160 --random-seed 20260422 --help 
@@ -0,0 +1 @@
+3842050
@@ -0,0 +1 @@
+python scripts/evaluation/tune_fusion.py --mode optimize --run-name coarse_fusion_clothing_top771_dryrun2 --search-space scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml --seed-report artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md --tenant-id 163 --dataset-id clothing_top771 --queries-file scripts/evaluation/queries/queries.txt --top-k 100 --language en --search-base-url http://127.0.0.1:6002 --eval-web-base-url http://127.0.0.1:6010 --max-evals 18 --batch-size 2 --candidate-pool-size 160 --random-seed 20260422 --help 
@@ -0,0 +1 @@
+3843512
@@ -0,0 +1 @@
+bash scripts/evaluation/run_coarse_fusion_tuning_resilient.sh coarse_fusion_clothing_top771_resilient_dryrun clothing_top771 18 2 160 20260422 scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md --help 
@@ -0,0 +1 @@
+4126011
@@ -260,6 +260,7 @@ function_score:
   score_mode: sum
   boost_mode: multiply
   functions: []
+
 coarse_rank:
   enabled: true
   input_window: 480
@@ -271,7 +272,7 @@ coarse_rank:
     text_exponent: 0.35
     # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
     # 因为es的打分已经给了trans进行了折扣，所以这里不再继续折扣
-    text_translation_weight: 0.8
+    text_translation_weight: 1.0
     knn_text_weight: 1.0
     knn_image_weight: 2.0
     knn_tie_breaker: 0.3
@@ -100,11 +100,8 @@ es_settings:
   number_of_shards: 1
   number_of_replicas: 0
   refresh_interval: 30s
-
-# 统一按“字段基名”配置；查询时按实际检索语言动态拼接 .{lang}
 field_boosts:
   title: 3.0
-  # qanchors enriched_tags 在 enriched_attributes.value中也存在，所以其实他的权重为自身权重+enriched_attributes.value的权重
   qanchors: 1.0
   enriched_tags: 1.0
   enriched_attributes.value: 1.5
@@ -118,7 +115,6 @@ field_boosts:
   brief: 1.0
   description: 1.0
   vendor: 1.0
-
 query_config:
   supported_languages:
   - zh
@@ -126,16 +122,12 @@ query_config:
   default_language: en
   enable_text_embedding: true
   enable_query_rewrite: true
-
-  zh_to_en_model: nllb-200-distilled-600m # nllb-200-distilled-600m deepl opus-mt-zh-en / opus-mt-en-zh
+  zh_to_en_model: nllb-200-distilled-600m
   en_to_zh_model: nllb-200-distilled-600m
   default_translation_model: nllb-200-distilled-600m
-  # 源语种不在 index_languages时翻译质量比较重要，因此单独配置
   zh_to_en_model__source_not_in_index: deepl
   en_to_zh_model__source_not_in_index: deepl
   default_translation_model__source_not_in_index: deepl
-
-  # 查询解析阶段：翻译与 query 向量并发执行，共用同一等待预算（毫秒）
   translation_embedding_wait_budget_ms_source_in_index: 300
   translation_embedding_wait_budget_ms_source_not_in_index: 400
   style_intent:
@@ -165,31 +157,22 @@ query_config:
     enabled: true
     dictionary_path: config/dictionaries/product_title_exclusion.tsv
   search_fields:
-    # 统一按“字段基名”配置；查询时按实际检索语言动态拼接 .{lang}
     multilingual_fields:
     - title
     - keywords
     - qanchors
     - enriched_tags
     - enriched_attributes.value
-    # - enriched_taxonomy_attributes.value
     - option1_values
     - option2_values
     - option3_values
     - category_path
     - category_name_text
-    # - brief
-    # - description
-    # - vendor
-    # shared_fields: 无语言后缀字段；示例: tags, option1_values, option2_values, option3_values
-
     shared_fields: null
     core_multilingual_fields:
     - title
     - qanchors
     - category_name_text
-
-  # 文本召回（主查询 + 翻译查询）
   text_query_strategy:
     base_minimum_should_match: 60%
     translation_minimum_should_match: 60%
@@ -206,8 +189,6 @@ query_config:
     phrase_match_boost: 3.0
   text_embedding_field: title_embedding
   image_embedding_field: image_embedding.vector
-
-  # null表示返回所有字段，[]表示不返回任何字段
   source_fields:
   - spu_id
   - handle
@@ -223,13 +204,8 @@ query_config:
   - category1_name
   - category2_name
   - category3_name
-  # - tags
-  # - keywords
-  # - qanchors
-  # - enriched_tags
   - enriched_attributes
   - enriched_taxonomy_attributes
-
   - min_price
   - compare_at_price
   - image_url
@@ -245,17 +221,14 @@ query_config:
   - option3_values
   - specifications
   - skus
-
-  # KNN：文本向量与多模态（图片）向量各自 boost 与召回（k / num_candidates）
   knn_text_boost: 4
   knn_image_boost: 4
   knn_text_k: 160
-  knn_text_num_candidates: 560 #  k * 3.4
+  knn_text_num_candidates: 560
   knn_text_k_long: 400
   knn_text_num_candidates_long: 1200
   knn_image_k: 400
   knn_image_num_candidates: 1200
-
 function_score:
   score_mode: sum
   boost_mode: multiply
@@ -269,20 +242,18 @@ coarse_rank:
     es_exponent: 0.05
     text_bias: 0.1
     text_exponent: 0.35
-    # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
-    # 因为es的打分已经给了trans进行了折扣，所以这里不再继续折扣
     text_translation_weight: 1.0
     knn_text_weight: 1.0
     knn_image_weight: 2.0
     knn_tie_breaker: 0.3
-    knn_bias: 0.2
-    knn_exponent: 5.6
+    knn_bias: 0.6
+    knn_exponent: 0.4
     knn_text_bias: 0.2
     knn_text_exponent: 0.0
     knn_image_bias: 0.2
     knn_image_exponent: 0.0
 fine_rank:
-  enabled: false # false 时保序透传
+  enabled: false
   input_window: 160
   output_window: 80
   timeout_sec: 10.0
@@ -290,7 +261,7 @@ fine_rank:
   rerank_doc_template: '{title}'
   service_profile: fine
 rerank:
-  enabled: false # false 时保序透传
+  enabled: false
   rerank_window: 160
   exact_knn_rescore_enabled: true
   exact_knn_rescore_window: 160
@@ -300,10 +271,6 @@ rerank:
   rerank_query_template: '{query}'
   rerank_doc_template: '{title}'
   service_profile: default
-  # 乘法融合：fused = Π (max(score,0) + bias) ** exponent（es / rerank / fine / text / knn）
-  # 其中 knn_score 先做一层 dis_max：
-  #   max(knn_text_weight * text_knn, knn_image_weight * image_knn)
-  #   + knn_tie_breaker * 另一侧较弱信号
   fusion:
     es_bias: 10.0
     es_exponent: 0.05
@@ -312,7 +279,6 @@ rerank:
     fine_bias: 0.1
     fine_exponent: 1.0
     text_bias: 0.1
-    # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
     text_exponent: 0.25
     text_translation_weight: 0.8
     knn_text_weight: 1.0
@@ -320,7 +286,6 @@ rerank:
     knn_tie_breaker: 0.3
     knn_bias: 0.0
     knn_exponent: 5.6
-
 services:
   translation:
     service_url: http://127.0.0.1:6006
@@ -330,9 +295,6 @@ services:
     cache:
       ttl_seconds: 62208000
       sliding_expiration: true
-      # When false, cache keys are exact-match per request model only (ignores model_quality_tiers for lookups)
-      # Higher tier = better quality. Multiple models may share one tier (同级).
-      # A request may reuse Redis keys from models with tier > A or tier == A (not from lower tiers).
       enable_model_quality_tier_cache: true
       model_quality_tiers:
         deepl: 30
@@ -443,10 +405,7 @@ services:
         device: cuda
         batch_size: 32
         normalize_embeddings: true
-    # 服务内图片后端（embedding 进程启动时读取；cnclip gRPC 与 6008 须同一 model_name）
-    # Chinese-CLIP：ViT-H-14 → 1024 维，ViT-L-14 → 768 维。须与 mappings/search_products.json 中
-    # image_embedding.vector.dims 一致（当前索引为 1024 → 默认 ViT-H-14）。
-    image_backend: clip_as_service  # clip_as_service | local_cnclip
+    image_backend: clip_as_service
     image_backends:
       clip_as_service:
         server: grpc://127.0.0.1:51000
@@ -472,7 +431,6 @@ services:
     request:
       max_docs: 1000
       normalize: true
-    # 命名实例：同一套 reranker 代码按实例名读取不同端口 / 后端 / runtime 目录。
     default_instance: default
     instances:
       default:
@@ -515,31 +473,11 @@ services:
         enforce_eager: false
         infer_batch_size: 100
         sort_by_doc_length: true
-
-        # standard=_format_instruction__standard（固定 yes/no system）；compact=_format_instruction（instruction 作 system 且 user 内重复 Instruct）
-        instruction_format: standard  # compact standard
-        # instruction: "Given a query, score the product for relevance"
-        # "rank products by given query" 比 “Given a query, score the product for relevance” 更好点
-        # instruction: "rank products by given query, category match first"
-        # instruction: "Rank products by query relevance, prioritizing category match"
-        # instruction: "Rank products by query relevance, prioritizing category and style match"
-        # instruction: "Rank by query relevance, prioritize category & style"
-        # instruction: "Relevance ranking: category & style match first"
-        # instruction: "Score product relevance by query with category & style match prioritized"
-        # instruction: "Rank products by query with category & style match prioritized"
-        # instruction: "Given a fashion shopping query, retrieve relevant products that answer the query"
+        instruction_format: standard
         instruction: rank products by given query
-
-      # vLLM LLM.score()（跨编码打分）。独立高性能环境 .venv-reranker-score（vllm 0.18 固定版）：./scripts/setup_reranker_venv.sh qwen3_vllm_score
-      # 与 qwen3_vllm 可共用同一 model_name / HF 缓存；venv 分离以便升级 vLLM 而不影响 generate 后端。
       qwen3_vllm_score:
         model_name: Qwen/Qwen3-Reranker-0.6B
-        # 官方 Hub 原版需 true；若改用已转换的 seq-cls 权重（如 tomaarsen/...-seq-cls）则设为 false
         use_original_qwen3_hf_overrides: true
-        # vllm_runner: "auto"
-        # vllm_convert: "auto"
-        # 可选：在 use_original_qwen3_hf_overrides 为 true 时与内置 overrides 合并
-        # hf_overrides: {}
         engine: vllm
         max_model_len: 172
         tensor_parallel_size: 1
@@ -549,10 +487,7 @@ services:
         enforce_eager: false
         infer_batch_size: 80
         sort_by_doc_length: true
-        # 默认 standard 与 vLLM 官方 Qwen3 reranker 前缀一致
-        instruction_format: standard  # compact standard
-        # instruction: "Rank products by query with category & style match prioritized"
-        # instruction: "Given a shopping query, rank products by relevance"
+        instruction_format: standard
         instruction: Rank products by query with category & style match prioritized
       qwen3_transformers:
         model_name: Qwen/Qwen3-Reranker-0.6B
@@ -620,25 +555,19 @@ services:
         endpoint: https://dashscope.aliyuncs.com/compatible-api/v1/reranks
         api_key_env: RERANK_DASHSCOPE_API_KEY_CN
         timeout_sec: 10.0
-        top_n_cap: 0 # 0 表示 top_n=当前请求文档数
-        batchsize: 64 # 0 关闭；>0 启用并发小包调度（top_n/top_n_cap 仍生效，分包后全局截断）
+        top_n_cap: 0
+        batchsize: 64
         instruct: Given a shopping query, rank product titles by relevance
         max_retries: 2
         retry_backoff_sec: 0.2
-
 spu_config:
   enabled: true
   spu_field: spu_id
   inner_hits_size: 10
-  # 配置哪些option维度参与检索（进索引、以及在线搜索）
-  # 格式为list，选择option1/option2/option3中的一个或多个
   searchable_option_dimensions:
   - option1
   - option2
   - option3
-
-# 每个租户可配置主语言 primary_language 与索引语言 index_languages（主市场语言，商家可勾选）
-# 默认 index_languages: [en, zh]，可配置为任意 SOURCE_LANG_CODE_MAP.keys() 的子集
 tenant_config:
   default:
     primary_language: en
@@ -0,0 +1,89 @@
+Prompt - 1
+
+二、在大标注集上寻参
+
+我以前经过过一轮调参，是基于54个评测样本（queries.txt），过程中发现的最优的参数是这一组：
+0.641241 {'es_bias': '7.214', 'es_exponent': '0.2025', 'text_bias': '4.0', 'text_exponent': '1.584', 'text_translation_weight': '1.4441', 'knn_text_weight': '0.1', 'knn_image_weight': '5.6232', 'knn_tie_breaker':
+    '0.021', 'knn_bias': '0.0019', 'knn_exponent': '11.8477', 'knn_text_bias': '2.3125', 'knn_text_exponent': '1.1547', 'knn_image_bias': '0.9641', 'knn_image_exponent': '5.8671'}
+
+这一组参数分布比较极端，text_bias太大（文本项得分事0~1的，加上4被稀释的很大），图片的exponent太大，不过在这个数据集上面确实是最好的，我觉得有过拟合的可能，因此要扩大数据集，先扩展标注集，然后使用扩展的标注集，继续进行寻参。
+
+因此新建了一个标注集合，标注任务也已经完成：Clothing Filtered 771。请启动寻参任务，把任务跑起来，以后等程序跑完了应该能拿到寻参结果，下次你可以结合程序执行的结果进行结论分析。
+
+至于调参方式，请参考以前的一轮调参：
+我当时的调参需求：
+
+请对coarse_rank fusion公式进行调参：
+  目前的baseline是这一组，Primary_Metric_Score: 0.637642：
+  coarse_rank:
+  ...
+    fusion:
+      es_bias: 10.0
+      es_exponent: 0.05
+      text_bias: 0.1
+      text_exponent: 0.35
+      text_translation_weight: 1.0
+      knn_text_weight: 1.0
+      knn_image_weight: 2.0
+      knn_tie_breaker: 0.3
+      knn_bias: 0.2
+      knn_exponent: 5.6
+      knn_text_bias: 0.2
+      knn_text_exponent: 0.0
+      knn_image_bias: 0.2
+      knn_image_exponent: 0.0
+  评估指标在/data/saas-search/artifacts/search_evaluation/batch_reports/batch_20260415T150754Z_00b6a8aa3d.md
+  请以这个为基准，发散思维，进行宽一点的范围调参。因为每次重启、评测都需要几分钟，请写一个调参的框架，基于框架、设定好多组参数、写好脚本，每轮收集效果自动的调整参数分布（因为笛卡尔积、多种参数调参成本太高，因此考虑贝叶斯调参等方法通
+  过多轮迭代通过脚本自动收敛）
+  每次调整参数后需要重启backend （有时候重启backend后eval-web好像也挂了，如果有可以追查原因并修复）
+  ./restart.sh backend
+  注意：请你调试脚本、进行一轮分析，最终要沉淀一套调参脚本，我下次可以重新跑（还是针对这组参数调参），能自动迭代（调整参数分布）、收集每组参数的指标、调优到最优的参数组合。
+
+
+
+你当时给出的调参脚本（ “种子实验 + 随机探索 + 基于历史最优分布的自适应采样”反复迭代。搜索范围已经放宽，新增了多组偏离 baseline 很远的 seed，也保留了 baseline 附近抖动，适合长时间慢慢收敛。）：
+
+  长时间自动调参脚本已经配好，核心文件是：
+
+  - 调参主程序：scripts/evaluation/tune_fusion.py
+  - 宽范围搜索空间：scripts/evaluation/tuning/coarse_rank_fusion_space.yaml
+  - 后台长跑启动脚本：scripts/evaluation/start_coarse_fusion_tuning_long.sh
+  - 续跑脚本：scripts/evaluation/resume_coarse_fusion_tuning_long.sh
+  - 使用说明：scripts/evaluation/tuning/README.md
+
+  直接启动一轮：
+
+  ./scripts/evaluation/start_coarse_fusion_tuning_long.sh
+
+  如果想跑更久一点：
+
+  MAX_EVALS=48 BATCH_SIZE=3 CANDIDATE_POOL_SIZE=512 RUN_NAME=coarse_fusion_long_001 \
+  ./scripts/evaluation/start_coarse_fusion_tuning_long.sh
+
+  看日志：
+
+  tail -f artifacts/search_evaluation/tuning_launches/<run_name>.log
+
+  看结果目录：
+
+  ls artifacts/search_evaluation/tuning_runs/<run_name>/
+
+  续跑：
+
+  ./scripts/evaluation/resume_coarse_fusion_tuning_long.sh <run_name>
+
+  每轮会自动做这些事：
+
+  - 生成一批候选 coarse_rank.fusion 参数
+  - 写入配置并重启 backend
+  - 检查 eval-web，必要时自愈拉起
+  - 跑评测并收集 Primary_Metric_Score
+  - 更新 trials.jsonl、leaderboard.csv、summary.md
+  - 基于历史结果调整下一轮采样分布
+
+上面只是历史的寻参任务的参考。现在请你使用新的数据集来进行寻参。
+要注意的是这个数据集比较大，因此每一轮行参任务耗时会比较长，进行大范围的精细的搜索不太可取。考虑仔细分析上次寻参的结果，在已有成果上，进行精细搜索，如果已有成果还不太够，可以在小数据集上进行粗搜，然后再到大数据集上寻参。
+
+
+
+Response - 1
@@ -377,10 +377,9 @@ CLI / 启动脚本设计
  
 这一组参数分布比较极端，text_bias太大（文本项得分事0~1的，加上4被稀释的很大），图片的exponent太大，不过在这个数据集上面确实是最好的，我觉得有过拟合的可能，因此要扩大数据集，先扩展标注集，然后使用扩展的标注集，继续进行寻参。
  
-我已经新建了一个标注集合，请启动寻参任务，把任务跑起来，以后等程序跑完了应该能拿到寻参结果，下次你可以结合程序执行的结果进行结论分析。
+因此新建了一个标注集合，标注任务也已经完成：Clothing Filtered 771。请启动寻参任务，把任务跑起来，以后等程序跑完了应该能拿到寻参结果，下次你可以结合程序执行的结果进行结论分析。
  
-
-以前的一轮调参：
+至于调参方式，请参考以前的一轮调参：
 我当时的调参需求：
  
 请对coarse_rank fusion公式进行调参：
@@ -411,7 +410,7 @@ CLI / 启动脚本设计
  
  
  
-你给出的调参脚本：
+你当时给出的调参脚本（ “种子实验 + 随机探索 + 基于历史最优分布的自适应采样”反复迭代。搜索范围已经放宽，新增了多组偏离 baseline 很远的 seed，也保留了 baseline 附近抖动，适合长时间慢慢收敛。）：
  
   长时间自动调参脚本已经配好，核心文件是：
  
@@ -421,8 +420,6 @@ CLI / 启动脚本设计
   - 续跑脚本：scripts/evaluation/resume_coarse_fusion_tuning_long.sh
   - 使用说明：scripts/evaluation/tuning/README.md
  
-  这套方案不是笛卡尔积穷举，而是“种子实验 + 随机探索 + 基于历史最优分布的自适应采样”反复迭代。搜索范围已经放宽，新增了多组偏离 baseline 很远的 seed，也保留了 baseline 附近抖动，适合长时间慢慢收敛。
-
   直接启动一轮：
  
   ./scripts/evaluation/start_coarse_fusion_tuning_long.sh
@@ -453,56 +450,5 @@ CLI / 启动脚本设计
   - 更新 trials.jsonl、leaderboard.csv、summary.md
   - 基于历史结果调整下一轮采样分布
  
-  你先跑一轮长任务。跑完后把 run_name 或 scripts/evaluation/tuning/README.md 里对应实验目录发我，我再基于结果做下一步分析和收缩搜索空间。
-
-
-
-
-› 我运行了：MAX_EVALS=400 BATCH_SIZE=3 CANDIDATE_POOL_SIZE=512 RUN_NAME=coarse_fusion_long_001 ./scripts/evaluation/start_coarse_fusion_tuning_long.sh
-
-  run_name=coarse_fusion_long_001
-  pid=2218620
-  log=artifacts/search_evaluation/tuning_launches/coarse_fusion_long_001.log
-  pid_file=artifacts/search_evaluation/tuning_launches/coarse_fusion_long_001.pid
-  cmd_file=artifacts/search_evaluation/tuning_launches/coarse_fusion_long_001.cmd
-  run_dir=artifacts/search_evaluation/tuning_runs/coarse_fusion_long_001
-
-  tail -f artifacts/search_evaluation/tuning_launches/coarse_fusion_long_001.log
-  cat artifacts/search_evaluation/tuning_runs/coarse_fusion_long_001/leaderboard.csv
-
-
-• 我先看这次长跑的日志和当前排行榜，确认任务是否正常推进、是否有异常中断，以及目前最优参数落在哪个方向。
-
-• Explored
-  └ Read coarse_fusion_long_001.log, leaderboard.csv
-    List coarse_fusion_long_001
-    Read summary.md
-
-───
-
-• 结果已经足够看出明显趋势了。我再补一层聚合分析，看看 top 区间里哪些参数方向反复出现，避免只盯着单个最优点。
-
-• Ran ps -p 2218620 -o pid=,etimes=,cmd=
-  └ (no output)
-
-• Ran python - <<'PY'
-  │ import csv, statistics, math
-  │ from pathlib import Path
-  │ … +15 lines
-  └ rows 385
-
-    … +61 lines (ctrl + t to view transcript)
-    knn_image_bias: mean=0.5631 median=0.2000 min=0.0010 max=4.0000
-    knn_image_exponent: mean=4.6316 median=5.8680 min=0.0000 max=6.0000
-
-• Ran python - <<'PY'
-  │ import csv
-  │ from pathlib import Path
-  │ … +19 lines
-  └ baseline_score 0.637642
-
-    … +30 lines (ctrl + t to view transcript)
-    10 bo_234 0.641241 {'es_bias': '7.214', 'es_exponent': '0.2025', 'text_bias': '4.0', 'text_exponent': '1.584', 'text_translation_weight': '1.4441', 'knn_text_weight': '0.1', 'knn_image_weight': '5.6232', 'knn_tie_breaker':
-    '0.021', 'knn_bias': '0.0019', 'knn_exponent': '11.8477', 'knn_text_bias': '2.3125', 'knn_text_exponent': '1.1547', 'knn_image_bias': '0.9641', 'knn_image_exponent': '5.8671'}
-
-这一次因为外部原因（磁盘满）终止了，以上是最好的一组参数。
+上面只是历史的寻参任务的参考。现在请你使用新的数据集来进行寻参。
+要注意的是这个数据集比较大，因此每一轮行参任务耗时会比较长，进行大范围的精细的搜索不太可取。考虑仔细分析上次寻参的结果，在已有成果上，进行精细搜索，如果已有成果还不太够，可以在小数据集上进行粗搜，然后再到大数据集上寻参。
 \ No newline at end of file
@@ -26,10 +26,28 @@ if [ ! -d &quot;${RUN_DIR}&quot; ]; then
   exit 1
 fi
  
-MAX_EVALS="${MAX_EVALS:-36}"
-BATCH_SIZE="${BATCH_SIZE:-3}"
-CANDIDATE_POOL_SIZE="${CANDIDATE_POOL_SIZE:-512}"
 DATASET_ID="${REPO_EVAL_DATASET_ID:-core_queries}"
+case "${DATASET_ID}" in
+  clothing_top771)
+    DEFAULT_SEED_REPORT="artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md"
+    DEFAULT_MAX_EVALS="18"
+    DEFAULT_BATCH_SIZE="2"
+    DEFAULT_CANDIDATE_POOL_SIZE="160"
+    ;;
+  *)
+    DEFAULT_SEED_REPORT="artifacts/search_evaluation/batch_reports/batch_20260415T150754Z_00b6a8aa3d.md"
+    DEFAULT_MAX_EVALS="36"
+    DEFAULT_BATCH_SIZE="3"
+    DEFAULT_CANDIDATE_POOL_SIZE="512"
+    ;;
+esac
+
+SEED_REPORT="${SEED_REPORT:-${DEFAULT_SEED_REPORT}}"
+MAX_EVALS="${MAX_EVALS:-${DEFAULT_MAX_EVALS}}"
+BATCH_SIZE="${BATCH_SIZE:-${DEFAULT_BATCH_SIZE}}"
+CANDIDATE_POOL_SIZE="${CANDIDATE_POOL_SIZE:-${DEFAULT_CANDIDATE_POOL_SIZE}}"
+BATCH_EVAL_TIMEOUT_SEC="${BATCH_EVAL_TIMEOUT_SEC:-0}"
+RANDOM_SEED="${RANDOM_SEED:-20260422}"
  
 LAUNCH_DIR="artifacts/search_evaluation/tuning_launches"
 mkdir -p "${LAUNCH_DIR}"
@@ -38,28 +56,25 @@ PID_PATH=&quot;${LAUNCH_DIR}/${RUN_NAME}.resume.pid&quot;
 CMD_PATH="${LAUNCH_DIR}/${RUN_NAME}.resume.cmd"
  
 CMD=(
-  python
-  scripts/evaluation/tune_fusion.py
-  --mode optimize
-  --resume-run "${RUN_DIR}"
-  --search-space "${RUN_DIR}/search_space.yaml"
-  --seed-report artifacts/search_evaluation/batch_reports/batch_20260415T150754Z_00b6a8aa3d.md
-  --tenant-id 163
-  --dataset-id "${DATASET_ID}"
-  --queries-file scripts/evaluation/queries/queries.txt
-  --top-k 100
-  --language en
-  --search-base-url http://127.0.0.1:6002
-  --eval-web-base-url http://127.0.0.1:6010
-  --max-evals "${MAX_EVALS}"
-  --batch-size "${BATCH_SIZE}"
-  --candidate-pool-size "${CANDIDATE_POOL_SIZE}"
+  bash
+  scripts/evaluation/run_coarse_fusion_tuning_resilient.sh
+  "${RUN_NAME}"
+  "${DATASET_ID}"
+  "${MAX_EVALS}"
+  "${BATCH_SIZE}"
+  "${CANDIDATE_POOL_SIZE}"
+  "${RANDOM_SEED}"
+  "${RUN_DIR}/search_space.yaml"
+  "${SEED_REPORT}"
+  "${RUN_DIR}"
 )
  
 if [ "$#" -gt 0 ]; then
   CMD+=("$@")
 fi
  
+export BATCH_EVAL_TIMEOUT_SEC
+
 printf '%q ' "${CMD[@]}" > "${CMD_PATH}"
 printf '\n' >> "${CMD_PATH}"
  
@@ -0,0 +1,117 @@
+#!/bin/bash
+
+set -euo pipefail
+
+cd "$(dirname "$0")/../.."
+source ./activate.sh
+
+usage() {
+  echo "usage: $0 <run_name> <dataset_id> <max_evals> <batch_size> <candidate_pool_size> <random_seed> <search_space> <seed_report> [resume_run_dir]" >&2
+  exit 1
+}
+
+if [ "$#" -lt 8 ]; then
+  usage
+fi
+
+RUN_NAME="$1"
+DATASET_ID="$2"
+MAX_EVALS="$3"
+BATCH_SIZE="$4"
+CANDIDATE_POOL_SIZE="$5"
+RANDOM_SEED="$6"
+SEARCH_SPACE="$7"
+SEED_REPORT="$8"
+RESUME_RUN_DIR="${9:-}"
+
+BATCH_EVAL_TIMEOUT_SEC="${BATCH_EVAL_TIMEOUT_SEC:-0}"
+RESTART_SLEEP_SEC="${RESTART_SLEEP_SEC:-30}"
+SEARCH_BASE_URL="${SEARCH_BASE_URL:-http://127.0.0.1:6002}"
+EVAL_WEB_BASE_URL="${EVAL_WEB_BASE_URL:-http://127.0.0.1:6010}"
+RUN_DIR="artifacts/search_evaluation/tuning_runs/${RUN_NAME}"
+
+mkdir -p "$(dirname "$RUN_DIR")"
+
+count_live_successes() {
+  python3 - "$RUN_DIR" <<'PY'
+import json
+import sys
+from pathlib import Path
+
+run_dir = Path(sys.argv[1])
+path = run_dir / "trials.jsonl"
+count = 0
+if path.is_file():
+    for line in path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        obj = json.loads(line)
+        if obj.get("status") == "ok" and not obj.get("is_seed"):
+            count += 1
+print(count)
+PY
+}
+
+build_cmd() {
+  local cmd=(
+    python
+    scripts/evaluation/tune_fusion.py
+    --mode optimize
+    --search-space "$SEARCH_SPACE"
+    --seed-report "$SEED_REPORT"
+    --tenant-id 163
+    --dataset-id "$DATASET_ID"
+    --queries-file scripts/evaluation/queries/queries.txt
+    --top-k 100
+    --language en
+    --search-base-url "$SEARCH_BASE_URL"
+    --eval-web-base-url "$EVAL_WEB_BASE_URL"
+    --max-evals "$MAX_EVALS"
+    --batch-size "$BATCH_SIZE"
+    --candidate-pool-size "$CANDIDATE_POOL_SIZE"
+    --random-seed "$RANDOM_SEED"
+    --batch-eval-timeout-sec "$BATCH_EVAL_TIMEOUT_SEC"
+  )
+  if [ -n "$RESUME_RUN_DIR" ]; then
+    cmd+=(--resume-run "$RESUME_RUN_DIR")
+  else
+    cmd+=(--run-name "$RUN_NAME")
+  fi
+  printf '%q ' "${cmd[@]}"
+  printf '\n'
+}
+
+attempt=0
+while true; do
+  live_successes="$(count_live_successes)"
+  if [ "$live_successes" -ge "$MAX_EVALS" ]; then
+    echo "[resilient] complete run_name=$RUN_NAME live_successes=$live_successes target=$MAX_EVALS"
+    exit 0
+  fi
+
+  attempt=$((attempt + 1))
+  if [ -d "$RUN_DIR" ]; then
+    RESUME_RUN_DIR="$RUN_DIR"
+  fi
+
+  echo "[resilient] attempt=$attempt run_name=$RUN_NAME live_successes=$live_successes target=$MAX_EVALS"
+  CMD_STR="$(build_cmd)"
+  echo "[resilient] cmd=$CMD_STR"
+
+  set +e
+  bash -lc "$CMD_STR"
+  exit_code=$?
+  set -e
+
+  live_successes="$(count_live_successes)"
+  echo "[resilient] exit_code=$exit_code live_successes=$live_successes"
+
+  if [ "$live_successes" -ge "$MAX_EVALS" ]; then
+    echo "[resilient] finished after attempt=$attempt"
+    exit 0
+  fi
+
+  echo "[resilient] sleeping ${RESTART_SLEEP_SEC}s before resume"
+  sleep "$RESTART_SLEEP_SEC"
+done
@@ -5,12 +5,34 @@ set -euo pipefail
 cd "$(dirname "$0")/../.."
 source ./activate.sh
  
-RUN_NAME="${RUN_NAME:-coarse_fusion_long_$(date -u +%Y%m%dT%H%M%SZ)}"
-MAX_EVALS="${MAX_EVALS:-36}"
-BATCH_SIZE="${BATCH_SIZE:-3}"
-CANDIDATE_POOL_SIZE="${CANDIDATE_POOL_SIZE:-512}"
-RANDOM_SEED="${RANDOM_SEED:-20260416}"
 DATASET_ID="${REPO_EVAL_DATASET_ID:-core_queries}"
+case "${DATASET_ID}" in
+  clothing_top771)
+    DEFAULT_SEARCH_SPACE="scripts/evaluation/tuning/coarse_rank_fusion_space_clothing_top771.yaml"
+    DEFAULT_SEED_REPORT="artifacts/search_evaluation/datasets/clothing_top771/batch_reports/batch_20260422T014610Z_5426bba1a6/report.md"
+    DEFAULT_MAX_EVALS="18"
+    DEFAULT_BATCH_SIZE="2"
+    DEFAULT_CANDIDATE_POOL_SIZE="160"
+    DEFAULT_RANDOM_SEED="20260422"
+    ;;
+  *)
+    DEFAULT_SEARCH_SPACE="scripts/evaluation/tuning/coarse_rank_fusion_space.yaml"
+    DEFAULT_SEED_REPORT="artifacts/search_evaluation/batch_reports/batch_20260415T150754Z_00b6a8aa3d.md"
+    DEFAULT_MAX_EVALS="36"
+    DEFAULT_BATCH_SIZE="3"
+    DEFAULT_CANDIDATE_POOL_SIZE="512"
+    DEFAULT_RANDOM_SEED="20260416"
+    ;;
+esac
+
+RUN_NAME="${RUN_NAME:-coarse_fusion_${DATASET_ID}_$(date -u +%Y%m%dT%H%M%SZ)}"
+SEARCH_SPACE="${SEARCH_SPACE:-${DEFAULT_SEARCH_SPACE}}"
+SEED_REPORT="${SEED_REPORT:-${DEFAULT_SEED_REPORT}}"
+MAX_EVALS="${MAX_EVALS:-${DEFAULT_MAX_EVALS}}"
+BATCH_SIZE="${BATCH_SIZE:-${DEFAULT_BATCH_SIZE}}"
+CANDIDATE_POOL_SIZE="${CANDIDATE_POOL_SIZE:-${DEFAULT_CANDIDATE_POOL_SIZE}}"
+RANDOM_SEED="${RANDOM_SEED:-${DEFAULT_RANDOM_SEED}}"
+BATCH_EVAL_TIMEOUT_SEC="${BATCH_EVAL_TIMEOUT_SEC:-0}"
  
 LAUNCH_DIR="artifacts/search_evaluation/tuning_launches"
 mkdir -p "${LAUNCH_DIR}"
@@ -19,29 +41,24 @@ PID_PATH=&quot;${LAUNCH_DIR}/${RUN_NAME}.pid&quot;
 CMD_PATH="${LAUNCH_DIR}/${RUN_NAME}.cmd"
  
 CMD=(
-  python
-  scripts/evaluation/tune_fusion.py
-  --mode optimize
-  --run-name "${RUN_NAME}"
-  --search-space scripts/evaluation/tuning/coarse_rank_fusion_space.yaml
-  --seed-report artifacts/search_evaluation/batch_reports/batch_20260415T150754Z_00b6a8aa3d.md
-  --tenant-id 163
-  --dataset-id "${DATASET_ID}"
-  --queries-file scripts/evaluation/queries/queries.txt
-  --top-k 100
-  --language en
-  --search-base-url http://127.0.0.1:6002
-  --eval-web-base-url http://127.0.0.1:6010
-  --max-evals "${MAX_EVALS}"
-  --batch-size "${BATCH_SIZE}"
-  --candidate-pool-size "${CANDIDATE_POOL_SIZE}"
-  --random-seed "${RANDOM_SEED}"
+  bash
+  scripts/evaluation/run_coarse_fusion_tuning_resilient.sh
+  "${RUN_NAME}"
+  "${DATASET_ID}"
+  "${MAX_EVALS}"
+  "${BATCH_SIZE}"
+  "${CANDIDATE_POOL_SIZE}"
+  "${RANDOM_SEED}"
+  "${SEARCH_SPACE}"
+  "${SEED_REPORT}"
 )
  
 if [ "$#" -gt 0 ]; then
   CMD+=("$@")
 fi
  
+export BATCH_EVAL_TIMEOUT_SEC
+
 printf '%q ' "${CMD[@]}" > "${CMD_PATH}"
 printf '\n' >> "${CMD_PATH}"
  
@@ -379,6 +379,7 @@ def run_batch_eval(
     top_k: int,
     language: str,
     force_refresh_labels: bool,
+    timeout_sec: int,
 ) -> Dict[str, Any]:
     cmd = [
         str(PROJECT_ROOT / ".venv" / "bin" / "python"),
@@ -397,13 +398,14 @@ def run_batch_eval(
         cmd.extend(["--queries-file", str(queries_file)])
     if force_refresh_labels:
         cmd.append("--force-refresh-labels")
+    timeout = timeout_sec if timeout_sec and timeout_sec > 0 else None
     completed = subprocess.run(
         cmd,
         cwd=PROJECT_ROOT,
         check=True,
         capture_output=True,
         text=True,
-        timeout=7200,
+        timeout=timeout,
     )
     output = (completed.stdout or "") + "\n" + (completed.stderr or "")
     batch_ids = re.findall(r"batch_id=([A-Za-z0-9_]+)", output)
@@ -1221,6 +1223,7 @@ def run_optimize_mode(args: argparse.Namespace) -&gt; None:
                         top_k=args.top_k,
                         language=args.language,
                         force_refresh_labels=force_refresh_labels,
+                        timeout_sec=args.batch_eval_timeout_sec,
                     )
                     ensure_disk_headroom(
                         min_free_gb=args.min_free_gb,
@@ -1362,6 +1365,7 @@ def build_parser() -&gt; argparse.ArgumentParser:
     parser.add_argument("--resume-run", default=None)
     parser.add_argument("--max-evals", type=int, default=12)
     parser.add_argument("--batch-size", type=int, default=3)
+    parser.add_argument("--batch-eval-timeout-sec", type=int, default=0)
     parser.add_argument("--init-random", type=int, default=None)
     parser.add_argument("--candidate-pool-size", type=int, default=None)
     parser.add_argument("--random-seed", type=int, default=20260415)
@@ -0,0 +1,161 @@
+target_path: coarse_rank.fusion
+
+baseline:
+  es_bias: 10.0
+  es_exponent: 0.05
+  text_bias: 0.1
+  text_exponent: 0.35
+  text_translation_weight: 1.0
+  knn_text_weight: 1.0
+  knn_image_weight: 2.0
+  knn_tie_breaker: 0.3
+  knn_bias: 0.2
+  knn_exponent: 5.6
+  knn_text_bias: 0.2
+  knn_text_exponent: 0.0
+  knn_image_bias: 0.2
+  knn_image_exponent: 0.0
+
+parameters:
+  es_bias: {min: 2.0, max: 20.0, scale: log, round: 4}
+  es_exponent: {min: 0.03, max: 0.28, scale: linear, round: 4}
+  text_bias: {min: 0.01, max: 4.0, scale: log, round: 4}
+  text_exponent: {min: 0.2, max: 1.6, scale: linear, round: 4}
+  text_translation_weight: {min: 0.7, max: 1.8, scale: linear, round: 4}
+  knn_text_weight: {min: 0.05, max: 1.8, scale: linear, round: 4}
+  knn_image_weight: {min: 1.2, max: 6.0, scale: linear, round: 4}
+  knn_tie_breaker: {min: 0.0, max: 0.4, scale: linear, round: 4}
+  knn_bias: {min: 0.001, max: 2.5, scale: log, round: 4}
+  knn_exponent: {min: 0.05, max: 12.0, scale: log, round: 4}
+  knn_text_bias: {min: 0.001, max: 4.0, scale: log, round: 4}
+  knn_text_exponent: {min: 0.0, max: 2.0, scale: linear, round: 4}
+  knn_image_bias: {min: 0.01, max: 1.5, scale: log, round: 4}
+  knn_image_exponent: {min: 0.0, max: 6.0, scale: linear, round: 4}
+
+seed_experiments:
+  - name: seed_low_knn_global
+    description: 先验证 021002 中出现的低 knn 全局指数，去掉 reranker 后是否仍有收益。
+    params:
+      knn_bias: 0.6
+      knn_exponent: 0.4
+  - name: seed_bigset_knn_soft
+    description: 从低 knn 全局指数出发，继续平滑 knn 非线性。
+    params:
+      text_exponent: 0.42
+      text_translation_weight: 1.05
+      knn_text_weight: 0.85
+      knn_image_weight: 2.4
+      knn_tie_breaker: 0.18
+      knn_bias: 0.9
+      knn_exponent: 0.18
+      knn_image_exponent: 0.2
+  - name: seed_bigset_knn_mid
+    description: 保留平滑 knn，但让 image 通路再强一点，验证大集是否需要适度非线性。
+    params:
+      es_bias: 8.0
+      es_exponent: 0.08
+      text_bias: 0.15
+      text_exponent: 0.5
+      text_translation_weight: 1.15
+      knn_text_weight: 0.65
+      knn_image_weight: 3.1
+      knn_tie_breaker: 0.12
+      knn_bias: 0.45
+      knn_exponent: 0.85
+      knn_text_bias: 0.35
+      knn_text_exponent: 0.2
+      knn_image_bias: 0.22
+      knn_image_exponent: 0.8
+  - name: seed_bigset_text_stable
+    description: 提高 lexical 区分度，观察大集是否更偏好稳健文本排序。
+    params:
+      es_bias: 7.0
+      es_exponent: 0.12
+      text_bias: 0.25
+      text_exponent: 0.72
+      text_translation_weight: 1.0
+      knn_text_weight: 0.55
+      knn_image_weight: 2.2
+      knn_tie_breaker: 0.08
+      knn_bias: 0.7
+      knn_exponent: 0.35
+      knn_text_bias: 0.5
+      knn_text_exponent: 0.4
+      knn_image_bias: 0.18
+      knn_image_exponent: 0.35
+  - name: seed_hybrid_transfer
+    description: 以大集 baseline 为主，温和吸收小集历史赢家中的 image/text 强化模式。
+    params:
+      es_bias: 7.2
+      es_exponent: 0.15
+      text_bias: 0.6
+      text_exponent: 0.82
+      text_translation_weight: 1.28
+      knn_text_weight: 0.45
+      knn_image_weight: 4.0
+      knn_tie_breaker: 0.08
+      knn_bias: 0.2
+      knn_exponent: 1.2
+      knn_text_bias: 0.8
+      knn_text_exponent: 0.45
+      knn_image_bias: 0.3
+      knn_image_exponent: 1.4
+  - name: seed_legacy_bo234
+    description: 直接验证 53 条集历史最优在 771 条集上的迁移表现。
+    params:
+      es_bias: 7.214
+      es_exponent: 0.2025
+      text_bias: 4.0
+      text_exponent: 1.584
+      text_translation_weight: 1.4441
+      knn_text_weight: 0.1
+      knn_image_weight: 5.6232
+      knn_tie_breaker: 0.021
+      knn_bias: 0.0019
+      knn_exponent: 11.8477
+      knn_text_bias: 2.3125
+      knn_text_exponent: 1.1547
+      knn_image_bias: 0.9641
+      knn_image_exponent: 5.8671
+  - name: seed_legacy_bo340
+    description: 验证小集冠军参数在大集上是否仍有价值。
+    params:
+      es_bias: 5.887
+      es_exponent: 0.2145
+      text_bias: 4.0
+      text_exponent: 1.6
+      text_translation_weight: 1.4788
+      knn_text_weight: 0.3693
+      knn_image_weight: 5.7028
+      knn_tie_breaker: 0.0174
+      knn_bias: 0.0016
+      knn_exponent: 12.0
+      knn_text_bias: 2.6071
+      knn_text_exponent: 1.0458
+      knn_image_bias: 0.8282
+      knn_image_exponent: 6.0
+  - name: seed_image_guard
+    description: 控制 image 权重但允许 image 子项指数，检查 recall 与 precision 的平衡点。
+    params:
+      es_bias: 9.0
+      es_exponent: 0.09
+      text_bias: 0.12
+      text_exponent: 0.45
+      text_translation_weight: 1.1
+      knn_text_weight: 0.7
+      knn_image_weight: 2.8
+      knn_tie_breaker: 0.1
+      knn_bias: 0.55
+      knn_exponent: 0.55
+      knn_text_bias: 0.25
+      knn_text_exponent: 0.15
+      knn_image_bias: 0.28
+      knn_image_exponent: 1.0
+
+optimizer:
+  init_random: 2
+  candidate_pool_size: 160
+  explore_probability: 0.12
+  local_jitter_probability: 0.62
+  elite_fraction: 0.25
+  min_normalized_distance: 0.08