V6.5-V2-dynamic: upload batch (scripts + state + model) [79 files]

scripts/train_v6_5_v2.py

@@ -2446,8 +2446,13 @@ def upload_to_hf_batch(
         if not src.exists():
             logger.warning(f"[V6.5-V2] Skipping non-existent file: {src}")
             continue
-        # Caminho no repo: mantém apenas o nome do arquivo (sem dirs locais)
-        path_in_repo = src.name
+        # V6.5-V2-auto-conscience-v2 — PRESERVA estrutura de diretórios no HF.
+        # path_in_repo = path relativo a BIGRU_ROOT (ex: src/bigru_t/model/kohonen_learning_system.py)
+        try:
+            path_in_repo = str(src.relative_to(BIGRU_ROOT)).replace("\\", "/")
+        except ValueError:
+            # Fallback: filename only (for files outside BIGRU_ROOT)
+            path_in_repo = src.name
         upload_plan.append({
             "local": str(src),
             "path_in_repo": path_in_repo,
@@ -2892,43 +2897,68 @@ def main() -> int:
     if hf_token:
         logger.info("\n[V6.5-V2] Coletando arquivos para upload HF em LOTE...")
 
-        # Scripts a enviar (sobrescrevem antigos no HF)
-        # V6.5-V2-metrics-FIX-2: inclui vqvae2_hierarchical_flexnet.py
-        # (vqvae2 reativado em FASE1 e FASE2 — módulo dependente do KLS).
-        scripts_to_upload: List[Path] = [
-            BIGRU_ROOT / "scripts" / "train_v6_5_v2.py",
-            SRC_ROOT / "bigru_t" / "model" / "kohonen_learning_system.py",
-            SRC_ROOT / "bigru_t" / "model" / "hyp_t.py",
-            SRC_ROOT / "bigru_t" / "model" / "som_metrics.py",  # V6.5-V2-metrics
-            SRC_ROOT / "bigru_t" / "model" / "vqvae2_hierarchical.py",
-            SRC_ROOT / "bigru_t" / "model" / "vqvae2_hierarchical_flexnet.py",  # V6.5-V2-metrics-FIX-2
-            SRC_ROOT / "bigru_t" / "model" / "attention_multimodal.py",
-            SRC_ROOT / "bigru_t" / "data" / "streaming_datasets.py",
-            SRC_ROOT / "bigru_t" / "utils" / "xeon_runtime.py",
-        ]
-        # V6.5-V2-metrics-FIX-3: FILTRA arquivos .pt do upload HF.
-        # User requirement: "ao concluir enviar para o HF os arquivos e módulos
-        # e scripts em lote" + auditoria encontrou que .pt files estavam sendo
-        # uploaded (MODEL_STATES_PATH e *_after_conhecimento.pt). Estados do
-        # modelo (>40MB cada) não devem ir ao HF — apenas scripts e relatórios.
-        outputs_to_upload: List[Path] = [
-            REPORT_PATH,
-            V2_PHASES_EVAL_PATH,
-            PREDICT_FIX_EVAL_PATH,
-            ATTENTION_EVAL_PATH,
-            USER_QUESTIONS_PATH,
-        ]
-        # Filtra os que existem E não são .pt/.pth/.bin/.safetensors
+        # V6.5-V2-auto-conscience-v2 — Coleta TODOS os arquivos .py sob src/ e
+        # scripts/ (exceto deprecados/), preservando estrutura de diretórios
+        # no HF (path_in_repo = path relativo a BIGRU_ROOT).
+        # User requirement (latest): "ao concluir enviar para o HF os arquivos
+        # de Estado do modelo e módulos python e scripts em lote" +
+        # "estado (e treinamento para permitir continuar novo treinamento)".
+        #
+        # Estratégia:
+        #   (a) Módulos .py + scripts .py sob src/ e scripts/ — preserva dirs
+        #   (b) Model state .pt — UPLOAD ao HF (user quer continuar treino)
+        #       Verifica tamanho ≤ 500MB antes de enviar (HF LFS free tier)
+        #   (c) Relatórios JSON — para auditoria
         all_files: List[Path] = []
-        _FORBIDDEN_UPLOAD_EXTS = {'.pt', '.pth', '.bin', '.safetensors', '.ckpt'}
-        for p in scripts_to_upload + outputs_to_upload:
-            if not p.exists():
-                logger.warning(f"[V6.5-V2] Upload file not found: {p}")
-                continue
-            if p.suffix.lower() in _FORBIDDEN_UPLOAD_EXTS:
-                logger.info(f"[V6.5-V2] Skipping model state from HF upload: {p.name}")
+        _EXCLUDE_DIRS = {"deprecados", "__pycache__", ".git", ".pytest_cache",
+                          "model_final", "docs", "node_modules", ".venv", "venv"}
+        _INCLUDE_EXTS = {".py", ".md", ".txt", ".json", ".sh"}
+        # (a) Walk src/ e scripts/ preservando paths relativos
+        for root_dir in [SRC_ROOT, BIGRU_ROOT / "scripts"]:
+            if not root_dir.exists():
                 continue
-            all_files.append(p)
+            for path in root_dir.rglob("*"):
+                if not path.is_file():
+                    continue
+                try:
+                    rel = path.relative_to(BIGRU_ROOT)
+                except ValueError:
+                    continue
+                if any(part in _EXCLUDE_DIRS for part in rel.parts):
+                    continue
+                if path.suffix.lower() not in _INCLUDE_EXTS:
+                    continue
+                all_files.append(path)
+        # (b) Model state .pt files (estado para continuar treino)
+        # User requirement: "estado (e treinamento para permitir continuar
+        # novo treinamento) do modelo testados e aprovados".
+        # Inclui: v6_5_v2_model_states.pt, v6_5_v2_model_states_after_conhecimento.pt
+        # Verifica tamanho ≤ 500MB (HF LFS free tier sem autenticar LFS).
+        _MAX_HF_LFS_SIZE_BYTES = 500 * 1024 * 1024  # 500MB
+        for pt_candidate in [
+            MODEL_STATES_PATH,
+            BIGRU_ROOT / "v6_5_v2_model_states_after_conhecimento.pt",
+        ]:
+            if pt_candidate.exists():
+                pt_size = pt_candidate.stat().st_size
+                if pt_size <= _MAX_HF_LFS_SIZE_BYTES:
+                    all_files.append(pt_candidate)
+                    logger.info(f"[V6.5-V2] Incluindo estado do modelo no upload HF: {pt_candidate.name} ({pt_size/1e6:.1f}MB)")
+                else:
+                    logger.warning(f"[V6.5-V2] Estado muito grande para HF LFS (>{_MAX_HF_LFS_SIZE_BYTES/1e6:.0f}MB): {pt_candidate.name} ({pt_size/1e6:.1f}MB) — será mantido localmente apenas")
+        # (c) Relatórios JSON
+        for rpt in [REPORT_PATH, V2_PHASES_EVAL_PATH, PREDICT_FIX_EVAL_PATH,
+                     ATTENTION_EVAL_PATH, USER_QUESTIONS_PATH]:
+            if rpt.exists():
+                all_files.append(rpt)
+        # Dedup
+        seen = set()
+        deduped_files: List[Path] = []
+        for p in all_files:
+            if str(p) not in seen:
+                seen.add(str(p))
+                deduped_files.append(p)
+        all_files = deduped_files
 
         # Apaga HF_TOKEN dos scripts ANTES do upload (não enviar tokens)
         scrub_result = scrub_hf_token_from_scripts(BIGRU_ROOT / "scripts")
@@ -2977,23 +3007,39 @@ def main() -> int:
     final_storage_cleanup = aggressive_storage_cleanup()
     aggressive_memory_cleanup()
 
-    # V6.5-V2-metrics-FIX: Remove TODOS os arquivos .pt do projeto após upload
-    # ao HF (user requirement: "remover imediatamente todos os arquivos *.pt
-    # e não armazenar mais arquivos no worklog"). O estado do modelo já foi
-    # enviado ao HF e pode ser recuperado de lá para continuar treinamento.
-    logger.info("[V6.5-V2-metrics-FIX] Removendo TODOS os arquivos .pt locais...")
+    # V6.5-V2-auto-conscience-v2 — Limpeza de .pt files: SÓ remove se o upload
+    # HF foi bem-sucedido. User requirement (latest):
+    #   "estado (e treinamento para permitir continuar novo treinamento)"
+    # Se HF upload falhou, MANTÉM os .pt locais para o usuário poder recuperá-los.
+    # Se HF upload OK, remove apenas os .pt do diretório do projeto (exceto
+    # download/, que é a área de entrega para o usuário).
     pt_files_removed: List[str] = []
-    for pt_file in PROJECT_ROOT.rglob("*.pt"):
-        try:
-            rel_path = str(pt_file.relative_to(PROJECT_ROOT))
-            pt_file.unlink()
-            pt_files_removed.append(rel_path)
-            logger.info(f"  removed .pt: {rel_path}")
-        except Exception as e:
-            logger.warning(f"  failed to remove {pt_file}: {e}")
-    logger.info(
-        f"[V6.5-V2-metrics-FIX] {len(pt_files_removed)} .pt files removed."
-    )
+    if hf_upload_result.get("uploaded", False):
+        logger.info("[V6.5-V2-auto-conscience-v2] HF upload OK — removendo .pt locais do projeto (estado já está no HF)...")
+        for pt_file in BIGRU_ROOT.rglob("*.pt"):
+            try:
+                rel_path = str(pt_file.relative_to(PROJECT_ROOT))
+                pt_file.unlink()
+                pt_files_removed.append(rel_path)
+                logger.info(f"  removed .pt: {rel_path}")
+            except Exception as e:
+                logger.warning(f"  failed to remove {pt_file}: {e}")
+        logger.info(
+            f"[V6.5-V2-auto-conscience-v2] {len(pt_files_removed)} .pt files removed from project."
+        )
+    else:
+        logger.warning(
+            "[V6.5-V2-auto-conscience-v2] HF upload falhou — MANTENDO .pt locais "
+            "para preservar estado do modelo. User pode recuperar manualmente."
+        )
+        # Copia .pt para download/ para que o usuário tenha acesso
+        for pt_file in BIGRU_ROOT.rglob("*.pt"):
+            try:
+                dst = DOWNLOAD_DIR / pt_file.name
+                shutil.copy2(pt_file, dst)
+                logger.info(f"  .pt copiado para download/: {pt_file.name}")
+            except Exception as e:
+                logger.warning(f"  failed to copy {pt_file}: {e}")
 
     # Atualiza worklog.md (sem referenciar .pt files)
     try:

v6_5_v2_attention_eval.json

@@ -3,18 +3,18 @@
   "user_requirement": "verificar se o mecanismo de atenção está ativo e acessado logicamente funcional",
   "metrics": {
     "active": true,
-    "n_calls": 10000,
+    "n_calls": 500,
     "n_errors": 0,
-    "last_norm_in": 109.84779357910156,
-    "last_norm_out": 114.40419006347656,
+    "last_norm_in": 108.67192077636719,
+    "last_norm_out": 112.93913269042969,
     "last_attn_activated": true,
-    "last_attn_diff_norm": 114.49571990966797,
+    "last_attn_diff_norm": 114.95559692382812,
     "n_heads": 8,
     "logic_functional": true
   },
   "active": true,
   "logic_functional": true,
-  "n_calls": 10000,
+  "n_calls": 500,
   "n_errors": 0,
   "n_heads": 8,
   "assessment": "PASS"

v6_5_v2_model_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d441bc350835134d8bf3d2cba4fb0a7c4f29871779b8b414adddf65fc6b792d5
+size 217669998

v6_5_v2_model_states_after_conhecimento.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d13463dc76a501f504b912149ced55883d2e3227c61d359157cb84e00fe331a2
+size 217675097

v6_5_v2_predict_fix_eval.json

@@ -5,8 +5,8 @@
   "results": [
     {
       "query": "o gato dorme na cama",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -14,8 +14,8 @@
     },
     {
       "query": "calcule dois mais dois",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -23,8 +23,8 @@
     },
     {
       "query": "qual é a capital do brasil",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -32,8 +32,8 @@
     },
     {
       "query": "explique o que é uma rede neural",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -41,8 +41,8 @@
     },
     {
       "query": "olá como você está",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -50,8 +50,8 @@
     },
     {
       "query": "traduza hello para portugues",
-      "prediction": "short_text",
-      "probability": 0.443034291267395,
+      "prediction": "punctuated",
+      "probability": 1.0,
       "is_gato_hardcoded": false,
       "is_cachorro_hardcoded": false,
       "is_registry_label": true,
@@ -65,70 +65,7 @@
         "unpunctuated",
         "punctuated"
       ],
-      "prediction": "unpunctuated",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "carolina-c4ai/corpus-carolina",
-      "expected_labels": [
-        "raw_corpus",
-        "normalized_text"
-      ],
-      "prediction": "raw_corpus",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "CEIA-POSITIVO/ultrachat_br_clustred_balanced_v1",
-      "expected_labels": [
-        "user_turn",
-        "assistant_turn"
-      ],
-      "prediction": "user_turn",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "dominguesm/Canarim-Instruct-PTBR-Dataset",
-      "expected_labels": [
-        "instruction",
-        "response"
-      ],
-      "prediction": "instruction",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "adalbertojunior/punctuation-ptbr",
-      "expected_labels": [
-        "unpunctuated",
-        "punctuated"
-      ],
-      "prediction": "unpunctuated",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "iara-project/news-articles-ptbr-dataset",
-      "expected_labels": [
-        "headline",
-        "body"
-      ],
-      "prediction": "headline",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "manoela/noticias_ptbr",
-      "expected_labels": [
-        "headline",
-        "body"
-      ],
-      "prediction": "headline",
-      "valid_for_dataset": true
-    },
-    {
-      "dataset": "BrunoN-Dev/corpus-ptbr-v1",
-      "expected_labels": [
-        "short_text",
-        "long_text"
-      ],
-      "prediction": "short_text",
+      "prediction": "punctuated",
       "valid_for_dataset": true
     }
   ],

v6_5_v2_report.json

@@ -1,6 +1,6 @@
 {
   "version": "V6.5-V2",
-  "timestamp": "2026-08-09T03:12:19.635254",
+  "timestamp": "2026-08-09T03:50:50.214658",
   "config": {
     "som_grid": [
       6,
@@ -54,10 +54,10 @@
     "init_done": true
   },
   "fp16_benchmark": {
-    "best_time_ms": 96.71666700160131,
-    "avg_time_ms": 98.70659000080195,
-    "best_tflops": 1.3234533815963772,
-    "avg_tflops": 1.296772586298038,
+    "best_time_ms": 93.99212000062107,
+    "avg_time_ms": 98.73566050009686,
+    "best_tflops": 1.361816288420287,
+    "avg_tflops": 1.2963907807136654,
     "matrix_size": 4000.0
   },
   "v2_verification": {
@@ -84,24 +84,24 @@
     "all_pass": true
   },
   "fase_1_conhecimento_summary": {
-    "total_samples": 8000,
-    "meta_atingida": true,
-    "elapsed_s": 460.5420436859131,
-    "storage_critical_stopped": false
+    "total_samples": 500,
+    "meta_atingida": false,
+    "elapsed_s": 37.09058332443237,
+    "storage_critical_stopped": true
   },
   "fase_2_punicão_summary": {
-    "total_samples": 2000,
-    "meta_atingida": true,
-    "elapsed_s": 728.1188757419586,
-    "punishment_events": 120,
-    "hypotheses_trainings": 60,
-    "delta_applications": 60,
-    "skipped": false
+    "total_samples": 0,
+    "meta_atingida": false,
+    "elapsed_s": 0.0,
+    "punishment_events": 0,
+    "hypotheses_trainings": 0,
+    "delta_applications": 0,
+    "skipped": true
   },
   "attention_eval_summary": {
     "active": true,
     "logic_functional": true,
-    "n_calls": 10000,
+    "n_calls": 500,
     "assessment": "PASS"
   },
   "predict_fix_summary": {
@@ -117,53 +117,52 @@
     "n_with_think": 0,
     "answer_rate": 1.0,
     "think_rate": 0.0,
-    "avg_latency_ms": 4.7105153401692705,
+    "avg_latency_ms": 4.463593165079753,
     "avg_reasoning_length": 69.0
   },
   "model_states_saved_to": "/home/z/my-project/BiGRU_T_version/v6_5_v2_model_states.pt",
   "save_info": {
     "saved": true,
     "path": "/home/z/my-project/BiGRU_T_version/v6_5_v2_model_states.pt",
-    "size_mb": 220.15666,
-    "size_gb": 0.20503686740994453,
+    "size_mb": 217.669998,
+    "size_gb": 0.20272098295390606,
     "size_status": "OK",
     "size_within_1gb_limit": true,
     "reason": "end_of_training_v2",
-    "step": 700,
-    "total_samples": 10000,
+    "step": 35,
+    "total_samples": 500,
     "n_tensors": 7,
     "n_buffer_tail": 64
   },
   "final_v2_metrics": {
     "version": "V2-dynamic",
-    "n_hypotheses": 4,
-    "n_hypotheses_active": 4,
+    "n_hypotheses": 16,
+    "n_hypotheses_active": 16,
     "max_n_hypotheses": 32,
-    "n_trials": 6,
-    "hyp_train_steps": 80,
+    "n_trials": 3,
+    "hyp_train_steps": 30,
     "hyp_lr": 0.0001,
-    "delta_scale": 0.0010000000474974513,
+    "delta_scale": 0.009999999776482582,
     "n_generators": 32,
     "punishment_count": 0,
     "success_count": 0,
-    "training_ready": false,
-    "classifier_trained": true,
-    "ewc_reference_set": true,
+    "training_ready": true,
+    "classifier_trained": false,
+    "ewc_reference_set": false,
     "buffer_size": 128,
-    "total_hyp_steps_executed": 4550,
-    "n_train_hyp_calls": 60,
+    "total_hyp_steps_executed": 0,
+    "n_train_hyp_calls": 0,
     "dynamic_adaptation": {
-      "loss_history_len": 8,
+      "loss_history_len": 0,
       "loss_stats": {
-        "slope": -0.00034456806523459297,
-        "volatility": 0.0024739346406965515,
-        "mean": 0.6898908242583275,
-        "std": 0.0017067448084313731,
-        "n": 8
+        "slope": 0.0,
+        "volatility": 0.0,
+        "mean": 0.0,
+        "n": 0
       },
-      "punishment_rate": 1.0,
-      "punishment_window_size": 12,
-      "n_adaptations": 20,
+      "punishment_rate": 0.0,
+      "punishment_window_size": 0,
+      "n_adaptations": 0,
       "limits": {
         "min_n_hypotheses": 4,
         "max_n_hypotheses": 32,
@@ -172,128 +171,7 @@
         "min_hyp_train_steps": 10,
         "max_hyp_train_steps": 80
       },
-      "last_5_adaptations": [
-        {
-          "trigger": "punishment",
-          "step": 115,
-          "before": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "after": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "adapted": false,
-          "rules_fired": [],
-          "loss_stats": {
-            "slope": -0.00037475568907601494,
-            "volatility": 0.0033018073049031742,
-            "mean": 0.6912502944469452,
-            "std": 0.0022823752717213938,
-            "n": 8
-          },
-          "punishment_rate": 1.0
-        },
-        {
-          "trigger": "auto",
-          "step": 116,
-          "before": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
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-          "after": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
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-          "adapted": false,
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-            "slope": -0.0005504623765037174,
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-            "std": 0.002346315435793899,
-            "n": 8
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-          "step": 117,
-          "before": {
-            "n_hypotheses": 4,
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-            "hyp_train_steps": 80
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-        {
-          "trigger": "auto",
-          "step": 118,
-          "before": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "after": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "adapted": false,
-          "rules_fired": [],
-          "loss_stats": {
-            "slope": -0.00034456806523459297,
-            "volatility": 0.0024739346406965515,
-            "mean": 0.6898908242583275,
-            "std": 0.0017067448084313731,
-            "n": 8
-          },
-          "punishment_rate": 1.0
-        },
-        {
-          "trigger": "punishment",
-          "step": 119,
-          "before": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "after": {
-            "n_hypotheses": 4,
-            "n_trials": 6,
-            "hyp_train_steps": 80
-          },
-          "adapted": false,
-          "rules_fired": [],
-          "loss_stats": {
-            "slope": -0.00034456806523459297,
-            "volatility": 0.0024739346406965515,
-            "mean": 0.6898908242583275,
-            "std": 0.0017067448084313731,
-            "n": 8
-          },
-          "punishment_rate": 1.0
-        }
-      ]
+      "last_5_adaptations": []
     }
   }
 }

v6_5_v2_user_questions.json

@@ -18,17 +18,17 @@
       "context_provided": false,
       "system_prompt_used": false,
       "few_shot_examples": false,
-      "som_prediction": "short_text",
+      "som_prediction": "punctuated",
       "reasoning_length": 69,
       "has_think": false,
       "has_plan": false,
       "has_answer": true,
       "has_decompose": false,
       "think_preview": "",
-      "answer_preview": "ReasoningEngine disabled. SOM prediction: short_text",
-      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: short_text</answer>",
+      "answer_preview": "ReasoningEngine disabled. SOM prediction: punctuated",
+      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: punctuated</answer>",
       "n_tags": 1,
-      "latency_ms": 4.777431488037109
+      "latency_ms": 4.607677459716797
     },
     {
       "query": "Lula reserva valor",
@@ -36,17 +36,17 @@
       "context_provided": false,
       "system_prompt_used": false,
       "few_shot_examples": false,
-      "som_prediction": "short_text",
+      "som_prediction": "punctuated",
       "reasoning_length": 69,
       "has_think": false,
       "has_plan": false,
       "has_answer": true,
       "has_decompose": false,
       "think_preview": "",
-      "answer_preview": "ReasoningEngine disabled. SOM prediction: short_text",
-      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: short_text</answer>",
+      "answer_preview": "ReasoningEngine disabled. SOM prediction: punctuated",
+      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: punctuated</answer>",
       "n_tags": 1,
-      "latency_ms": 4.729270935058594
+      "latency_ms": 4.328012466430664
     },
     {
       "query": "Amazonas força-tarefa vítimas",
@@ -54,17 +54,17 @@
       "context_provided": false,
       "system_prompt_used": false,
       "few_shot_examples": false,
-      "som_prediction": "short_text",
+      "som_prediction": "punctuated",
       "reasoning_length": 69,
       "has_think": false,
       "has_plan": false,
       "has_answer": true,
       "has_decompose": false,
       "think_preview": "",
-      "answer_preview": "ReasoningEngine disabled. SOM prediction: short_text",
-      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: short_text</answer>",
+      "answer_preview": "ReasoningEngine disabled. SOM prediction: punctuated",
+      "raw_response_preview": "<answer>ReasoningEngine disabled. SOM prediction: punctuated</answer>",
       "n_tags": 1,
-      "latency_ms": 4.624843597412109
+      "latency_ms": 4.455089569091797
     }
   ],
   "summary": {
@@ -72,7 +72,7 @@
     "n_with_think": 0,
     "answer_rate": 1.0,
     "think_rate": 0.0,
-    "avg_latency_ms": 4.7105153401692705,
+    "avg_latency_ms": 4.463593165079753,
     "avg_reasoning_length": 69.0
   },
   "quality_assessment": {