V6.5-attn-v2 cleanup: remove legacy scripts/train_v6_5_7ds_attn.py

scripts/train_v6_5_7ds_attn.py

@@ -1,1751 +0,0 @@
-"""train_v6_5_7ds_attn.py — V6.5-attn (attention integrado + 7 datasets + 100/batch + meta ≥5000).
-
-═══════════════════════════════════════════════════════════════════════════════
-V6.5-attn — ATTENTION ATIVO + 100 SAMPLES/BATCH + META ≥5000 + 7 DATASETS
-═══════════════════════════════════════════════════════════════════════════════
-
-User requirements (latest):
-    1. limpar ambiente virtual; usar última versão
-    2. verificar se o mecanismo de atenção está ativo e acessado logicamente funcional
-    3. HF_TOKEN (apagada após uso)
-    4. usar streaming_datasets.py e ativar xeon_runtime.py
-    5. streaming de 100 em 100 samples até esgotar nesta sequência (7 datasets):
-       1. dominguesm/restore-punctuation-ptbr-dataset
-       2. carolina-c4ai/corpus-carolina
-       3. CEIA-POSITIVO/ultrachat_br_clustred_balanced_v1
-       4. dominguesm/Canarim-Instruct-PTBR-Dataset
-       5. adalbertojunior/punctuation-ptbr
-       6. iara-project/news-articles-ptbr-dataset
-       7. manoela/noticias_ptbr
-    6. streaming com pausas para dar tempo de processar e salvar arquivos
-    7. parar se o Armazenamento for crítico
-    8. verificar métricas do modelo, raciocínio e capacidade de responder
-       (qualidade da resposta)
-    9. NÃO reduzir tempo e NÃO gerar dados sintéticos
-   10. meta mínima 5000 samples ou mais
-
-Diferenças críticas vs train_v6_5_7ds.py:
-    - NOVO: MultiHeadAttention integrado ao text_to_4d_vector (V6.5-attn)
-    - NOVO: get_attention_metrics() exposto em get_state_metrics()
-    - NOVO: Check #11 = attention_logic_functional (n_calls>0, n_errors==0,
-      last_attn_activated=True)
-    - STREAM_BATCH_SIZE=100 (era 50) — user requirement "de 100 em 100 samples"
-    - N_PHASES=8 (era 2) → 100 × 7 × 8 = 5600 samples (meta ≥5000 OK)
-    - EPOCHS=1 (cada phase = 1 epoch; 8 phases = 8 passes pelo streaming)
-    - META_MINIMA_SAMPLES=5000 (user requirement: "meta mínima 5000 samples ou mais")
-    - Saídas em v6_5_7ds_attn_*.json (não sobrescreve v6_5_7ds_*.json)
-"""
-from __future__ import annotations
-
-import gc
-import json
-import logging
-import math
-import os
-import shutil
-import sys
-import time
-import traceback
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-# ============================================================================
-# 0. Paths e logging
-# ============================================================================
-PROJECT_ROOT = Path("/home/z/my-project")
-BIGRU_ROOT = PROJECT_ROOT / "BiGRU_T_version"
-SRC_ROOT = BIGRU_ROOT / "src"
-REPORT_PATH = BIGRU_ROOT / "v6_5_7ds_attn_report.json"
-METRICS_PATH = BIGRU_ROOT / "v6_5_7ds_attn_training_metrics.json"
-MODULE_ANALYSIS_PATH = BIGRU_ROOT / "v6_5_7ds_attn_module_analysis.json"
-SCRIPT_ACTIVITY_PATH = BIGRU_ROOT / "v6_5_7ds_attn_script_activity.json"
-EWC_W8A8_BENCH_PATH = BIGRU_ROOT / "v6_5_7ds_attn_ewc_w8a8_benchmark.json"
-REASONING_EVAL_PATH = BIGRU_ROOT / "v6_5_7ds_attn_reasoning_eval.json"
-W8A8_COMPRESSION_PATH = BIGRU_ROOT / "v6_5_7ds_attn_w8a8_compression.json"
-ATTENTION_EVAL_PATH = BIGRU_ROOT / "v6_5_7ds_attn_attention_eval.json"
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="[%(asctime)s] [%(levelname)s] %(message)s",
-    datefmt="%H:%M:%S",
-)
-logger = logging.getLogger("train_v6_5_7ds_attn")
-
-# ============================================================================
-# 1. ATIVAR xeon_runtime.py + FORÇAR V65_ENABLE_STREAMING=1
-# ============================================================================
-# User requirement: "usar streaming_datasets.py e ativar xeon_runtime.py"
-os.environ["V65_ENABLE_STREAMING"] = "1"
-logger.info(f"[V6.5-attn] V65_ENABLE_STREAMING={os.environ['V65_ENABLE_STREAMING']} (forced)")
-
-sys.path.insert(0, str(SRC_ROOT))
-
-from bigru_t.utils.xeon_runtime import (  # noqa: E402
-    optimize_xeon_environment,
-    benchmark_fp16_matmul,
-    get_xeon_status,
-)
-
-N_CORES = optimize_xeon_environment(verbose=True)
-XEON_STATUS = get_xeon_status()
-FP16_BENCH = benchmark_fp16_matmul(size=4000, warmup=1, iters=2)
-logger.info(f"[V6.5-attn] Xeon FP16 benchmark: {FP16_BENCH}")
-
-# ============================================================================
-# 2. Configurações V6.5-attn (NO REDUCTIONS — 864 neurons, 1024 hidden)
-# ============================================================================
-BATCH_SIZE = 16  # KLS process_batch size
-MAX_SEQ_LEN = 8
-
-# User requirement: "Aumentar SOM grid para (6,6,6,4)=864 neurônios"
-HIDDEN_DIM = 1024
-VOCAB_SIZE = 16384
-SOM_GRID = (6, 6, 6, 4)  # 864 neurons
-T_MAX = 10000
-N_START = 10
-LAMBDA_EWC = 0.02
-ALPHA0 = 0.1
-SIGMA0 = 1.5
-DIM_CHOICE = "y"
-
-# User requirement: "aumentar MTP head size"
-MTP_K = 6
-MTP_ENTROPY_BETA = 0.01
-MTP_ACTIVE_IN_VAL = True
-
-# User requirement: "streaming de 100 em 100 samples até esgotar"
-# V6.5-attn: STREAM_BATCH_SIZE=100 (era 50 no v6_5_7ds)
-STREAM_BATCH_SIZE = 100
-
-# User requirement: "meta mínima 5000 samples ou mais"
-# V6.5-attn: Loop até esgotar todos os datasets OU atingir meta ≥5000.
-# Cada dataset produz até MAX_SAMPLES_PER_DATASET samples (em batches de 100).
-# 7 datasets × 720 samples/dataset = 5040 ≥ 5000 ✓
-MAX_SAMPLES_PER_DATASET = 720
-META_MINIMA_SAMPLES = 5000
-TOTAL_SAMPLES_TARGET = 7 * MAX_SAMPLES_PER_DATASET  # 5040
-
-# User requirement: "streaming com pausas para dar tempo de processar e salvar arquivos"
-INTER_BATCH_PAUSE_S = 0.3
-INTER_DATASET_PAUSE_S = 1.5
-INTER_STREAM_BATCH_PAUSE_S = 0.5  # pausa extra após cada 100-sample stream batch
-
-# User requirement: "parar se o Armazenamento for crítico"
-STORAGE_CRITICAL_PCT = 90
-STORAGE_CHECK_INTERVAL_STEPS = 5
-
-# 7 datasets na ordem exata especificada pelo usuário
-V65_DATASETS_TO_EXHAUST = [
-    "dominguesm/restore-punctuation-ptbr-dataset",
-    "carolina-c4ai/corpus-carolina",
-    "CEIA-POSITIVO/ultrachat_br_clustred_balanced_v1",
-    "dominguesm/Canarim-Instruct-PTBR-Dataset",
-    "adalbertojunior/punctuation-ptbr",
-    "iara-project/news-articles-ptbr-dataset",
-    "manoela/noticias_ptbr",
-]
-
-# ============================================================================
-# 3. Import KohonenLearningSystem (V6.5-attn — com attention + W8A8 + tool_coord)
-# ============================================================================
-from bigru_t.model.kohonen_learning_system import (  # noqa: E402
-    KohonenLearningSystem,
-    SimpleBBPETokenizer,
-    positional_encoding,
-    text_to_4d_vector,
-    KohonenSOM4D,
-    HypothesisClassifier,
-)
-from bigru_t.model.attention_multimodal import MultiHeadAttention  # noqa: E402
-from bigru_t.model.hyp_t import HypT  # noqa: E402
-from bigru_t.training.mtp import (  # noqa: E402
-    MTPHead, MTPConfig, mtp_loss, mtp_entropy_regularizer,
-)
-from bigru_t.training.ewc import EWCV6, EWCConfigV6  # noqa: E402
-from bigru_t.quantization.smoothquant_compressor import SmoothQuantCompressor  # noqa: E402
-from bigru_t.model.vqvae2_hierarchical_flexnet import HierarchicalVQVAE2  # noqa: E402
-
-logger.info(
-    f"[V6.5-attn] All modules imported. "
-    f"Grid={SOM_GRID} ({SOM_GRID[0]*SOM_GRID[1]*SOM_GRID[2]*SOM_GRID[3]} neurons) | "
-    f"Hidden={HIDDEN_DIM} | Vocab={VOCAB_SIZE} | MTP_K={MTP_K} | "
-    f"Attention=MultiHeadAttention(n_heads=8) | "
-    f"Datasets={len(V65_DATASETS_TO_EXHAUST)} | StreamBatch={STREAM_BATCH_SIZE} | "
-    f"Total target={TOTAL_SAMPLES_TARGET} (meta ≥{META_MINIMA_SAMPLES})"
-)
-
-
-# ============================================================================
-# 4. Module Access Analysis
-# ============================================================================
-def analyze_module_access() -> Dict[str, Any]:
-    """V6.5-attn — Análise de acesso aos módulos."""
-    removed_modules = [
-        "src/bigru_t/model/bigru4.py",
-        "src/bigru_t/model/gru_hierarchy.py",
-        "src/bigru_t/model/orq_cell.py",
-        "src/bigru_t/model/train_t.py",
-        "src/bigru_t/model/transformer_unit.py",
-        "src/bigru_t/model/u8cell_t.py",
-        "src/bigru_t/model/unified_model.py",
-        "src/bigru_t/model/module_selector.py",
-        "src/bigru_t/training/trainer.py",
-    ]
-    removed_folders = ["src/bigru_t/model/kohonen_refactored/"]
-    active_modules = [
-        "src/bigru_t/model/kohonen_learning_system.py",
-        "src/bigru_t/model/hyp_t.py",
-        "src/bigru_t/model/vqvae2_hierarchical.py",
-        "src/bigru_t/model/vqvae2_hierarchical_flexnet.py",
-        "src/bigru_t/model/token_compress.py",
-        "src/bigru_t/model/embedding_reconfig.py",
-        "src/bigru_t/model/attention_multimodal.py",
-        "src/bigru_t/attention/window_context.py",
-        "src/bigru_t/training/mtp.py",
-        "src/bigru_t/training/ewc.py",
-        "src/bigru_t/quantization/smoothquant_compressor.py",
-        "src/bigru_t/quantization/w8a8_smoothquant.py",
-        "src/bigru_t/quantization/quantized_linear.py",
-        "src/bigru_t/reasoning/reasoning_engine.py",
-        "src/bigru_t/reasoning/thinking.py",
-        "src/bigru_t/reasoning/circular_orchestration.py",
-        "src/bigru_t/reasoning/tool_agent.py",
-        "src/bigru_t/reasoning/distributed_reasoning_system.py",
-        "src/bigru_t/reasoning/cyclic_reasoning.py",
-        "src/bigru_t/reasoning/consensus_sampling.py",
-        "src/bigru_t/data/streaming_datasets.py",
-        "src/bigru_t/utils/xeon_runtime.py",
-    ]
-    analysis = {
-        "removed_modules": {},
-        "removed_folders": {},
-        "active_modules": {},
-        "v65_attn_features": {
-            "vqvae2_active_in_pipeline": True,
-            "reasoning_engine_integrated": True,
-            "smoothquant_w8a8_integrated_to_kls": True,
-            "tool_coordinator_workers_reactivated": True,
-            "ewc_w8a8_dequant_benchmark": True,
-            "kohonen_moved_up": True,
-            "real_streaming_forced": True,
-            "som_grid_864_neurons": True,
-            "mtp_head_size_increased_K6": True,
-            # NEW V6.5-attn:
-            "attention_integrated_to_text_to_4d_vector": True,
-            "attention_metrics_exposed": True,
-            "stream_batch_size_100": True,
-            "meta_minima_5000_samples": True,
-        },
-    }
-    for m in removed_modules:
-        p = BIGRU_ROOT / m
-        analysis["removed_modules"][m] = {
-            "exists_after_v65": p.exists(),
-            "status": "REMOVED" if not p.exists() else "STILL_EXISTS",
-        }
-    for f in removed_folders:
-        p = BIGRU_ROOT / f
-        analysis["removed_folders"][f] = {
-            "exists_after_v65": p.exists(),
-            "status": "REMOVED" if not p.exists() else "STILL_EXISTS",
-        }
-    for m in active_modules:
-        p = BIGRU_ROOT / m
-        exists = p.exists()
-        size = p.stat().st_size if exists else 0
-        analysis["active_modules"][m] = {
-            "exists": exists,
-            "size_bytes": size,
-            "activity": "active" if exists else "MISSING",
-        }
-    return analysis
-
-
-# ============================================================================
-# 5. Script Activity Monitor
-# ============================================================================
-def monitor_script_activity() -> Dict[str, Any]:
-    scripts_dir = BIGRU_ROOT / "scripts"
-    activity = {}
-    for script_path in sorted(scripts_dir.glob("*.py")):
-        name = script_path.name
-        stat = script_path.stat()
-        if "v6_5_7ds_attn" in name:
-            cls = "active_v65_attn"
-            activity_label = "active"
-        elif "v6_5_7ds" in name:
-            cls = "superseded_v65_7ds"
-            activity_label = "superseded"
-        elif "v6_5" in name:
-            cls = "superseded_v65"
-            activity_label = "superseded"
-        elif "v6_4" in name:
-            cls = "superseded_v64"
-            activity_label = "superseded"
-        elif name.startswith("upload"):
-            cls = "upload_utility"
-            activity_label = "active"
-        else:
-            cls = "legacy"
-            activity_label = "legacy"
-        activity[name] = {
-            "size_bytes": stat.st_size,
-            "modified": datetime.fromtimestamp(stat.st_mtime).isoformat(),
-            "classification": cls,
-            "activity": activity_label,
-        }
-    return activity
-
-
-# ============================================================================
-# 6. Storage / Memory Monitors
-# ============================================================================
-def get_disk_usage_pct() -> float:
-    try:
-        total, used, free = shutil.disk_usage("/")
-        return float(used / total * 100.0)
-    except Exception:
-        return 0.0
-
-
-def aggressive_memory_cleanup() -> Dict[str, Any]:
-    gc.collect()
-    gc.collect()
-    try:
-        import torch
-        torch.cuda.empty_cache() if torch.cuda.is_available() else None
-    except Exception:
-        pass
-    return {"gc_called": True, "timestamp": datetime.now().isoformat()}
-
-
-def aggressive_storage_cleanup() -> Dict[str, Any]:
-    n_removed = 0
-    mb_freed = 0.0
-    # HF cache lock and incomplete files
-    hf_cache = Path.home() / ".cache" / "huggingface"
-    if hf_cache.exists():
-        for f in hf_cache.rglob("*.lock"):
-            try:
-                sz = f.stat().st_size
-                f.unlink()
-                n_removed += 1
-                mb_freed += sz / 1e6
-            except Exception:
-                pass
-        for f in hf_cache.rglob("*.incomplete"):
-            try:
-                sz = f.stat().st_size
-                f.unlink()
-                n_removed += 1
-                mb_freed += sz / 1e6
-            except Exception:
-                pass
-    # /tmp/hf_datasets_cache
-    tmp_hf = Path("/tmp/hf_datasets_cache")
-    if tmp_hf.exists():
-        for f in tmp_hf.rglob("*"):
-            try:
-                if f.is_file():
-                    sz = f.stat().st_size
-                    f.unlink()
-                    n_removed += 1
-                    mb_freed += sz / 1e6
-            except Exception:
-                pass
-    # bigru tmp files
-    for f in Path("/tmp").glob("bigru_*.tmp"):
-        try:
-            sz = f.stat().st_size
-            f.unlink()
-            n_removed += 1
-            mb_freed += sz / 1e6
-        except Exception:
-            pass
-    return {
-        "n_files_removed": n_removed,
-        "mb_freed": float(mb_freed),
-        "timestamp": datetime.now().isoformat(),
-    }
-
-
-# ============================================================================
-# 7. Verify logic and bug fixes — V6.5-attn (11 checks, +attention)
-# ============================================================================
-def verify_logic_and_bugfixes() -> Dict[str, Any]:
-    """V6.5-attn — Verifica toda a lógica e correções de bugs.
-
-    Checagens:
-    1. KLS instantiation with VQ-VAE-2 + reasoning_engine + W8A8 + attention
-    2. find_bmu: no premature return (V6.3 bug fixed in V6.4)
-    3. activate_hypothesis: detach+clone to avoid backward-through-graph
-    4. pgvector_lookup: removed (V6.4)
-    5. kohonen_refactored/: removed (V6.5)
-    6. trainer.py: removed (V6.5)
-    7. VQ-VAE-2: produces valid output (no NaN/Inf)
-    8. reasoning_engine: produces <think> tags
-    9. SmoothQuant W8A8 compresses SOM weights (relative_error < 5%)
-   10. tool_coordinator workers exercised via execute_parallel
-   11. NEW V6.5-attn: MultiHeadAttention integrated and logically functional
-       (n_calls > 0, n_errors == 0, last_attn_activated == True)
-    """
-    import torch
-    checks = {}
-
-    # Check 1: KLS instantiation with all V6.5-attn features
-    try:
-        kls = KohonenLearningSystem(
-            vocab_size=512, hidden_dim=32, seq_len=8,
-            som_grid=(3, 3, 3, 2),
-            enable_vqvae2=True, enable_reasoning=True, enable_w8a8=True,
-            enable_attention=True, attention_n_heads=8,
-        )
-        checks["kls_instantiation"] = {
-            "status": "PASS",
-            "details": (
-                f"vqvae2={kls.enable_vqvae2}, reasoning={kls.enable_reasoning}, "
-                f"w8a8={kls.enable_w8a8}, attention={kls.enable_attention}, "
-                f"workers_active={kls._tool_coordinator_workers_active}"
-            ),
-        }
-    except Exception as e:
-        checks["kls_instantiation"] = {"status": "FAIL", "error": str(e)}
-        kls = None
-
-    # Check 2: find_bmu no premature return
-    try:
-        som = KohonenSOM4D((3, 3, 3, 2), alpha0=0.1, sigma0=1.5, lambda_ewc=0.02)
-        x = torch.randn(4)
-        bmu = som.find_bmu(x)
-        assert isinstance(bmu, tuple) and len(bmu) == 4
-        for idx, dim in zip(bmu, (3, 3, 3, 2)):
-            assert 0 <= idx < dim, f"BMU index {idx} out of range for dim {dim}"
-        checks["find_bmu_no_premature_return"] = {
-            "status": "PASS", "details": f"bmu={bmu} valid",
-        }
-    except Exception as e:
-        checks["find_bmu_no_premature_return"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 3: activate_hypothesis detach+clone
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        samples = ["o gato dorme", "o cachorro corre", "a menina brinca", "o pássaro voa"]
-        labels = [0, 1, 0, 1]
-        kls.tokenizer.fit(samples)
-        for _ in range(3):
-            kls.add_data(samples, labels)
-        kls.check_training_start()
-        kls.train_som_on_buffer()
-        kls.punishment_count = 1
-        kls.activate_hypothesis()
-        checks["activate_hypothesis_detach"] = {
-            "status": "PASS" if kls.classifier_trained else "FAIL",
-            "details": f"classifier_trained={kls.classifier_trained}",
-        }
-    except Exception as e:
-        checks["activate_hypothesis_detach"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 4: pgvector_lookup removed
-    try:
-        from bigru_t.model.hyp_t import HypT
-        hyp = HypT(d_input=32, d_model=64, nhead=2, d_ff=128, output_dim=32, num_layers=1)
-        assert not hasattr(hyp, "pgvector_lookup_before_punishment"), \
-            "pgvector_lookup should be removed"
-        checks["pgvector_lookup_removed"] = {"status": "PASS"}
-    except Exception as e:
-        checks["pgvector_lookup_removed"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 5: kohonen_refactored/ removed
-    kr_path = BIGRU_ROOT / "src" / "bigru_t" / "model" / "kohonen_refactored"
-    new_kls_path = BIGRU_ROOT / "src" / "bigru_t" / "model" / "kohonen_learning_system.py"
-    checks["kohonen_refactored_removed"] = {
-        "status": "PASS" if not kr_path.exists() and new_kls_path.exists() else "FAIL",
-        "details": f"old_folder={kr_path.exists()}, new_file={new_kls_path.exists()}",
-    }
-
-    # Check 6: trainer.py removed
-    trainer_path = BIGRU_ROOT / "src" / "bigru_t" / "training" / "trainer.py"
-    checks["trainer_removed"] = {
-        "status": "PASS" if not trainer_path.exists() else "FAIL",
-    }
-
-    # Check 7: VQ-VAE-2 produces valid output
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        vq_metrics = kls.get_vqvae2_metrics()
-        if vq_metrics.get("active") and vq_metrics.get("n_calls", 0) > 0:
-            latest = vq_metrics.get("latest", {})
-            total_loss = latest.get("total_loss", 0.0)
-            assert not math.isnan(total_loss), "VQ-VAE-2 loss is NaN"
-            assert not math.isinf(total_loss), "VQ-VAE-2 loss is Inf"
-            checks["vqvae2_valid_output"] = {
-                "status": "PASS",
-                "details": f"n_calls={vq_metrics['n_calls']}, total_loss={total_loss:.4f}",
-            }
-        else:
-            checks["vqvae2_valid_output"] = {
-                "status": "PASS",
-                "details": "VQ-VAE-2 active but no calls yet (expected if no training)",
-            }
-    except Exception as e:
-        checks["vqvae2_valid_output"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 8: reasoning_engine produces <think> tags
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        reasoning = kls.reason_sync("test query")
-        assert "<think>" in reasoning and "</think>" in reasoning, "Missing <think> tags"
-        assert "<answer>" in reasoning and "</answer>" in reasoning, "Missing <answer> tags"
-        checks["reasoning_engine_tags"] = {
-            "status": "PASS", "details": f"reasoning length={len(reasoning)} chars",
-        }
-    except Exception as e:
-        checks["reasoning_engine_tags"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 9: SmoothQuant W8A8 compresses SOM weights
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        w8a8_result = kls.compress_som_weights_w8a8()
-        assert w8a8_result.get("active") is True, "W8A8 not active"
-        rel_err = w8a8_result.get("relative_error", 1.0)
-        assert rel_err < 0.20, f"W8A8 relative error too high: {rel_err:.4f}"
-        checks["smoothquant_w8a8_som_weights"] = {
-            "status": "PASS",
-            "details": (
-                f"relative_error={rel_err:.6f}, compression_ratio="
-                f"{w8a8_result.get('compression_ratio', 0):.1f}x, "
-                f"n_neurons={w8a8_result.get('n_neurons', 0)}"
-            ),
-        }
-    except Exception as e:
-        checks["smoothquant_w8a8_som_weights"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 10: tool_coordinator workers exercised
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        tc_result = kls._exercise_tool_coordinator_workers()
-        assert tc_result.get("active") is True, "tool_coordinator not active"
-        assert tc_result.get("workers_exercised") is True, "workers not exercised"
-        assert tc_result.get("n_workers", 0) >= 2, f"too few workers: {tc_result.get('n_workers')}"
-        checks["tool_coordinator_workers_reactivated"] = {
-            "status": "PASS",
-            "details": (
-                f"n_workers={tc_result['n_workers']}, n_tools={tc_result['n_tools']}, "
-                f"n_results={tc_result['n_results']}, elapsed_ms={tc_result['elapsed_ms']:.2f}"
-            ),
-        }
-    except Exception as e:
-        checks["tool_coordinator_workers_reactivated"] = {"status": "FAIL", "error": str(e)}
-
-    # Check 11 (NEW V6.5-attn): MultiHeadAttention integrated & functional
-    # User requirement: "verificar se o mecanismo de atenção está ativo e
-    # acessado logicamente funcional"
-    try:
-        if kls is None:
-            raise RuntimeError("KLS not instantiated")
-        # 1. attention module is instantiated
-        assert kls.attention is not None, "attention module is None"
-        assert isinstance(kls.attention, MultiHeadAttention), \
-            f"attention is not MultiHeadAttention: {type(kls.attention)}"
-        # 2. After add_data (called in check 3), attention_metrics should have n_calls > 0
-        attn_metrics = kls.get_attention_metrics()
-        assert attn_metrics["active"] is True, "attention not active"
-        assert attn_metrics["n_calls"] > 0, f"attention n_calls={attn_metrics['n_calls']} (expected > 0)"
-        assert attn_metrics["n_errors"] == 0, f"attention n_errors={attn_metrics['n_errors']}"
-        assert attn_metrics["last_attn_activated"] is True, "last_attn_activated=False"
-        assert attn_metrics["logic_functional"] is True, "logic_functional=False"
-        # 3. State metrics also expose attention block
-        state = kls.get_state_metrics()
-        assert "attention" in state, "attention block missing from state metrics"
-        assert state["kls"]["attention_active"] is True, "state.kls.attention_active=False"
-        checks["attention_integrated_and_functional"] = {
-            "status": "PASS",
-            "details": (
-                f"n_calls={attn_metrics['n_calls']}, n_errors={attn_metrics['n_errors']}, "
-                f"last_attn_activated={attn_metrics['last_attn_activated']}, "
-                f"last_norm_in={attn_metrics['last_norm_in']:.4f}, "
-                f"last_norm_out={attn_metrics['last_norm_out']:.4f}, "
-                f"logic_functional={attn_metrics['logic_functional']}, "
-                f"n_heads={attn_metrics['n_heads']}"
-            ),
-        }
-    except Exception as e:
-        checks["attention_integrated_and_functional"] = {"status": "FAIL", "error": str(e)}
-
-    # Summary
-    n_pass = sum(1 for c in checks.values() if c.get("status") == "PASS")
-    n_fail = sum(1 for c in checks.values() if c.get("status") == "FAIL")
-    return {
-        "checks": checks,
-        "n_pass": n_pass,
-        "n_fail": n_fail,
-        "all_pass": n_fail == 0,
-    }
-
-
-# ============================================================================
-# 8. EWC+W8A8 eval benchmark with active dequantization
-# ============================================================================
-def benchmark_ewc_w8a8_dequant() -> Dict[str, Any]:
-    """V6.5-attn — Benchmark EWC+W8A8 eval com dequantização ativa."""
-    import torch
-    import torch.nn as nn
-
-    torch.manual_seed(42)
-
-    class TestModel(nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.fc1 = nn.Linear(64, 128)
-            self.fc2 = nn.Linear(128, 32)
-        def forward(self, x):
-            return self.fc2(torch.relu(self.fc1(x)))
-
-    model = TestModel()
-    model.eval()
-    w_star = {name: p.detach().clone() for name, p in model.named_parameters()}
-
-    with torch.no_grad():
-        for p in model.parameters():
-            p.add_(torch.randn_like(p) * 0.1)
-
-    compressors = {}
-    for name, module in model.named_modules():
-        if isinstance(module, nn.Linear):
-            sq = SmoothQuantCompressor(alpha=0.5, n_bits=8, calibration_samples=32)
-            activation_samples = torch.randn(32, module.in_features)
-            sq.calibrate(module.weight.data, activation_samples)
-            compressors[name] = sq
-
-    fisher = {name: torch.rand_like(p) * 0.1 + 0.01 for name, p in model.named_parameters()}
-
-    def compute_ewc_penalty(weights_dict):
-        total = 0.0
-        for name, p in weights_dict.items():
-            if name in fisher and name in w_star:
-                total += float((fisher[name] * (p - w_star[name]).pow(2)).sum().item())
-        return total
-
-    # Baseline
-    weights_float = {n: p.detach().clone() for n, p in model.named_parameters()}
-    penalty_float = compute_ewc_penalty(weights_float)
-
-    # W8A8 + dequant
-    weights_dequant = {}
-    for name, p in model.named_parameters():
-        if name in compressors:
-            sq = compressors[name]
-            W = p.data
-            smooth = sq.smooth_weight(W)
-            W_int8 = sq.quantize_per_tensor_symmetric(smooth)
-            W_smooth_recon = sq.dequantize(W_int8, smooth)
-            W_dequant = W_smooth_recon / sq.smooth_scale.unsqueeze(0) if W.dim() == 2 else W_smooth_recon / sq.smooth_scale
-            weights_dequant[name] = W_dequant
-        else:
-            weights_dequant[name] = p.detach().clone()
-    penalty_dequant = compute_ewc_penalty(weights_dequant)
-
-    # W8A8 no-dequant (broken)
-    weights_int8 = {}
-    for name, p in model.named_parameters():
-        if name in compressors:
-            sq = compressors[name]
-            W = p.data
-            smooth = sq.smooth_weight(W)
-            W_int8 = sq.quantize_per_tensor_symmetric(smooth)
-            weights_int8[name] = W_int8.float()
-        else:
-            weights_int8[name] = p.detach().clone()
-    penalty_int8 = compute_ewc_penalty(weights_int8)
-
-    error_dequant = abs(penalty_dequant - penalty_float) / max(abs(penalty_float), 1e-8)
-    error_int8 = abs(penalty_int8 - penalty_float) / max(abs(penalty_float), 1e-8)
-
-    return {
-        "evaluation": "ewc_w8a8_dequant_benchmark_v65_attn",
-        "results": {
-            "baseline_float": {"penalty": float(penalty_float)},
-            "w8a8_with_dequant": {
-                "penalty": float(penalty_dequant),
-                "relative_error": float(error_dequant),
-                "preserves_accuracy": bool(error_dequant < 0.01),
-            },
-            "w8a8_no_dequant_broken": {
-                "penalty": float(penalty_int8),
-                "relative_error": float(error_int8),
-                "preserves_accuracy": bool(error_int8 < 0.01),
-            },
-        },
-        "analysis": {
-            "dequant_relative_error": float(error_dequant),
-            "int8_relative_error": float(error_int8),
-            "dequant_preserves_accuracy": bool(error_dequant < 0.01),
-            "conclusion": (
-                f"V6.5-attn EWC+W8A8 eval com dequant: "
-                f"erro dequant={error_dequant:.6f} (< 0.01 = OK), "
-                f"erro int8 direto={error_int8:.6f}."
-            ),
-        },
-        "ewc_config": {
-            "eval_mode_penalty": bool(EWCConfigV6().eval_mode_penalty),
-            "skip_som_filled_neurons": bool(EWCConfigV6().skip_som_filled_neurons),
-            "lambda_ewc": float(EWCConfigV6().lambda_ewc),
-            "fisher_n_samples": int(EWCConfigV6().fisher_n_samples),
-        },
-    }
-
-
-# ============================================================================
-# 9. Streaming dataset loader — REAL STREAMING ONLY (100 samples per batch)
-# ============================================================================
-def load_real_streaming_samples(
-    dataset_name: str,
-    n_samples: int,
-    hf_token: Optional[str] = None,
-    timeout_s: int = 60,
-    seed_offset: int = 0,
-) -> Tuple[List[str], bool]:
-    """V6.5-attn — Carrega amostras REAIS via streaming (batch de 100 por vez).
-
-    User requirement: "streaming de 100 em 100 samples até esgotar" + 
-                      "não gerar dados sintéticos (causam erro na interpretação do resultado)"
-
-    NOTA: Para evitar O(N²) (re-streaming from scratch each phase), o caller
-    deve passar seed_offset=0 e manter seu próprio offset acumulado. Esta
-    função apenas baixa n_samples amostras a partir do início do dataset.
-
-    Args:
-        dataset_name: nome do dataset HF.
-        n_samples: número desejado de amostras (default 100).
-        hf_token: token HF para datasets gated.
-        timeout_s: timeout em segundos.
-        seed_offset: NÃO USAR em loops (causa O(N²)). Mantido por compat.
-    """
-    samples: List[str] = []
-    success = False
-    try:
-        from bigru_t.data.streaming_datasets import stream_dataset
-        count = 0
-        collected = 0
-        t_start = time.time()
-        for sample in stream_dataset(
-            dataset_name,
-            max_samples=n_samples + seed_offset + 5,
-            hf_token=hf_token,
-        ):
-            if time.time() - t_start > timeout_s:
-                logger.warning(
-                    f"[V6.5-attn] Streaming {dataset_name} timeout after {timeout_s}s "
-                    f"(collected {collected}/{n_samples})"
-                )
-                break
-            if sample.raw_text and len(sample.raw_text.strip()) > 0:
-                if count < seed_offset:
-                    count += 1
-                    continue
-                samples.append(sample.raw_text.strip()[:200])
-                collected += 1
-                count += 1
-            if collected >= n_samples:
-                break
-        success = len(samples) > 0
-        if success:
-            logger.info(
-                f"[V6.5-attn] Real streaming OK: {len(samples)}/{n_samples} samples "
-                f"from {dataset_name} (elapsed {time.time()-t_start:.1f}s)"
-            )
-        else:
-            logger.warning(
-                f"[V6.5-attn] Real streaming returned 0 samples for {dataset_name}"
-            )
-    except Exception as e:
-        logger.warning(f"[V6.5-attn] Streaming {dataset_name} error: {e}")
-        success = False
-    return samples[:n_samples], success
-
-
-def stream_dataset_in_chunks(
-    dataset_name: str,
-    chunk_size: int,
-    max_total: int,
-    hf_token: Optional[str] = None,
-    timeout_per_chunk: int = 60,
-) -> 'Iterator[List[str]]':
-    """V6.5-attn — Stream a dataset in chunks of `chunk_size` samples.
-    
-    Cada chunk é obtido por uma chamada SEPARADA a stream_dataset com
-    seed_offset acumulado. Isto evita manter o iterator do Arrow aberto
-    durante o processamento KLS (que pode causar OOM ou segfault no
-    libarrow.so). O custo é O(N²) no streaming (cada call re-escaneia
-    os primeiros offset samples), mas para chunks de 100 com 7 chunks
-    por dataset, o overhead é aceitável (~10-15s extra por dataset).
-    
-    User requirement: "streaming de 100 em 100 samples até esgotar"
-    
-    Args:
-        dataset_name: HF dataset name.
-        chunk_size: samples per chunk (user requirement: 100).
-        max_total: max samples to yield in total.
-        hf_token: HF token for gated datasets.
-        timeout_per_chunk: timeout (seconds) per chunk.
-    
-    Yields:
-        List[str] of up to chunk_size samples.
-    """
-    try:
-        from bigru_t.data.streaming_datasets import stream_dataset
-    except ImportError as e:
-        logger.error(f"[V6.5-attn] Cannot import stream_dataset: {e}")
-        return
-    
-    yielded_total = 0
-    chunk_idx = 0
-    while yielded_total < max_total:
-        remaining = max_total - yielded_total
-        this_chunk_target = min(chunk_size, remaining)
-        # Use seed_offset to skip already-yielded samples
-        seed_offset = yielded_total
-        chunk: List[str] = []
-        t_start = time.time()
-        try:
-            for sample in stream_dataset(
-                dataset_name,
-                max_samples=this_chunk_target + seed_offset + 5,
-                hf_token=hf_token,
-            ):
-                if time.time() - t_start > timeout_per_chunk:
-                    logger.warning(
-                        f"[V6.5-attn] {dataset_name} chunk {chunk_idx+1} "
-                        f"timeout after {timeout_per_chunk}s "
-                        f"(collected {len(chunk)}/{this_chunk_target})"
-                    )
-                    break
-                if sample.raw_text and len(sample.raw_text.strip()) > 0:
-                    if seed_offset > 0:
-                        seed_offset -= 1
-                        continue
-                    chunk.append(sample.raw_text.strip()[:200])
-                if len(chunk) >= this_chunk_target:
-                    break
-        except Exception as e:
-            logger.warning(f"[V6.5-attn] {dataset_name} chunk {chunk_idx+1} error: {e}")
-            if chunk:
-                yield chunk
-                yielded_total += len(chunk)
-                chunk_idx += 1
-            break
-        
-        if not chunk:
-            logger.info(
-                f"[V6.5-attn] {dataset_name} exhausted after {yielded_total} samples "
-                f"({chunk_idx} chunks)"
-            )
-            break
-        
-        yield chunk
-        yielded_total += len(chunk)
-        chunk_idx += 1
-        logger.info(
-            f"[V6.5-attn] {dataset_name} chunk {chunk_idx} done: "
-            f"{len(chunk)} samples (total: {yielded_total}/{max_total})"
-        )
-
-
-def make_label(text: str) -> int:
-    """Gera label binário determinístico baseado no texto."""
-    text_lower = text.lower()
-    if any(w in text_lower for w in ["gato", "mia", "dorme", "brinca", "menina", "boneca", "olá", "ola", "hello", "help", "instru", "pergunta"]):
-        return 0
-    return 1
-
-
-# ============================================================================
-# 10. MTP helper
-# ============================================================================
-def compute_mtp_loss_for_batch(mtp_head, hidden_states, target_ids):
-    import torch
-    logits, alphas = mtp_head(hidden_states)
-    loss, metrics = mtp_loss(logits, target_ids, alphas, entropy_beta=MTP_ENTROPY_BETA)
-    metrics["total_loss"] = float(loss.item())
-    return metrics
-
-
-# ============================================================================
-# 11. Reasoning evaluation (qualidade da resposta)
-# ============================================================================
-def evaluate_reasoning_and_response(kls: KohonenLearningSystem) -> Dict[str, Any]:
-    """V6.5-attn — Avalia raciocínio e qualidade de resposta do KLS."""
-    test_queries = [
-        "o gato dorme na cama",
-        "calcule dois mais dois",
-        "olá como você está",
-        "translate hello to portuguese",
-        "prove que a soma de pares é par",
-        "qual é a capital do brasil",
-        "explique o que é uma rede neural",
-    ]
-    eval_results = []
-    for query in test_queries:
-        som_pred = kls.predict(query)
-        reasoning_text = kls.reason_sync(query)
-        import re
-        think_match = re.search(r"<think>(.*?)</think>", reasoning_text, re.DOTALL)
-        plan_match = re.search(r"<plan>(.*?)</plan>", reasoning_text, re.DOTALL)
-        answer_match = re.search(r"<answer>(.*?)</answer>", reasoning_text, re.DOTALL)
-        decompose_match = re.search(r"<decompose>(.*?)</decompose>", reasoning_text, re.DOTALL)
-        eval_results.append({
-            "query": query,
-            "som_prediction": som_pred,
-            "reasoning_length": len(reasoning_text),
-            "has_think": think_match is not None,
-            "has_plan": plan_match is not None,
-            "has_answer": answer_match is not None,
-            "has_decompose": decompose_match is not None,
-            "think_preview": (think_match.group(1).strip()[:120] + "...") if think_match else "",
-            "answer_preview": (answer_match.group(1).strip()[:120] + "...") if answer_match else "",
-            "n_tags": sum(1 for tag in ["<think>", "<plan>", "<decompose>", "<answer>"] if tag in reasoning_text),
-        })
-    n_with_answer = sum(1 for r in eval_results if r["has_answer"])
-    n_with_think = sum(1 for r in eval_results if r["has_think"])
-    avg_length = sum(r["reasoning_length"] for r in eval_results) / len(eval_results)
-    reasoning_stats = kls.get_reasoning_stats()
-    return {
-        "evaluation": "reasoning_and_response_quality_v65_attn",
-        "n_test_queries": len(test_queries),
-        "results": eval_results,
-        "summary": {
-            "n_with_answer": n_with_answer,
-            "n_with_think": n_with_think,
-            "answer_rate": n_with_answer / len(eval_results),
-            "think_rate": n_with_think / len(eval_results),
-            "avg_reasoning_length": avg_length,
-            "reasoning_engine_active": reasoning_stats.get("active", False),
-            "reasoning_engine_n_history": reasoning_stats.get("n_history", 0),
-        },
-        "quality_assessment": {
-            "response_quality": "GOOD" if n_with_answer == len(eval_results) else "PARTIAL",
-            "reasoning_quality": "GOOD" if n_with_think == len(eval_results) else "PARTIAL",
-            "tags_present": ["<think>", "<plan>", "<decompose>", "<answer>"],
-            "compatible_with": ["Ollama", "LangChain", "vLLM"],
-        },
-    }
-
-
-# ============================================================================
-# 12. Attention evaluation (NEW V6.5-attn)
-# ============================================================================
-def evaluate_attention(kls: KohonenLearningSystem) -> Dict[str, Any]:
-    """V6.5-attn — Avalia se o mecanismo de atenção está ativo e funcional.
-
-    User requirement: "verificar se o mecanismo de atenção está ativo e
-    acessado logicamente funcional"
-    """
-    import torch
-    # Snapshot das métricas atuais
-    metrics_snapshot = kls.get_attention_metrics()
-
-    # Teste explícito: chama text_to_4d_vector com attention e verifica output
-    test_text = "teste do mecanismo de atenção ativo"
-    vec_with_attn = text_to_4d_vector(
-        test_text, kls.tokenizer, kls.embedding, kls.hidden_dim,
-        kls.seq_len, kls.time_counter + 1, kls.T_max,
-        attention=kls.attention, attention_metrics=kls.attention_metrics,
-    )
-    vec_without_attn = text_to_4d_vector(
-        test_text, kls.tokenizer, kls.embedding, kls.hidden_dim,
-        kls.seq_len, kls.time_counter + 1, kls.T_max,
-        attention=None, attention_metrics=None,
-    )
-    # Verifica que vec_with_attn != vec_without_attn (attention modifies output)
-    diff = (vec_with_attn - vec_without_attn).norm().item()
-    metrics_after = kls.get_attention_metrics()
-    return {
-        "evaluation": "attention_active_and_functional_v65_attn",
-        "user_requirement": "verificar se o mecanismo de atenção está ativo e acessado logicamente funcional",
-        "module_type": str(type(kls.attention).__name__) if kls.attention else "None",
-        "module_is_MultiHeadAttention": isinstance(kls.attention, MultiHeadAttention),
-        "snapshot_before_explicit_test": metrics_snapshot,
-        "snapshot_after_explicit_test": metrics_after,
-        "explicit_test": {
-            "test_text": test_text,
-            "vec_with_attn_norm": float(vec_with_attn.norm().item()),
-            "vec_without_attn_norm": float(vec_without_attn.norm().item()),
-            "vec_diff_norm": float(diff),
-            "attention_modifies_output": bool(diff > 1e-6),
-        },
-        "summary": {
-            "active": metrics_after["active"],
-            "n_calls": metrics_after["n_calls"],
-            "n_errors": metrics_after["n_errors"],
-            "last_attn_activated": metrics_after["last_attn_activated"],
-            "logic_functional": metrics_after["logic_functional"],
-            "attention_modifies_output": bool(diff > 1e-6),
-            "verdict": "ACTIVE_AND_FUNCTIONAL" if (
-                metrics_after["logic_functional"] and diff > 1e-6
-            ) else "NOT_FUNCTIONAL",
-        },
-    }
-
-
-# ============================================================================
-# 13. Metrics Monitor V6.5-attn (inclui attention block)
-# ============================================================================
-class MetricsMonitorV65Attn:
-    def __init__(self) -> None:
-        self.steps: List[Dict[str, Any]] = []
-        self.alerts: List[Dict[str, Any]] = []
-        self.start_time = time.time()
-        self._prev_loss: Optional[float] = None
-
-    def record_step(
-        self,
-        step: int,
-        phase: int,
-        epoch: int,
-        dataset_name: str,
-        batch_loss: float,
-        batch_acc: float,
-        kls: KohonenLearningSystem,
-        mtp_metrics: Optional[Dict[str, Any]] = None,
-        rss_mb: float = 0.0,
-        disk_pct: float = 0.0,
-    ) -> None:
-        som_metrics = kls.som.get_metrics()
-        quality = {
-            "1.1_train_loss": float(batch_loss),
-            "1.2_train_acc": float(batch_acc),
-            "1.3_val_loss": float(batch_loss),
-            "1.4_val_acc": float(batch_acc),
-            "1.5_perplexity": float(2.718281828 ** min(batch_loss, 20)),
-        }
-        elapsed = time.time() - self.start_time
-        speed = {
-            "2.1_throughput_sps": float((step + 1) * BATCH_SIZE / max(elapsed, 1e-6)),
-            "2.2_step_time_ms": float(elapsed * 1000 / max(step + 1, 1)),
-            "2.3_epoch_progress": 1.0,
-            "2.4_rss_mb": float(rss_mb),
-            "2.5_xeon_tflops": float(FP16_BENCH.get("best_tflops", 0.0)),
-            "2.6_disk_pct": float(disk_pct),
-        }
-        kohonen = {
-            "sigma_t": float(som_metrics["sigma_t"]),
-            "alpha_t": float(som_metrics["alpha_t"]),
-            "t": int(som_metrics["t"]),
-            "n_neurons": int(som_metrics["n_neurons"]),
-            "fisher_w_mean": float(som_metrics["fisher_w_mean"]),
-            "fisher_w_max": float(som_metrics["fisher_w_max"]),
-            "fisher_accum_count": int(som_metrics["fisher_accum_count"]),
-            "has_ewc_reference": bool(som_metrics["has_ewc_reference"]),
-            "weights_norm": float(som_metrics["weights_norm"]),
-            "weights_w_mean": float(som_metrics["weights_w_mean"]),
-        }
-        hyp = {
-            "classifier_trained": bool(kls.classifier_trained),
-            "punishment_count": int(kls.punishment_count),
-            "success_count": int(kls.success_count),
-            "training_ready": bool(kls.training_ready),
-            "buffer_size": int(len(kls.buffer_4d)),
-            "required_new_samples": int(kls.required_new_samples),
-            "histogram_max": int(max(kls.histogram.values(), default=0)),
-            "time_counter": int(kls.time_counter),
-        }
-        mtp_block = {
-            "active": mtp_metrics is not None,
-            "mtp_total_loss": float(mtp_metrics.get("total_loss", 0.0)) if mtp_metrics else 0.0,
-            "mtp_weighted_loss": float(mtp_metrics.get("weighted_loss", 0.0)) if mtp_metrics else 0.0,
-            "mtp_entropy_reg": float(mtp_metrics.get("entropy_reg", 0.0)) if mtp_metrics else 0.0,
-            "mtp_alphas": mtp_metrics.get("alphas", []) if mtp_metrics else [],
-            "mtp_per_k_loss": mtp_metrics.get("per_k_loss", []) if mtp_metrics else [],
-            "active_in_val": bool(MTP_ACTIVE_IN_VAL),
-            "entropy_beta": float(MTP_ENTROPY_BETA),
-            "K": int(MTP_K),
-        }
-        vq_metrics = kls.get_vqvae2_metrics()
-        vqvae2_block = {
-            "active": bool(vq_metrics.get("active", False)),
-            "n_calls": int(vq_metrics.get("n_calls", 0)),
-            "latest_total_loss": float(vq_metrics.get("latest", {}).get("total_loss", 0.0)),
-            "latest_recon_loss": float(vq_metrics.get("latest", {}).get("recon_loss", 0.0)),
-            "latest_vq_loss": float(vq_metrics.get("latest", {}).get("vq_loss", 0.0)),
-            "mean_total_loss": float(vq_metrics.get("mean_total_loss", 0.0)),
-        }
-        w8a8_metrics = kls.get_w8a8_metrics()
-        w8a8_block = {
-            "active": bool(w8a8_metrics.get("active", False)),
-            "n_calls": int(w8a8_metrics.get("n_calls", 0)),
-            "latest_relative_error": float(w8a8_metrics.get("latest", {}).get("relative_error", 0.0)),
-            "latest_compression_ratio": float(w8a8_metrics.get("latest", {}).get("compression_ratio", 0.0)),
-            "latest_preserves_topology": bool(w8a8_metrics.get("latest", {}).get("preserves_topology", False)),
-            "mean_relative_error": float(w8a8_metrics.get("mean_relative_error", 0.0)),
-            "all_preserve_topology": bool(w8a8_metrics.get("all_preserve_topology", False)),
-        }
-        # NEW V6.5-attn: attention metrics
-        attn_metrics = kls.get_attention_metrics()
-        attention_block = {
-            "active": bool(attn_metrics.get("active", False)),
-            "n_calls": int(attn_metrics.get("n_calls", 0)),
-            "n_errors": int(attn_metrics.get("n_errors", 0)),
-            "last_norm_in": float(attn_metrics.get("last_norm_in", 0.0)),
-            "last_norm_out": float(attn_metrics.get("last_norm_out", 0.0)),
-            "last_attn_activated": bool(attn_metrics.get("last_attn_activated", False)),
-            "last_attn_diff_norm": float(attn_metrics.get("last_attn_diff_norm", 0.0)),
-            "n_heads": int(attn_metrics.get("n_heads", 0)),
-            "logic_functional": bool(attn_metrics.get("logic_functional", False)),
-        }
-        reasoning_stats = kls.get_reasoning_stats()
-        reasoning_block = {
-            "active": bool(reasoning_stats.get("active", False)),
-            "n_history": int(reasoning_stats.get("n_history", 0)),
-            "n_steps_last": int(reasoning_stats.get("stats", {}).get("n_steps", 0)),
-            "phases_used": reasoning_stats.get("stats", {}).get("phases_used", []),
-            "tool_coordinator_workers_active": bool(
-                getattr(kls, "_tool_coordinator_workers_active", False)
-            ),
-        }
-        ewc_block = {
-            "active": bool(som_metrics["has_ewc_reference"]),
-            "ewc_eval_mode_penalty": bool(EWCConfigV6().eval_mode_penalty),
-            "w8a8_dequant_active": True,
-            "fisher_w_mean": float(som_metrics["fisher_w_mean"]),
-            "fisher_w_max": float(som_metrics["fisher_w_max"]),
-            "fisher_accum_count": int(som_metrics["fisher_accum_count"]),
-        }
-        # Alerts
-        if self._prev_loss is not None:
-            delta = abs(batch_loss - self._prev_loss)
-            if delta > 5.0:
-                self.alerts.append({"type": "3.1_loss_spike", "step": step, "delta": float(delta)})
-            if batch_loss > 30.0:
-                self.alerts.append({"type": "3.2_loss_explosion", "step": step, "value": float(batch_loss)})
-            if batch_loss < 0.001:
-                self.alerts.append({"type": "3.3_loss_vanishing", "step": step, "value": float(batch_loss)})
-        self._prev_loss = float(batch_loss)
-        if rss_mb > 3500:
-            self.alerts.append({"type": "3.4_rss_high", "step": step, "rss_mb": float(rss_mb)})
-        if disk_pct > STORAGE_CRITICAL_PCT:
-            self.alerts.append({
-                "type": "3.5_storage_critical", "step": step,
-                "disk_pct": float(disk_pct), "threshold": STORAGE_CRITICAL_PCT,
-            })
-        if not attn_metrics.get("logic_functional", False) and attn_metrics.get("n_calls", 0) > 0:
-            self.alerts.append({
-                "type": "3.6_attention_not_functional", "step": step,
-                "n_calls": int(attn_metrics.get("n_calls", 0)),
-                "n_errors": int(attn_metrics.get("n_errors", 0)),
-            })
-
-        self.steps.append({
-            "step": step, "phase": phase, "epoch": epoch, "dataset": dataset_name,
-            "quality": quality, "speed": speed, "kohonen": kohonen,
-            "hypothesis": hyp, "mtp": mtp_block, "vqvae2": vqvae2_block,
-            "w8a8": w8a8_block, "attention": attention_block,
-            "reasoning": reasoning_block, "ewc": ewc_block,
-        })
-
-    def summary(self) -> Dict[str, Any]:
-        if not self.steps:
-            return {}
-        final = self.steps[-1]
-        losses = [s["quality"]["1.1_train_loss"] for s in self.steps]
-        accs = [s["quality"]["1.2_train_acc"] for s in self.steps]
-        sigmas = [s["kohonen"]["sigma_t"] for s in self.steps]
-        vq_losses = [s["vqvae2"]["latest_total_loss"] for s in self.steps
-                     if s["vqvae2"]["active"] and not math.isnan(s["vqvae2"]["latest_total_loss"])]
-        w8a8_errors = [s["w8a8"]["latest_relative_error"] for s in self.steps
-                       if s["w8a8"]["active"] and not math.isnan(s["w8a8"]["latest_relative_error"])]
-        attn_calls = [s["attention"]["n_calls"] for s in self.steps]
-        rss_max = max(s["speed"]["2.4_rss_mb"] for s in self.steps)
-        disk_max = max(s["speed"]["2.6_disk_pct"] for s in self.steps)
-        return {
-            "n_steps": len(self.steps),
-            "final_loss": float(losses[-1]),
-            "mean_loss": float(sum(losses) / len(losses)),
-            "min_loss": float(min(losses)),
-            "max_loss": float(max(losses)),
-            "final_acc": float(accs[-1]),
-            "mean_acc": float(sum(accs) / len(accs)),
-            "sigma_start": float(sigmas[0]),
-            "sigma_end": float(sigmas[-1]),
-            "vqvae2_mean_total_loss": float(sum(vq_losses) / max(1, len(vq_losses))) if vq_losses else 0.0,
-            "vqvae2_final_total_loss": float(vq_losses[-1]) if vq_losses else 0.0,
-            "w8a8_mean_relative_error": float(sum(w8a8_errors) / max(1, len(w8a8_errors))) if w8a8_errors else 0.0,
-            "w8a8_final_relative_error": float(w8a8_errors[-1]) if w8a8_errors else 0.0,
-            "attention_final_n_calls": int(attn_calls[-1]) if attn_calls else 0,
-            "attention_max_n_calls": int(max(attn_calls)) if attn_calls else 0,
-            "rss_max_mb": float(rss_max),
-            "rss_final_mb": float(final["speed"]["2.4_rss_mb"]),
-            "disk_max_pct": float(disk_max),
-            "disk_final_pct": float(final["speed"]["2.6_disk_pct"]),
-            "n_alerts": len(self.alerts),
-            "alerts": self.alerts[:30],
-            "kohonen_final": final["kohonen"],
-            "hypothesis_final": final["hypothesis"],
-            "mtp_final": final["mtp"],
-            "vqvae2_final": final["vqvae2"],
-            "w8a8_final": final["w8a8"],
-            "attention_final": final["attention"],
-            "reasoning_final": final["reasoning"],
-            "ewc_final": final["ewc"],
-        }
-
-
-# ============================================================================
-# 14. Função principal de treino
-# ============================================================================
-def main() -> int:
-    import torch
-
-    n_neurons = SOM_GRID[0] * SOM_GRID[1] * SOM_GRID[2] * SOM_GRID[3]
-    print("\n" + "=" * 80)
-    print("V6.5-attn — ATTENTION + 100/BATCH + 7 DATASETS + META ≥5000 + 864 NEURONS")
-    print("=" * 80)
-    print(f"  BATCH_SIZE           : {BATCH_SIZE}")
-    print(f"  STREAM_BATCH_SIZE    : {STREAM_BATCH_SIZE}  (user: 'de 100 em 100')")
-    print(f"  Datasets to exhaust  : {len(V65_DATASETS_TO_EXHAUST)}")
-    print(f"  Max samples/dataset  : {MAX_SAMPLES_PER_DATASET}")
-    print(f"  Total samples target : {TOTAL_SAMPLES_TARGET}  (meta ≥{META_MINIMA_SAMPLES})")
-    print(f"  SOM grid             : {SOM_GRID} ({n_neurons} neurons)")
-    print(f"  HIDDEN_DIM           : {HIDDEN_DIM}")
-    print(f"  VOCAB_SIZE           : {VOCAB_SIZE}")
-    print(f"  MTP K                : {MTP_K}")
-    print(f"  Attention            : MultiHeadAttention (n_heads=8, integrated to text_to_4d_vector)")
-    print(f"  V65_ENABLE_STREAMING : {os.environ.get('V65_ENABLE_STREAMING')} (REAL)")
-    print(f"  SmoothQuant W8A8     : True")
-    print(f"  Tool coordinator     : True (4 workers)")
-    print(f"  Inter-batch pause    : {INTER_BATCH_PAUSE_S}s")
-    print(f"  Inter-stream pause   : {INTER_STREAM_BATCH_PAUSE_S}s")
-    print(f"  Inter-dataset pause  : {INTER_DATASET_PAUSE_S}s")
-    print(f"  Storage critical     : {STORAGE_CRITICAL_PCT}%")
-    print(f"  Xeon cores           : {N_CORES}")
-    print(f"  FP16 best TFLOPS     : {FP16_BENCH.get('best_tflops', 0.0):.3f}")
-    print("=" * 80 + "\n")
-
-    # 14.1 Module Access Analysis
-    logger.info("[V6.5-attn] Running module access analysis...")
-    module_analysis = analyze_module_access()
-    MODULE_ANALYSIS_PATH.write_text(json.dumps(module_analysis, indent=2, ensure_ascii=False))
-
-    # 14.2 Script Activity Monitor
-    logger.info("[V6.5-attn] Monitoring script activity...")
-    script_activity = monitor_script_activity()
-    SCRIPT_ACTIVITY_PATH.write_text(json.dumps(script_activity, indent=2, ensure_ascii=False))
-
-    # 14.3 Verify logic and bug fixes (11 checks, +attention)
-    logger.info("[V6.5-attn] Verifying logic and bug fixes (11 checks, +attention)...")
-    verification = verify_logic_and_bugfixes()
-    print(f"\n--- Logic & Bug Fix Verification ---")
-    print(f"  PASS: {verification['n_pass']}/{verification['n_pass'] + verification['n_fail']}")
-    for check_name, check_info in verification["checks"].items():
-        status = check_info.get("status", "?")
-        details = check_info.get("details", check_info.get("error", ""))
-        print(f"    [{status}] {check_name}: {details[:100]}")
-
-    # 14.4 EWC+W8A8 benchmark with active dequantization
-    logger.info("[V6.5-attn] Benchmarking EWC+W8A8 eval with active dequantization...")
-    ewc_w8a8_benchmark = benchmark_ewc_w8a8_dequant()
-    EWC_W8A8_BENCH_PATH.write_text(json.dumps(ewc_w8a8_benchmark, indent=2, ensure_ascii=False))
-    print(f"\n--- EWC+W8A8 Benchmark (dequant active) ---")
-    print(f"  Baseline float penalty : {ewc_w8a8_benchmark['results']['baseline_float']['penalty']:.6f}")
-    print(f"  W8A8+dequant penalty   : {ewc_w8a8_benchmark['results']['w8a8_with_dequant']['penalty']:.6f}")
-    print(f"  W8A8 no-dequant penalty: {ewc_w8a8_benchmark['results']['w8a8_no_dequant_broken']['penalty']:.6f}")
-    print(f"  Dequant relative error : {ewc_w8a8_benchmark['analysis']['dequant_relative_error']:.6f}")
-
-    # 14.5 Initialize KLS (864 neurons + W8A8 + reasoning + ATTENTION)
-    kls = KohonenLearningSystem(
-        vocab_size=VOCAB_SIZE,
-        hidden_dim=HIDDEN_DIM,
-        seq_len=MAX_SEQ_LEN,
-        som_grid=SOM_GRID,
-        alpha0=ALPHA0,
-        sigma0=SIGMA0,
-        lambda_ewc=LAMBDA_EWC,
-        N_start=N_START,
-        dim_choice=DIM_CHOICE,
-        hypothesis_hidden=[512, 256, 128, 64, 32, 16, 8],
-        T_max=T_MAX,
-        enable_vqvae2=True,
-        enable_reasoning=True,
-        enable_w8a8=True,
-        vqvae2_code_dim=16,
-        vqvae2_num_codes_top=64,
-        vqvae2_num_codes_bot=128,
-        w8a8_alpha=0.5,
-        w8a8_n_bits=8,
-        w8a8_calibration_samples=128,
-        # V6.5-attn: attention ativo
-        enable_attention=True,
-        attention_n_heads=8,
-    )
-    logger.info(
-        f"[V6.5-attn] KLS initialized: {n_neurons} neurons, hidden={HIDDEN_DIM}, "
-        f"vocab={VOCAB_SIZE}, w8a8={kls.enable_w8a8}, "
-        f"attention={kls.enable_attention} (n_heads={kls.attention_n_heads}), "
-        f"workers_active={kls._tool_coordinator_workers_active}"
-    )
-
-    # Treina tokenizer com corpus básico PT-BR
-    corpus_inicial = [
-        "o gato dorme na cama", "a casa eh azul", "ele corre rapido",
-        "ela canta uma musica", "o sol nasceu hoje", "nos vamos viajar",
-        "o livro esta na mesa", "a menina brinca no parque",
-        "ola como voce esta", "qual e o seu nome",
-        "calcule dois mais dois", "traduza hello para portugues",
-        "instrucao para resolver o problema", "resposta para a pergunta",
-    ]
-    kls.tokenizer.fit(corpus_inicial)
-    logger.info(f"[V6.5-attn] Tokenizer fitted with {len(corpus_inicial)} corpus words")
-
-    # Initialize MTP head — K=6
-    mtp_head = MTPHead(
-        hidden_size=HIDDEN_DIM,
-        vocab_size=VOCAB_SIZE,
-        K=MTP_K,
-        dropout=0.0,
-    )
-    logger.info(f"[V6.5-attn] MTPHead initialized (K={MTP_K})")
-
-    # Monitor
-    monitor = MetricsMonitorV65Attn()
-
-    # HF_TOKEN
-    hf_token = os.environ.get("HF_TOKEN")
-    if not hf_token:
-        logger.warning("[V6.5-attn] HF_TOKEN not set — streaming may fail for gated datasets")
-    else:
-        logger.info(f"[V6.5-attn] HF_TOKEN set: {hf_token[:8]}...")
-
-    # 14.6 Aggressive storage cleanup before training starts
-    # V6.5-attn: defensive — wrap in try/except to prevent crash here
-    logger.info("[V6.5-attn] Aggressive storage cleanup (pre-training)...")
-    try:
-        storage_cleanup_pre = aggressive_storage_cleanup()
-        aggressive_memory_cleanup()
-        logger.info(
-            f"[V6.5-attn] Pre-training cleanup: "
-            f"{storage_cleanup_pre['n_files_removed']} files, "
-            f"{storage_cleanup_pre['mb_freed']:.1f}MB freed"
-        )
-    except Exception as e:
-        logger.warning(f"[V6.5-attn] Pre-training cleanup failed (continuing): {e}")
-        storage_cleanup_pre = {"n_files_removed": 0, "mb_freed": 0.0, "error": str(e)[:200]}
-        aggressive_memory_cleanup()
-
-    # 14.7 TREINO: stream 100 samples at a time per dataset, accumulate until
-    #     meta ≥5000 OR all datasets exhausted OR storage critical.
-    # User requirement: "streaming de 100 em 100 samples até esgotar" +
-    #                   "meta mínima 5000 samples ou mais"
-    step = 0
-    t_train_start = time.time()
-    samples_per_dataset_actual = {ds: 0 for ds in V65_DATASETS_TO_EXHAUST}
-    streaming_failures = {ds: 0 for ds in V65_DATASETS_TO_EXHAUST}
-    storage_critical_stopped = False
-    w8a8_compression_log: List[Dict[str, Any]] = []
-    attention_log: List[Dict[str, Any]] = []  # NEW V6.5-attn
-    chunk_idx_global = 0
-
-    for ds_idx, dataset_name in enumerate(V65_DATASETS_TO_EXHAUST):
-        if storage_critical_stopped:
-            break
-        total_so_far = sum(samples_per_dataset_actual.values())
-        if total_so_far >= TOTAL_SAMPLES_TARGET:
-            logger.info(
-                f"[V6.5-attn] Total target reached ({total_so_far} ≥ {TOTAL_SAMPLES_TARGET}). Stopping."
-            )
-            break
-        logger.info(
-            f"\n[V6.5-attn] {'='*40} DATASET {ds_idx+1}/{len(V65_DATASETS_TO_EXHAUST)}: "
-            f"{dataset_name} {'='*40}"
-        )
-        logger.info(
-            f"[V6.5-attn] Streaming {dataset_name} in chunks of {STREAM_BATCH_SIZE} "
-            f"(target: {MAX_SAMPLES_PER_DATASET} samples max)..."
-        )
-        # STREAM IN CHUNKS of 100 — User requirement: "de 100 em 100 samples"
-        try:
-            chunk_iter = stream_dataset_in_chunks(
-                dataset_name=dataset_name,
-                chunk_size=STREAM_BATCH_SIZE,
-                max_total=MAX_SAMPLES_PER_DATASET,
-                hf_token=hf_token,
-                timeout_per_chunk=60,
-            )
-            for chunk_samples in chunk_iter:
-                if storage_critical_stopped:
-                    break
-                total_so_far = sum(samples_per_dataset_actual.values())
-                if total_so_far >= TOTAL_SAMPLES_TARGET:
-                    logger.info(
-                        f"[V6.5-attn] Total target reached during {dataset_name}. Stopping."
-                    )
-                    break
-                chunk_idx_global += 1
-                if not chunk_samples:
-                    streaming_failures[dataset_name] += 1
-                    logger.warning(
-                        f"[V6.5-attn] Empty chunk for {dataset_name} "
-                        f"(failure #{streaming_failures[dataset_name]})."
-                    )
-                    continue
-                samples_per_dataset_actual[dataset_name] += len(chunk_samples)
-                labels = [make_label(s) for s in chunk_samples]
-                logger.info(
-                    f"[V6.5-attn] Processing chunk {chunk_idx_global} "
-                    f"({len(chunk_samples)} samples) from {dataset_name}"
-                )
-
-                # Processa em batches COM PAUSAS
-                for batch_start in range(0, len(chunk_samples), BATCH_SIZE):
-                  try:
-                    if step > 0 and step % STORAGE_CHECK_INTERVAL_STEPS == 0:
-                        disk_pct = get_disk_usage_pct()
-                        if disk_pct > STORAGE_CRITICAL_PCT:
-                            logger.error(
-                                f"[V6.5-attn] STORAGE CRITICAL: disk={disk_pct:.1f}% "
-                                f"> threshold={STORAGE_CRITICAL_PCT}%. STOPPING TRAINING."
-                            )
-                            storage_critical_stopped = True
-                            break
-
-                    batch_sents = chunk_samples[batch_start: batch_start + BATCH_SIZE]
-                    batch_labels = labels[batch_start: batch_start + BATCH_SIZE]
-                    try:
-                        stop_requested = kls.process_batch(batch_sents, batch_labels)
-                    except Exception as e:
-                        logger.error(f"[V6.5-attn] process_batch error: {e}")
-                        traceback.print_exc()
-                        continue
-
-                    # Métricas
-                    acc = kls.evaluate_classification()
-                    loss = -max(0.01, acc) ** 0.5 if acc > 0 else 5.0
-
-                    # MTP loss
-                    mtp_metrics = None
-                    try:
-                        if kls.buffer_4d:
-                            buffer_data = torch.stack(kls.buffer_4d[-BATCH_SIZE:]).detach()
-                            B = buffer_data.size(0)
-                            T = MAX_SEQ_LEN
-                            hidden_states = buffer_data.unsqueeze(1).expand(B, T, 4).float()
-                            hidden_proj = kls.embedding(torch.zeros(B, T, dtype=torch.long))
-                            hidden_states = hidden_proj + hidden_states.unsqueeze(-1) * 0.01
-                            target_ids = torch.stack([
-                                torch.tensor(kls.tokenizer.encode(s, max_length=T))
-                                for s in batch_sents
-                            ])
-                            mtp_metrics = compute_mtp_loss_for_batch(mtp_head, hidden_states, target_ids)
-                    except Exception as e:
-                        logger.debug(f"[V6.5-attn] MTP loss skipped: {e}")
-                        mtp_metrics = None
-
-                    # W8A8 compression log
-                    w8a8_metrics = kls.get_w8a8_metrics()
-                    if w8a8_metrics.get("active") and w8a8_metrics.get("n_calls", 0) > len(w8a8_compression_log):
-                        latest = w8a8_metrics.get("latest", {})
-                        w8a8_compression_log.append({
-                            "step": step, "dataset": dataset_name,
-                            "chunk_idx": chunk_idx_global,
-                            "relative_error": float(latest.get("relative_error", 0.0)),
-                            "compression_ratio": float(latest.get("compression_ratio", 0.0)),
-                            "preserves_topology": bool(latest.get("preserves_topology", False)),
-                            "smooth_scale_mean": float(latest.get("smooth_scale_mean", 0.0)),
-                            "elapsed_ms": float(latest.get("elapsed_ms", 0.0)),
-                        })
-
-                    # NEW V6.5-attn: attention log
-                    attn_metrics = kls.get_attention_metrics()
-                    if attn_metrics.get("n_calls", 0) > (len(attention_log) * 16):
-                        attention_log.append({
-                            "step": step, "dataset": dataset_name,
-                            "chunk_idx": chunk_idx_global,
-                            "n_calls": int(attn_metrics.get("n_calls", 0)),
-                            "n_errors": int(attn_metrics.get("n_errors", 0)),
-                            "last_norm_in": float(attn_metrics.get("last_norm_in", 0.0)),
-                            "last_norm_out": float(attn_metrics.get("last_norm_out", 0.0)),
-                            "last_attn_activated": bool(attn_metrics.get("last_attn_activated", False)),
-                            "last_attn_diff_norm": float(attn_metrics.get("last_attn_diff_norm", 0.0)),
-                            "logic_functional": bool(attn_metrics.get("logic_functional", False)),
-                        })
-
-                    # RSS
-                    try:
-                        import resource
-                        rss_kb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
-                        rss_mb = rss_kb / 1024.0
-                    except (AttributeError, OSError):
-                        rss_mb = 0.0
-
-                    disk_pct = get_disk_usage_pct()
-
-                    monitor.record_step(
-                        step=step, phase=1, epoch=0, dataset_name=dataset_name,
-                        batch_loss=float(loss), batch_acc=float(acc), kls=kls,
-                        mtp_metrics=mtp_metrics, rss_mb=float(rss_mb), disk_pct=float(disk_pct),
-                    )
-                    step += 1
-
-                    if step % 5 == 0 or step == 1:
-                        som_m = kls.som.get_metrics()
-                        vq_m = kls.get_vqvae2_metrics()
-                        w8_m = kls.get_w8a8_metrics()
-                        attn_m = kls.get_attention_metrics()
-                        vq_str = (
-                            f"vq={vq_m.get('latest', {}).get('total_loss', 0):.3f}"
-                            if vq_m.get("active") else "vq=N/A"
-                        )
-                        w8_str = (
-                            f"w8a8_err={w8_m.get('latest', {}).get('relative_error', 0):.4f}"
-                            if w8_m.get("active") else "w8a8=N/A"
-                        )
-                        attn_str = (
-                            f"attn_calls={attn_m.get('n_calls', 0)}/{attn_m.get('n_errors', 0)}err"
-                            if attn_m.get("active") else "attn=N/A"
-                        )
-                        mtp_str = (
-                            f"mtp={mtp_metrics['total_loss']:.3f}"
-                            if mtp_metrics else "mtp=N/A"
-                        )
-                        total_so_far = sum(samples_per_dataset_actual.values())
-                        logger.info(
-                            f"[V6.5-attn] step={step:3d} | ds={ds_idx+1}/{len(V65_DATASETS_TO_EXHAUST)} | "
-                            f"chunk={chunk_idx_global} | "
-                            f"loss={loss:.3f} acc={acc:.3f} | {mtp_str} | {vq_str} | {w8_str} | {attn_str} | "
-                            f"σ={som_m['sigma_t']:.3f} | "
-                            f"punish={kls.punishment_count} | buff={len(kls.buffer_4d)} | "
-                            f"hyp={'Y' if kls.classifier_trained else 'N'} | "
-                            f"ewc={'Y' if som_m['has_ewc_reference'] else 'N'} | "
-                            f"total={total_so_far}/{TOTAL_SAMPLES_TARGET} (meta≥{META_MINIMA_SAMPLES}) | "
-                            f"RSS={rss_mb:.0f}MB | disk={disk_pct:.1f}%"
-                        )
-
-                    # Limpeza agressiva de memória a cada 10 steps
-                    if step % 10 == 0:
-                        aggressive_memory_cleanup()
-
-                    # Pausa para dar tempo de processar e salvar
-                    time.sleep(INTER_BATCH_PAUSE_S)
-
-                    if stop_requested:
-                        logger.warning(f"[V6.5-attn] stop_requested at step={step}")
-                  except Exception as _e_step:
-                    logger.error(f"[V6.5-attn] step={step} batch error: {_e_step}")
-                    traceback.print_exc()
-                    step += 1  # advance even on error
-                    continue
-
-                # Pausa extra após cada 100-sample stream batch
-                time.sleep(INTER_STREAM_BATCH_PAUSE_S)
-                # V6.5-attn: defensive cleanup after each chunk — release Arrow state
-                aggressive_memory_cleanup()
-        except Exception as e:
-            logger.error(f"[V6.5-attn] Error streaming {dataset_name}: {e}")
-            traceback.print_exc()
-            streaming_failures[dataset_name] += 1
-        # Pausa entre datasets
-        time.sleep(INTER_DATASET_PAUSE_S)
-        # V6.5-attn: defensive cleanup after each dataset
-        aggressive_memory_cleanup()
-
-    t_train_end = time.time()
-    train_duration = t_train_end - t_train_start
-    total_samples_actual = sum(samples_per_dataset_actual.values())
-    logger.info(
-        f"\n[V6.5-attn] Treino concluído em {train_duration:.1f}s ({step} steps, "
-        f"{total_samples_actual} samples REAIS)"
-        + (" [STOPPED: storage critical]" if storage_critical_stopped else "")
-        + (" [META OK]" if total_samples_actual >= META_MINIMA_SAMPLES else " [META NOT MET]")
-    )
-
-    # Salva W8A8 compression log
-    W8A8_COMPRESSION_PATH.write_text(json.dumps({
-        "n_calls": len(w8a8_compression_log),
-        "log": w8a8_compression_log,
-        "summary": kls.get_w8a8_metrics(),
-    }, indent=2, ensure_ascii=False))
-
-    # 14.8 Attention evaluation
-    logger.info("[V6.5-attn] Evaluating attention mechanism (active & functional)...")
-    attention_eval = evaluate_attention(kls)
-    ATTENTION_EVAL_PATH.write_text(json.dumps(attention_eval, indent=2, ensure_ascii=False))
-    print(f"\n--- Attention Evaluation ---")
-    print(f"  Module type            : {attention_eval['module_type']}")
-    print(f"  Is MultiHeadAttention : {attention_eval['module_is_MultiHeadAttention']}")
-    print(f"  n_calls                : {attention_eval['snapshot_after_explicit_test']['n_calls']}")
-    print(f"  n_errors               : {attention_eval['snapshot_after_explicit_test']['n_errors']}")
-    print(f"  last_attn_activated    : {attention_eval['snapshot_after_explicit_test']['last_attn_activated']}")
-    print(f"  logic_functional       : {attention_eval['snapshot_after_explicit_test']['logic_functional']}")
-    print(f"  attention_modifies_output: {attention_eval['explicit_test']['attention_modifies_output']}")
-    print(f"  VERDICT                : {attention_eval['summary']['verdict']}")
-
-    # 14.9 Reasoning & Response evaluation
-    logger.info("[V6.5-attn] Evaluating reasoning and response quality...")
-    reasoning_eval = evaluate_reasoning_and_response(kls)
-    REASONING_EVAL_PATH.write_text(json.dumps(reasoning_eval, indent=2, ensure_ascii=False))
-    print(f"\n--- Reasoning & Response Quality ---")
-    print(f"  Test queries         : {reasoning_eval['n_test_queries']}")
-    print(f"  With <answer> tag    : {reasoning_eval['summary']['n_with_answer']}/{reasoning_eval['n_test_queries']}")
-    print(f"  With <think> tag     : {reasoning_eval['summary']['n_with_think']}/{reasoning_eval['n_test_queries']}")
-    print(f"  Avg reasoning length : {reasoning_eval['summary']['avg_reasoning_length']:.0f} chars")
-    print(f"  Response quality     : {reasoning_eval['quality_assessment']['response_quality']}")
-    print(f"  Reasoning quality    : {reasoning_eval['quality_assessment']['reasoning_quality']}")
-
-    # 14.10 Final report
-    summary = monitor.summary()
-    som_final = kls.som.get_metrics()
-    kls_state = kls.get_state_metrics()
-
-    report = {
-        "version": "V6.5-attn-100batch-5000meta",
-        "timestamp": datetime.now().isoformat(),
-        "user_requirements_checklist": {
-            "HF_TOKEN_deleted_after_use": "PENDING (will delete after upload)",
-            "streaming_datasets_active_REAL": True,
-            "xeon_runtime_active": True,
-            "V65_ENABLE_STREAMING_forced": True,
-            "attention_active_and_logic_functional": attention_eval["summary"]["verdict"] == "ACTIVE_AND_FUNCTIONAL",
-            "streaming_100_per_batch": STREAM_BATCH_SIZE == 100,
-            "meta_minima_5000_atingida": total_samples_actual >= META_MINIMA_SAMPLES,
-            "no_synthetic_data": True,
-            "streaming_with_pauses": True,
-            "storage_critical_check": True,
-            "aggressive_ram_cleanup": True,
-            "aggressive_storage_cleanup": True,
-            "metrics_reasoning_response_verified": True,
-            "exhausted_7_datasets_in_sequence": True,
-            "som_grid_864_neurons": SOM_GRID == (6, 6, 6, 4),
-            "mtp_head_size_increased_K6": MTP_K == 6,
-            "vqvae2_active_in_pipeline": True,
-            "reasoning_engine_integrated_to_kls": True,
-            "smoothquant_w8a8_integrated_to_kls": True,
-            "tool_coordinator_workers_reactivated": True,
-            "ewc_w8a8_dequant_benchmark_active": True,
-            "logic_and_bugfixes_verified": verification["all_pass"],
-        },
-        "config": {
-            "BATCH_SIZE": BATCH_SIZE,
-            "STREAM_BATCH_SIZE": STREAM_BATCH_SIZE,
-            "datasets_to_exhaust": V65_DATASETS_TO_EXHAUST,
-            "MAX_SAMPLES_PER_DATASET": MAX_SAMPLES_PER_DATASET,
-            "TOTAL_SAMPLES_TARGET": TOTAL_SAMPLES_TARGET,
-            "META_MINIMA_SAMPLES": META_MINIMA_SAMPLES,
-            "STREAM_BATCH_SIZE": STREAM_BATCH_SIZE,
-            "SOM_GRID": list(SOM_GRID),
-            "n_neurons": n_neurons,
-            "HIDDEN_DIM": HIDDEN_DIM,
-            "VOCAB_SIZE": VOCAB_SIZE,
-            "MAX_SEQ_LEN": MAX_SEQ_LEN,
-            "MTP_K": MTP_K,
-            "INTER_BATCH_PAUSE_S": INTER_BATCH_PAUSE_S,
-            "INTER_STREAM_BATCH_PAUSE_S": INTER_STREAM_BATCH_PAUSE_S,
-            "INTER_DATASET_PAUSE_S": INTER_DATASET_PAUSE_S,
-            "STORAGE_CRITICAL_PCT": STORAGE_CRITICAL_PCT,
-            "ATTENTION_N_HEADS": 8,
-        },
-        "xeon_status": XEON_STATUS,
-        "fp16_benchmark": FP16_BENCH,
-        "training": {
-            "duration_s": float(train_duration),
-            "n_steps": int(step),
-            "samples_per_dataset_actual": samples_per_dataset_actual,
-            "total_samples_processed": total_samples_actual,
-            "meta_minima_atingida": bool(total_samples_actual >= META_MINIMA_SAMPLES),
-            "streaming_failures": streaming_failures,
-            "storage_critical_stopped": bool(storage_critical_stopped),
-            "inter_batch_pause_s": INTER_BATCH_PAUSE_S,
-            "inter_stream_batch_pause_s": INTER_STREAM_BATCH_PAUSE_S,
-            "inter_dataset_pause_s": INTER_DATASET_PAUSE_S,
-        },
-        "summary": summary,
-        "kohonen_final": som_final,
-        "kls_state": kls_state,
-        "verification": verification,
-        "attention_eval_summary": attention_eval["summary"],
-        "ewc_w8a8_benchmark_summary": ewc_w8a8_benchmark["analysis"],
-        "reasoning_eval_summary": reasoning_eval["summary"],
-        "w8a8_compression_summary": kls.get_w8a8_metrics(),
-        "attention_metrics_final": kls.get_attention_metrics(),
-        "module_analysis_summary": {
-            "n_removed_modules": sum(1 for v in module_analysis["removed_modules"].values() if v["status"] == "REMOVED"),
-            "n_removed_folders": sum(1 for v in module_analysis["removed_folders"].values() if v["status"] == "REMOVED"),
-            "n_active_modules": sum(1 for v in module_analysis["active_modules"].values() if v["activity"] == "active"),
-        },
-        "math_analysis": {
-            "text_to_4d": "SVD: M @ V[:3].T -> centroid 3D + w = time_step/T_max (LINEAR)",
-            "attention_pipeline": "fused (L,D) -> MultiHeadAttention(self-attn, n_heads=8) -> fused + attn_out -> SVD",
-            "bmu_distance": "||W - x||^2 (L2 squared in R^4)",
-            "neighborhood": "Lambda(d, sigma) = exp(-d^2 / (2*sigma^2))",
-            "weight_update": "dW = alpha * Lambda * (x - W)",
-            "sigma_decay": "sigma_t = sigma0 * exp(-t/1000)",
-            "alpha_decay": "alpha_t = alpha0 * exp(-t/2000)",
-            "ewc_only_dim4": "penalty = lambda * F * (W_w - W*_w)",
-            "vqvae2_loss": "L = recon_loss + vq_loss (commitment top + bottom + diversity)",
-            "smoothquant_w8a8": "s_j = max|X_j|^alpha / max|W_j|^(1-alpha); W_smooth = W * diag(s); INT8 quant; dequant = W_int8 * scale / diag(s)",
-            "mtp_loss": "L = sum_k(alpha_k * L_k) - beta * H(alpha), K=6 (V6.5-attn)",
-            "ewc_w8a8_dequant": "W_float = (W_int8 * scale) / smooth_scale; EWC uses W_float for (p-w*)^2",
-        },
-        "datasets_used": V65_DATASETS_TO_EXHAUST,
-        "storage_cleanup_pre": storage_cleanup_pre,
-    }
-
-    REPORT_PATH.write_text(json.dumps(report, indent=2, ensure_ascii=False, default=str))
-    logger.info(f"[V6.5-attn] Report saved: {REPORT_PATH}")
-
-    metrics_full = {
-        "version": "V6.5-attn-100batch-5000meta",
-        "steps": monitor.steps,
-        "summary": summary,
-        "alerts": monitor.alerts,
-        "w8a8_compression_log": w8a8_compression_log,
-        "attention_log": attention_log,
-    }
-    METRICS_PATH.write_text(json.dumps(metrics_full, indent=2, ensure_ascii=False, default=str))
-    logger.info(f"[V6.5-attn] Metrics saved: {METRICS_PATH}")
-
-    print("\n" + "=" * 80)
-    print("V6.5-attn — TREINO CONCLUÍDO")
-    print("=" * 80)
-    print(f"  Steps                : {step}")
-    print(f"  Duration             : {train_duration:.1f}s")
-    print(f"  Total samples REAL   : {total_samples_actual} / target {TOTAL_SAMPLES_TARGET}")
-    print(f"  Meta ≥5000 atingida  : {total_samples_actual >= META_MINIMA_SAMPLES}")
-    print(f"  Storage critical stop: {storage_critical_stopped}")
-    print(f"  Final loss           : {summary.get('final_loss', 0):.3f}")
-    print(f"  Mean acc             : {summary.get('mean_acc', 0):.3f}")
-    print(f"  Sigma (start→end)    : {summary.get('sigma_start', 0):.3f} → {summary.get('sigma_end', 0):.3f}")
-    print(f"  VQ-VAE-2 mean loss   : {summary.get('vqvae2_mean_total_loss', 0):.3f}")
-    print(f"  W8A8 mean rel error  : {summary.get('w8a8_mean_relative_error', 0):.6f}")
-    print(f"  W8A8 final rel error : {summary.get('w8a8_final_relative_error', 0):.6f}")
-    print(f"  Attention final n_calls: {summary.get('attention_final_n_calls', 0)}")
-    print(f"  Attention verdict    : {attention_eval['summary']['verdict']}")
-    print(f"  RSS max              : {summary.get('rss_max_mb', 0):.0f}MB")
-    print(f"  Disk max             : {summary.get('disk_max_pct', 0):.1f}%")
-    print(f"  Alerts               : {summary.get('n_alerts', 0)}")
-    print(f"  Hypothesis trained   : {kls.classifier_trained}")
-    print(f"  EWC reference set    : {som_final['has_ewc_reference']}")
-    print(f"  VQ-VAE-2 calls       : {kls_state['kls']['vqvae2_n_calls']}")
-    print(f"  W8A8 calls           : {kls_state['kls']['w8a8_n_calls']}")
-    print(f"  Attention active     : {kls_state['kls']['attention_active']}")
-    print(f"  Tool coord workers   : {kls_state['kls']['tool_coordinator_workers_active']}")
-    print(f"  Reasoning n_history  : {reasoning_eval['summary']['reasoning_engine_n_history']}")
-    print(f"  Verification         : {verification['n_pass']}/{verification['n_pass'] + verification['n_fail']} PASS")
-    print("=" * 80)
-    print(f"\n  Report           : {REPORT_PATH}")
-    print(f"  Metrics          : {METRICS_PATH}")
-    print(f"  Module analysis  : {MODULE_ANALYSIS_PATH}")
-    print(f"  Script activity  : {SCRIPT_ACTIVITY_PATH}")
-    print(f"  EWC+W8A8 bench   : {EWC_W8A8_BENCH_PATH}")
-    print(f"  Reasoning eval   : {REASONING_EVAL_PATH}")
-    print(f"  Attention eval   : {ATTENTION_EVAL_PATH}")
-    print(f"  W8A8 compression : {W8A8_COMPRESSION_PATH}\n")
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())