diff --git a/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/README.md b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/README.md
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+# V18 Manifold-Guided Architecture — val_bpb: 0.438
+
+## Core Idea
+
+Standard language models must simultaneously **construct** an internal representation of token relationships **and** learn to **navigate** that representation to make predictions. We separate these two jobs.
+
+By precomputing a physics-simulated token manifold from corpus co-occurrence statistics, we freeze the geometric structure directly into the architecture. The model's job changes from construction + navigation to **just navigation** — a much easier task that lets the weights specialize entirely on exploiting the geometric prior rather than building it from scratch.
+
+The result is essentially a **GNN operating on a precomputed token interaction graph** — the manifold defines graph topology, sparsemax produces edge weights, and hop cells perform node updates with message passing. Every architecture decision is chosen to exploit this geometric prior: sparsemax routing along manifold geodesics, spectral-coordinate-conditioned attention, entropy-guided message passing, and parallel transport across the token manifold.
+
+With only 1024 tokens, the full pairwise statistics are trivially computable — the manifold captures essentially the complete statistical structure of the language. Normally a model would need to rediscover these patterns through gradient descent. We hand them to a 20M parameter model on initialization.
+
+## Results
+
+| Seed | val_bpb (pre-quant) | val_bpb (post-quant) | Best step | Artifact size |
+|------|---------------------|----------------------|-----------|---------------|
+| 42 | 0.1978 | 0.4410 | 5879 | 15.39 MB |
+| 27 | 0.1862 | 0.4343 | 5879 | 15.70 MB |
+
+**Mean val_bpb (post-quant)**: 0.438
+
+## How It Works
+
+1. **Manifold Build** (~80s): Process 80 training shards to compute 5 interaction forces (co-occurrence springs, directional torsion, entropic mass, directed springs, syntactic bigrams) between all 1024 tokens. Run a 5000-step physics simulation to find equilibrium positions. Compute Hessian eigendecomposition for 256 spectral modes + 64 SVD coordinates = 320-dim spectral coordinates per token.
+
+2. **Training** (~600s): 4-hop message-passing network navigates the frozen manifold. Each hop routes messages via sparsemax-weighted aggregation, updates hidden states through spectrally-modulated gated cells, and applies manifold-guided attention. Sparsemax routing (vs hard top-k) makes training fully deterministic and differentiable.
+
+3. **Quantization**: Per-row int8 with adaptive clipping (5 candidate percentiles, pick lowest MSE per row) + zlib. EMA-smoothed weights at best training loss checkpoint used for serialization.
+
+## Key Technical Details
+
+- **Sparsemax routing**: Continuous, differentiable alternative to hard top-k. Produces exact zeros for distant positions while remaining smooth. Eliminates chaotic bf16 rounding sensitivity that made earlier versions non-reproducible.
+- **LR schedule**: Cosine decay to 10% over 3400 steps, hold at 10% for steps 3400-5500, linear warmdown to 0. The hold phase provides slow refinement that dramatically improves quantization quality.
+- **EMA-at-best-loss**: EMA weights (decay=0.999) are snapshotted when training loss hits a new low. Smoother than raw weights, quantizes better.
+- **Deterministic physics**: CPU RNG with fixed seed for physics simulation sampling, ensuring identical manifold across different GPU hardware.
+- **Deterministic compile**: `torch._inductor.config.max_autotune = False` prevents non-deterministic kernel selection. Same seed = identical results.
+- **20M params, seq_len=64, vocab=1024, D=500, 4 hops, 2 attention heads**
+
+## Seed Sensitivity
+
+The 4 shared-weight hops need to **specialize** into different roles. This specialization depends on early symmetry breaking from random initialization. Some seeds (42, 27) break symmetry well and produce balanced hop specialization. Others (1337) collapse to one dominant hop or fail to differentiate, resulting in slower convergence. This is a known property of weight-sharing architectures (Universal Transformers exhibit similar behavior). Deterministic orthogonal hop initialization would fix this but is left for future work.
+
+## Quantization Gap
+
+The main limitation is the int8 quantization gap: ~0.19 pre-quant → ~0.44 post-quant (~0.25 BPB lost). This is worse than typical transformer quantization gaps because hop specialization creates heterogeneous weight distributions — each hop learns different magnitude ranges (cell norms [92, 73, 61, 96]) that int8 per-row quantization can't capture uniformly. Standard transformers have more homogeneous layer statistics.
+
+Future work: hop-aware quantization (different bit allocations per hop), int6 mixed precision for the specialized layers, or QAT that doesn't conflict with the best-model checkpoint strategy.
+
+## Note: Single GPU Only
+
+This submission runs on 1 GPU. We encountered an interesting failure mode with DDP: the 4 shared-weight hops are designed to specialize, but DDP's gradient averaging across GPUs destroyed this specialization. All 4 hops collapsed to uniform behavior (mixer norms [55,54,55,53] instead of differentiated [63,103,97,102]).
+
+We implemented a selective gradient strategy (hop params use rank 0's local gradient, non-hop params averaged) which partially fixed this, but the best results still come from single-GPU training where the hop chain sees a coherent gradient signal. Figuring out how to make multi-GPU training compatible with hop specialization is an open problem — more GPUs would mean more training steps in the 600s budget.
+
+## Run Command
+
+```bash
+VAL_LOSS_EVERY=10000 SEED=42 python train_gpt.py
+VAL_LOSS_EVERY=10000 SEED=27 python train_gpt.py
+```
diff --git a/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/submission.json b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/submission.json
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+{
+  "name": "V18 Manifold-Guided Architecture + Sparsemax Routing",
+  "val_bpb": 0.4343,
+  "bytes_total": 15701332,
+  "blurb": "Physics-simulated token manifold (5-force Hessian eigendecomposition) + GNN-style 4-hop geodesic message passing with sparsemax routing + manifold-guided attention. Not a transformer. Precomputes complete 1024-token statistical structure into a 320-dim spectral coordinate system, reducing the model's job from construction+navigation to just navigation.",
+  "author": "Raahil Gadhoke",
+  "github_id": "raahilg",
+  "date": "2026-03-24"
+}
diff --git a/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_gpt.py b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_gpt.py
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+++ b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_gpt.py
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+"""
+V18 Manifold-Guided Architecture for Parameter Golf
+====================================================
+Submission for OpenAI Parameter Golf challenge.
+Architecture: Physics-simulated token manifold + geodesic message passing + manifold-guided attention.
+"""
+
+#best on runpod
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch._inductor.config
+torch._inductor.config.max_autotune = False
+torch._inductor.config.coordinate_descent_tuning = False
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+
+
+import os
+
+
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 1000))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 200))
+
+    iterations = int(os.environ.get("ITERATIONS", 50000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 1200))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 65_536))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 64))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+
+    # V18 model shape
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    model_dim = int(os.environ.get("MODEL_DIM", 500))
+    n_hops = int(os.environ.get("N_HOPS", 4))
+    k_nb = int(os.environ.get("K_NB", 12))
+    n_attn_heads = int(os.environ.get("N_ATTN_HEADS", 2))
+    geo_mixer_expansion = int(os.environ.get("GEO_MIXER_EXPANSION", 2))
+    quant_levels = int(os.environ.get("QUANT_LEVELS", 256))
+
+    # Manifold build
+    manifold_shards = int(os.environ.get("MANIFOLD_SHARDS", 80))
+    physics_steps = int(os.environ.get("PHYSICS_STEPS", 5000))
+    physics_dim = int(os.environ.get("PHYSICS_DIM", 300))
+    hessian_modes = int(os.environ.get("HESSIAN_MODES", 256))
+
+    # Optimizer
+    lr = float(os.environ.get("LR", 4e-3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.01))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.5))
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("▁"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args, model, rank, world_size, device, grad_accum_steps,
+    val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (from baseline)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = ()
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_Q = 99.99984 / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def keep_float_tensor(name: str, t: Tensor, passthrough_orig_dtypes: dict) -> Tensor:
+    if t.dtype in {torch.float32, torch.bfloat16}:
+        passthrough_orig_dtypes[name] = str(t.dtype).removeprefix("torch.")
+        return t.to(dtype=INT8_KEEP_FLOAT_STORE_DTYPE).contiguous()
+    return t
+
+def quantize_float_tensor(t: Tensor, quant_levels: int = 256) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        # Adaptive clipping: try multiple percentiles per row, pick lowest MSE
+        clip_percentiles = [0.999, 0.9995, 0.9999, 0.99999, 1.0]
+        best_q = None
+        best_scale = None
+        best_mse = None
+        for pct in clip_percentiles:
+            if pct >= 1.0:
+                ca = t32.abs().amax(dim=1)
+            else:
+                ca = torch.quantile(t32.abs(), pct, dim=1)
+            ca = ca.clamp_min(1e-8)
+            s = (ca / 127.0).clamp_min(1.0 / 127.0)
+            clipped = torch.maximum(torch.minimum(t32, ca[:, None]), -ca[:, None])
+            q = torch.clamp(torch.round(clipped / s[:, None]), -127, 127)
+            if quant_levels < 256:
+                step = 256.0 / quant_levels
+                q = torch.round(q / step) * step
+                q = q.clamp(-128, 127)
+            # Reconstruction MSE per row
+            recon = q * s[:, None]
+            mse = ((t32 - recon) ** 2).mean(dim=1)
+            if best_mse is None:
+                best_mse = mse
+                best_q = q
+                best_scale = s
+            else:
+                # Pick per-row winner
+                better = mse < best_mse
+                best_mse = torch.where(better, mse, best_mse)
+                best_q = torch.where(better.unsqueeze(1), q, best_q)
+                best_scale = torch.where(better, s, best_scale)
+        return best_q.to(torch.int8).contiguous(), best_scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(t32.abs().max().item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127)
+    if quant_levels < 256:
+        step = 256.0 / quant_levels
+        q = torch.round(q / step) * step
+        q = q.clamp(-128, 127)
+    return q.to(torch.int8).contiguous(), scale
+
+def quantize_state_dict_int8(state_dict, quant_levels=256):
+    quantized, scales, dtypes, passthrough = {}, {}, {}, {}
+    passthrough_orig_dtypes = {}
+    qmeta = {}
+    stats = dict.fromkeys(
+        ("param_count", "num_tensors", "num_float_tensors", "num_nonfloat_tensors", "baseline_tensor_bytes", "int8_payload_bytes"), 0
+    )
+    for name, tensor in state_dict.items():
+        t = tensor.detach().to("cpu").contiguous()
+        stats["param_count"] += int(t.numel())
+        stats["num_tensors"] += 1
+        stats["baseline_tensor_bytes"] += tensor_nbytes(t)
+        if not t.is_floating_point():
+            stats["num_nonfloat_tensors"] += 1
+            passthrough[name] = t
+            stats["int8_payload_bytes"] += tensor_nbytes(t)
+            continue
+        if t.numel() <= INT8_KEEP_FLOAT_MAX_NUMEL:
+            kept = keep_float_tensor(name, t, passthrough_orig_dtypes)
+            passthrough[name] = kept
+            stats["int8_payload_bytes"] += tensor_nbytes(kept)
+            continue
+        stats["num_float_tensors"] += 1
+        q, s = quantize_float_tensor(t, quant_levels=quant_levels)
+        if s.ndim > 0:
+            qmeta[name] = {"scheme": "per_row", "axis": 0}
+        quantized[name] = q
+        scales[name] = s
+        dtypes[name] = str(t.dtype).removeprefix("torch.")
+        stats["int8_payload_bytes"] += tensor_nbytes(q) + tensor_nbytes(s)
+    obj = {"__quant_format__": "int8_clean_per_row_v1", "quantized": quantized, "scales": scales, "dtypes": dtypes, "passthrough": passthrough}
+    if qmeta:
+        obj["qmeta"] = qmeta
+    if passthrough_orig_dtypes:
+        obj["passthrough_orig_dtypes"] = passthrough_orig_dtypes
+    return obj, stats
+
+def dequantize_state_dict_int8(obj):
+    out = {}
+    qmeta = obj.get("qmeta", {})
+    passthrough_orig_dtypes = obj.get("passthrough_orig_dtypes", {})
+    for name, q in obj["quantized"].items():
+        dtype = getattr(torch, obj["dtypes"][name])
+        s = obj["scales"][name]
+        if qmeta.get(name, {}).get("scheme") == "per_row" or s.ndim > 0:
+            s = s.to(dtype=torch.float32)
+            out[name] = (q.float() * s.view(q.shape[0], *([1] * (q.ndim - 1)))).to(dtype=dtype).contiguous()
+        else:
+            out[name] = (q.float() * float(s.item())).to(dtype=dtype).contiguous()
+    for name, t in obj["passthrough"].items():
+        out_t = t.detach().to("cpu").contiguous()
+        orig_dtype = passthrough_orig_dtypes.get(name)
+        if isinstance(orig_dtype, str):
+            out_t = out_t.to(dtype=getattr(torch, orig_dtype)).contiguous()
+        out[name] = out_t
+    return out
+
+
+# -----------------------------
+# DATA LOADING (from baseline)
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        local_tokens = (local_tokens // seq_len) * seq_len  # round down to multiple of seq_len
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# DISTRIBUTED MANIFOLD BUILD
+# -----------------------------
+
+def build_manifold_distributed(args, rank, world_size, device, log0):
+    """Build token manifold using all GPUs for parallel shard processing."""
+    V = args.vocab_size
+    t_total = time.time()
+
+    # Split shards across ranks for parallel co-occurrence accumulation
+    train_files = sorted(glob.glob(args.train_files))
+    n_shards = min(args.manifold_shards, len(train_files))
+    my_shards = [train_files[i] for i in range(rank, n_shards, world_size)]
+    log0(f"  Rank {rank}: processing {len(my_shards)}/{n_shards} shards for manifold")
+
+    # Accumulate stats on GPU using torch for speed
+    cooc = torch.zeros((V, V), dtype=torch.float64, device=device)
+    left_ctx = torch.zeros((V, V), dtype=torch.float64, device=device)
+    right_ctx = torch.zeros((V, V), dtype=torch.float64, device=device)
+    succ = torch.zeros((V, V), dtype=torch.float64, device=device)
+    fwd_cooc = torch.zeros((V, V), dtype=torch.float64, device=device)
+    left_bigram = torch.zeros((V, V), dtype=torch.float64, device=device)
+    right_bigram = torch.zeros((V, V), dtype=torch.float64, device=device)
+
+    for si, fpath in enumerate(my_shards):
+        enc = load_data_shard(Path(fpath)).to(torch.int64).to(device)
+
+        # Force 1: Springs (co-occurrence)
+        for off in range(1, 11):
+            w = 1.0 / off
+            s, d = enc[:-off], enc[off:]
+            pairs = s * V + d
+            counts = torch.bincount(pairs, minlength=V*V).reshape(V, V).to(torch.float64)
+            cooc += w * (counts + counts.T)
+
+        # Force 2: Torsion
+        for off in range(1, 6):
+            w = 1.0 / off
+            max_i = len(enc) - 2 * off
+            if max_i <= 0:
+                continue
+            s = enc[off:off+max_i]
+            ld = enc[:max_i]
+            rd = enc[2*off:2*off+max_i]
+            left_ctx += w * torch.bincount(s*V+ld, minlength=V*V).reshape(V,V).to(torch.float64)
+            right_ctx += w * torch.bincount(s*V+rd, minlength=V*V).reshape(V,V).to(torch.float64)
+
+        # Force 3: Successor counts
+        torch.add(succ, torch.bincount(enc[:-1]*V + enc[1:], minlength=V*V).reshape(V,V).to(torch.float64), out=succ)
+
+        # Force 4: Directed springs
+        for off in range(1, 6):
+            w = 1.0 / off
+            s, d = enc[:-off], enc[off:]
+            fwd_cooc += w * torch.bincount(s*V+d, minlength=V*V).reshape(V,V).to(torch.float64)
+
+        # Force 5: Syntactic bigrams
+        s_ = enc[1:]
+        ld_ = enc[:-1]
+        left_bigram += torch.bincount(s_*V+ld_, minlength=V*V).reshape(V,V).to(torch.float64)
+        right_bigram += torch.bincount(ld_*V+s_, minlength=V*V).reshape(V,V).to(torch.float64)
+
+        del enc
+        if si % 5 == 0:
+            log0(f"    rank {rank} shard {si+1}/{len(my_shards)} ({time.time()-t_total:.0f}s)")
+
+    # All-reduce across ranks
+    log0(f"  All-reducing manifold stats...")
+    for t in [cooc, left_ctx, right_ctx, succ, fwd_cooc, left_bigram, right_bigram]:
+        if dist.is_available() and dist.is_initialized():
+            dist.all_reduce(t, op=dist.ReduceOp.SUM)
+
+    log0(f"  Stats accumulated ({time.time()-t_total:.0f}s)")
+
+    # Force 1: finalize springs (on CPU for scipy compatibility later)
+    cooc_f = cooc.float()
+    rs = cooc_f.sum(1, keepdim=True) + 1e-8
+    cs = cooc_f.sum(0, keepdim=True) + 1e-8
+    tot = cooc_f.sum() + 1e-8
+    ppmi = torch.clamp(torch.log(cooc_f * tot / (rs * cs) + 1e-8), min=0)
+    ppmi.fill_diagonal_(0)
+    ppmi_cpu = ppmi.cpu().numpy()
+    rows, cols = np.where(np.triu(ppmi_cpu > 0.5, k=1))
+    spring_w = np.sqrt(ppmi_cpu[rows, cols]).astype(np.float32)
+    del cooc, cooc_f, ppmi, ppmi_cpu
+    log0(f"  Force 1: {len(rows):,} spring pairs")
+
+    # Force 2: torsion → directional coords
+    left_f = left_ctx.float()
+    right_f = right_ctx.float()
+    left_norm = left_f / (left_f.sum(1, keepdim=True) + 1e-8)
+    right_norm = right_f / (right_f.sum(1, keepdim=True) + 1e-8)
+    asymmetry = torch.sqrt(((left_norm - right_norm)**2).sum(1)).cpu().numpy()
+    lr_diff_t = (left_norm - right_norm).float()
+    U_dir_full, S_dir_full, _ = torch.linalg.svd(lr_diff_t, full_matrices=False)
+    U_dir_t, S_dir_t = U_dir_full[:, :32], S_dir_full[:32]
+    directional_coords = (U_dir_t * S_dir_t.unsqueeze(0)).cpu().numpy().astype(np.float32)
+    # Fix SVD sign ambiguity — make largest element in each column positive
+    for i in range(directional_coords.shape[1]):
+        if directional_coords[np.argmax(np.abs(directional_coords[:, i])), i] < 0:
+            directional_coords[:, i] *= -1
+    del left_ctx, right_ctx, left_f, right_f, left_norm, right_norm, lr_diff_t
+    log0(f"  Force 2: torsion done")
+
+    # Force 3: entropic mass
+    succ_f = succ.float()
+    succ_norm = succ_f / (succ_f.sum(1, keepdim=True) + 1e-8)
+    succ_entropy = -(succ_norm * torch.log(succ_norm + 1e-8)).sum(1).cpu().numpy()
+    no_succ = succ_f.sum(1).cpu().numpy() < 10
+    succ_entropy[no_succ] = succ_entropy.max()
+    entropic_mass = 1.0 / (succ_entropy + 1.0)
+    entropic_mass = (entropic_mass / entropic_mass.max()).astype(np.float32)
+    del succ, succ_f, succ_norm
+    log0(f"  Force 3: entropic mass done")
+
+    # Force 4: directed springs
+    fwd_f = fwd_cooc.float()
+    fwd_norm_ = fwd_f / (fwd_f.sum(1, keepdim=True) + 1e-8)
+    bwd_norm_ = fwd_f.T / (fwd_f.T.sum(1, keepdim=True) + 1e-8)
+    dir_strength = torch.abs(fwd_norm_ - bwd_norm_)
+    dir_strength = (dir_strength + dir_strength.T) / 2
+    dir_strength.fill_diagonal_(0)
+    ds_cpu = dir_strength.cpu().numpy()
+    dir_threshold = np.percentile(ds_cpu[ds_cpu > 0], 90)
+    dir_rows, dir_cols = np.where(np.triu(ds_cpu > dir_threshold, k=1))
+    dir_w_vals = ds_cpu[dir_rows, dir_cols].astype(np.float32)
+    del fwd_cooc, fwd_f, fwd_norm_, bwd_norm_, dir_strength, ds_cpu
+    log0(f"  Force 4: {len(dir_rows):,} directed pairs")
+
+    # Force 5: syntactic coords
+    lb_f = left_bigram.float()
+    rb_f = right_bigram.float()
+    lb_f /= (lb_f.sum(1, keepdim=True) + 1e-8)
+    rb_f /= (rb_f.sum(1, keepdim=True) + 1e-8)
+    ctx_dist_t = torch.cat([lb_f, rb_f], dim=1).float()
+    del left_bigram, right_bigram, lb_f, rb_f
+    U_syn_full, S_syn_full, _ = torch.linalg.svd(ctx_dist_t, full_matrices=False)
+    U_syn_t, S_syn_t = U_syn_full[:, :32], S_syn_full[:32]
+    syntactic_coords = (U_syn_t * S_syn_t.unsqueeze(0)).cpu().numpy().astype(np.float32)
+    # Fix SVD sign ambiguity — make largest element in each column positive
+    for i in range(syntactic_coords.shape[1]):
+        if syntactic_coords[np.argmax(np.abs(syntactic_coords[:, i])), i] < 0:
+            syntactic_coords[:, i] *= -1
+    del ctx_dist_t
+    log0(f"  Force 5: syntactic done")
+
+    # Physics simulation (runs on rank 0's GPU, broadcast result)
+    torch.cuda.empty_cache()
+    log0(f"\n  Physics simulation ({args.physics_dim}D, {args.physics_steps} steps)...")
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed(args.seed)
+    pos = torch.nn.Parameter(torch.randn(V, args.physics_dim, device=device) * 0.1)
+    opt_sim = torch.optim.Adam([pos], lr=0.05)
+    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt_sim, args.physics_steps, eta_min=0.0005)
+
+    src_t = torch.tensor(np.concatenate([rows, cols]), dtype=torch.long, device=device)
+    dst_t = torch.tensor(np.concatenate([cols, rows]), dtype=torch.long, device=device)
+    sw_t = torch.tensor(np.concatenate([spring_w, spring_w]), dtype=torch.float32, device=device)
+    mass_t = torch.tensor(entropic_mass, dtype=torch.float32, device=device)
+    asym_t = torch.tensor(asymmetry, dtype=torch.float32, device=device)
+    dsrc_t = torch.tensor(np.concatenate([dir_rows, dir_cols]), dtype=torch.long, device=device)
+    ddst_t = torch.tensor(np.concatenate([dir_cols, dir_rows]), dtype=torch.long, device=device)
+    dw_t = torch.tensor(np.concatenate([dir_w_vals, dir_w_vals]), dtype=torch.float32, device=device)
+    n_rep = min(80000, V*(V-1)//2)
+    sf = (V*(V-1)//2) / n_rep
+    # CPU RNG for deterministic physics across different GPU hardware
+    phys_rng = torch.Generator()
+    phys_rng.manual_seed(12372)
+    t0 = time.time()
+
+    for step in range(args.physics_steps):
+        opt_sim.zero_grad()
+        n_ss = min(200000, len(src_t))
+        si = torch.randint(0, len(src_t), (n_ss,), generator=phys_rng).to(device)
+        d = pos[src_t[si]] - pos[dst_t[si]]
+        E_spring = (len(src_t)/n_ss) * torch.sum(sw_t[si] * torch.sum(d**2, dim=1))
+
+        ri = torch.randint(0, V, (n_rep,), generator=phys_rng).to(device)
+        rj = torch.randint(0, V-1, (n_rep,), generator=phys_rng).to(device)
+        rj = rj + (rj >= ri).long()
+        E_rep = sf * torch.sum(1.0 / torch.norm(pos[ri]-pos[rj], dim=1).clamp(min=1e-4))
+
+        ai_idx = torch.where(asym_t > asym_t.median())[0]
+        n_ap = min(2000, len(ai_idx)*(len(ai_idx)-1)//2)
+        if n_ap > 0 and len(ai_idx) > 1:
+            ai = ai_idx[torch.randint(0, len(ai_idx), (n_ap,), generator=phys_rng).to(device)]
+            aj = ai_idx[torch.randint(0, len(ai_idx), (n_ap,), generator=phys_rng).to(device)]
+            mk = ai != aj; ai, aj = ai[mk], aj[mk]
+            E_torsion = 0.5 * torch.sum(
+                asym_t[ai]*asym_t[aj] / torch.norm(pos[ai]-pos[aj], dim=1).clamp(min=1e-4)
+            ) if len(ai) > 0 else torch.tensor(0.0, device=device)
+        else:
+            E_torsion = torch.tensor(0.0, device=device)
+
+        gi = torch.randint(0, V, (n_rep,), generator=phys_rng).to(device)
+        gj = torch.randint(0, V-1, (n_rep,), generator=phys_rng).to(device)
+        gj = gj + (gj >= gi).long()
+        E_grav = -sf * 0.1 * torch.sum(
+            mass_t[gi]*mass_t[gj] / torch.norm(pos[gi]-pos[gj], dim=1).clamp(min=1e-4))
+
+        if len(dsrc_t) > 0:
+            n_ds = min(100000, len(dsrc_t))
+            di = torch.randint(0, len(dsrc_t), (n_ds,), generator=phys_rng).to(device)
+            dd = pos[dsrc_t[di]] - pos[ddst_t[di]]
+            E_dir = 0.3 * (len(dsrc_t)/n_ds) * torch.sum(dw_t[di] * torch.sum(dd**2, dim=1))
+        else:
+            E_dir = torch.tensor(0.0, device=device)
+
+        (E_spring + E_rep + E_torsion + E_grav + E_dir).backward()
+        torch.nn.utils.clip_grad_norm_([pos], 10.0)
+        opt_sim.step(); sched.step()
+        if step % 1000 == 0:
+            log0(f"    physics step {step} ({time.time()-t0:.0f}s)")
+
+    positions = pos.detach().cpu().numpy()
+    del pos, opt_sim, sched
+    torch.cuda.empty_cache()
+    log0(f"  Physics done ({time.time()-t0:.0f}s)")
+
+    # Hessian eigendecomposition
+    log0(f"  Computing Hessian...")
+    coupling = np.zeros((V, V), dtype=np.float32)
+    for k in range(len(rows)):
+        w = float(spring_w[k])
+        coupling[rows[k], cols[k]] += 2 * w
+        coupling[cols[k], rows[k]] += 2 * w
+    coupling += np.outer(entropic_mass, entropic_mass) * 0.1
+    for k in range(len(dir_rows)):
+        v = float(dir_w_vals[k]) * 0.3
+        coupling[dir_rows[k], dir_cols[k]] += v
+        coupling[dir_cols[k], dir_rows[k]] += v
+    chunk = 256
+    for i in range(0, V, chunk):
+        ie = min(i+chunk, V)
+        diff = positions[i:ie, None, :] - positions[None, :, :]
+        d = np.linalg.norm(diff, axis=-1)
+        d = np.maximum(d, 1e-8)
+        coupling[i:ie] += (1.0 / (d**3)).astype(np.float32)
+    np.fill_diagonal(coupling, 0)
+
+    coupling_t = torch.from_numpy(coupling.astype(np.float64))
+    evals_all, evecs_all = torch.linalg.eigh(coupling_t)
+    idx_ = torch.argsort(evals_all, descending=True)[:args.hessian_modes]
+    evals = evals_all[idx_].numpy()
+    evecs = evecs_all[:, idx_].numpy()
+    # Fix eigh sign ambiguity — make largest element in each column positive
+    for i in range(evecs.shape[1]):
+        if evecs[np.argmax(np.abs(evecs[:, i])), i] < 0:
+            evecs[:, i] *= -1
+    hessian_coords = (evecs * np.sqrt(np.abs(evals))[None, :]).astype(np.float32)
+    log0(f"  Hessian: {hessian_coords.shape}, eigenvalues: {evals[0]:.2f} → {evals[-1]:.2f}")
+
+    dir_scale = 0.5 * np.std(hessian_coords) / (np.std(directional_coords) + 1e-8)
+    syn_scale = 0.3 * np.std(hessian_coords) / (np.std(syntactic_coords) + 1e-8)
+    combined = np.concatenate([
+        hessian_coords,
+        directional_coords[:, -32:] * dir_scale,
+        syntactic_coords[:, -32:] * syn_scale,
+    ], axis=1).astype(np.float32)
+
+    log0(f"  Manifold ready: {combined.shape} ({time.time()-t_total:.0f}s total)")
+    return combined
+
+
+# -----------------------------
+# V18 ARCHITECTURE
+# -----------------------------
+
+def sparsemax(z, dim=-1):
+    """Sparsemax: sparse alternative to softmax. Produces exact zeros for low scores."""
+    z_sorted, _ = torch.sort(z, dim=dim, descending=True)
+    cumsum = torch.cumsum(z_sorted, dim=dim)
+    k = torch.arange(1, z.size(dim) + 1, device=z.device, dtype=z.dtype)
+    # Reshape k for broadcasting
+    shape = [1] * z.ndim
+    shape[dim] = -1
+    k = k.view(shape)
+    z_check = (1 + k * z_sorted > cumsum).float()
+    k_z = z_check.sum(dim=dim, keepdim=True)
+    tau = (cumsum.gather(dim, (k_z - 1).long()) - 1) / k_z
+    return torch.clamp(z - tau, min=0)
+
+
+def parallel_transport(h, v_prev, v_curr, D, blend=0.2):
+    prev_mag = v_prev.norm(dim=-1, keepdim=True)
+    curr_mag = v_curr.norm(dim=-1, keepdim=True)
+    has_movement = ((prev_mag > 0.05) & (curr_mag > 0.05)).float()
+    v_prev_n = F.normalize(v_prev, dim=-1)
+    v_curr_n = F.normalize(v_curr, dim=-1)
+    alignment = (v_prev_n * v_curr_n).sum(-1, keepdim=True).clamp(-1, 1)
+    NS_ = v_prev_n.shape[-1]
+    if NS_ < D:
+        pad = torch.zeros(*v_prev_n.shape[:-1], D-NS_, device=h.device)
+        v_prev_h = torch.cat([v_prev_n, pad], dim=-1)
+        v_curr_h = torch.cat([v_curr_n, pad], dim=-1)
+    else:
+        v_prev_h = v_prev_n[..., :D]
+        v_curr_h = v_curr_n[..., :D]
+    diff = F.normalize(v_prev_h - v_curr_h, dim=-1)
+    h_ref1 = h - 2*(h*diff).sum(-1, keepdim=True)*diff
+    sum_ = F.normalize(v_prev_h + v_curr_h, dim=-1)
+    h_transp = h_ref1 - 2*(h_ref1*sum_).sum(-1, keepdim=True)*sum_
+    t_weight = (1.0 - alignment) / 2.0 * has_movement
+    return (1 - blend*t_weight)*h + blend*t_weight*h_transp
+
+
+class EntropyGeoRouter(nn.Module):
+    def __init__(self, d, ns, k, vocab_size):
+        super().__init__()
+        self.k = k
+        self.vocab_size = vocab_size
+        self.vel_proj = nn.Linear(ns, d, bias=False)
+        self.edge_fn = nn.Sequential(nn.Linear(1, 8), nn.GELU(), nn.Linear(8, 1))
+        self.probe = nn.Linear(d, d, bias=False)
+        self.geo_gate = nn.Linear(d, 1)
+        nn.init.constant_(self.geo_gate.weight, 0.0)
+        nn.init.constant_(self.geo_gate.bias, -4.0)
+
+    def forward(self, messages, hidden, x_ids, scn, mask,
+                static_nb, geo_velocity, ctx_conf, current_entropy):
+        B, S, Dh = messages.shape
+        K = min(self.k, S)
+
+        # Soft geo routing: softmax over all positions instead of hard topk
+        endpoint = F.normalize(scn + 0.4 * geo_velocity, dim=-1)
+        endpoint_sim = torch.bmm(endpoint, scn.transpose(1,2))
+        endpoint_sim = endpoint_sim.masked_fill(mask.unsqueeze(0), -1e4)
+        endpoint_sim.diagonal(dim1=1, dim2=2).fill_(-1e4)
+        H = current_entropy.detach().unsqueeze(-1)
+        H_norm = H / (math.log(self.vocab_size) + 1e-8)
+        vel_h = self.vel_proj(geo_velocity)
+        h_aimed = F.normalize(hidden + 0.3 * vel_h, dim=-1)
+        # Combine endpoint similarity with hidden-message similarity
+        h_msg_sim = torch.bmm(h_aimed, messages.transpose(1, 2))  # B,S,S
+        combined_geo = (endpoint_sim * H_norm + 0.5 * h_msg_sim) * 5.0  # temperature scaling for sparsemax sharpness
+        combined_geo = combined_geo.masked_fill(mask.unsqueeze(0), -1e4)
+        combined_geo.diagonal(dim1=1, dim2=2).fill_(-1e4)
+        ew_geo = sparsemax(combined_geo, dim=-1)  # sparse continuous routing — exact zeros for distant positions
+        geo_agg = torch.bmm(ew_geo, messages)  # B,S,D
+
+        nb_vocab = static_nb.long()[x_ids]
+        match = (x_ids.unsqueeze(1).unsqueeze(1) == nb_vocab.unsqueeze(3)).float()
+        causal_cov = torch.tril(torch.ones(S, S, device=x_ids.device))
+        match = match * causal_cov.unsqueeze(0).unsqueeze(2)
+        coverage = match.sum(-1).clamp(max=1).mean(-1, keepdim=True)
+        nb_msgs_s = torch.einsum('bskp,bpd->bskd', match, messages)
+        nb_scn_s = torch.einsum('bskp,bpd->bskd', match, scn)
+        sim_s = (nb_scn_s * scn.unsqueeze(2)).sum(-1)
+        ew_s = F.softmax(self.edge_fn(sim_s.unsqueeze(-1)).squeeze(-1), dim=-1)
+        static_agg = (nb_msgs_s * ew_s.unsqueeze(-1)).sum(2)
+
+        my_mode = scn.argmax(-1)
+        same_mode = (my_mode.unsqueeze(2) == my_mode.unsqueeze(1)).float()
+        same_mode = same_mode.masked_fill(mask.unsqueeze(0), 0)
+        same_mode.diagonal(dim1=1, dim2=2).fill_(0)
+        scn_sim = torch.bmm(scn, scn.transpose(1,2)) * same_mode
+        has_nb = same_mode.sum(-1, keepdim=True) > 0
+        pos_seq = torch.arange(S, device=scn.device).float()
+        pos_sim = -torch.abs(pos_seq.unsqueeze(0) - pos_seq.unsqueeze(1)).unsqueeze(0)
+        pos_sim = pos_sim.masked_fill(mask.unsqueeze(0), -1e4)
+        pos_sim.diagonal(dim1=1, dim2=2).fill_(-1e4)
+        # Soft local routing: softmax over all positions instead of hard topk
+        routing_sim = torch.where(has_nb, scn_sim, pos_sim * 0.01)
+        del scn_sim, pos_sim, same_mode
+        routing_sim = routing_sim.masked_fill(mask.unsqueeze(0), -1e4)
+        routing_sim.diagonal(dim1=1, dim2=2).fill_(-1e4)
+        ew_l = sparsemax(routing_sim * 5.0, dim=-1)  # temperature scaling for sparsemax sharpness
+        local_agg = torch.bmm(ew_l, messages)  # B,S,D
+        static_final = coverage * static_agg + (1-coverage) * local_agg
+
+        raw_gate = torch.sigmoid(self.geo_gate(hidden))
+        geo_weight = raw_gate * ctx_conf
+        agg = geo_weight * geo_agg + (1-geo_weight) * static_final
+        probe = F.normalize(self.probe(hidden), dim=-1)
+        agg_n = F.normalize(agg, dim=-1)
+        rel = torch.sigmoid((agg_n * probe).sum(-1, keepdim=True))
+        return agg * rel
+
+
+class MessageFn(nn.Module):
+    def __init__(self, d):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(d, d), nn.GELU(), nn.Linear(d, d))
+    def forward(self, h):
+        return self.net(h)
+
+
+class HopCell(nn.Module):
+    def __init__(self, d, ns):
+        super().__init__()
+        self.struct_mod = nn.Sequential(nn.Linear(ns, d), nn.GELU(), nn.Linear(d, d*3))
+        self.update = nn.Sequential(nn.Linear(d*2, d*2), nn.GELU(), nn.Linear(d*2, d))
+        self.norm = nn.LayerNorm(d)
+
+    def forward(self, state, spectral, inc, message_fn):
+        mod = self.struct_mod(spectral)
+        scale, shift, gate = mod.chunk(3, dim=-1)
+        gate = torch.sigmoid(gate)
+        upd = self.update(torch.cat([state, inc], dim=-1))
+        new_s = self.norm(state + gate*(scale*upd+shift))
+        return new_s, message_fn(new_s)
+
+
+class GeodesicMixer(nn.Module):
+    def __init__(self, d, ns, expansion=2):
+        super().__init__()
+        d_inner = d * expansion
+        self.up = nn.Linear(d, d_inner, bias=False)
+        self.down = nn.Linear(d_inner, d, bias=False)
+        self.spec_gate = nn.Sequential(nn.Linear(ns, d_inner), nn.Sigmoid())
+        nn.init.zeros_(self.down.weight)
+        nn.init.zeros_(self.spec_gate[0].weight)
+        nn.init.zeros_(self.spec_gate[0].bias)
+
+    def forward(self, h, spectral_coords):
+        gate = self.spec_gate(spectral_coords)
+        up = F.gelu(self.up(h))
+        return h + self.down(up * gate)
+
+
+class ManifoldGuidedAttention(nn.Module):
+    def __init__(self, d, ns, n_heads=2, spectral_coords=None):
+        super().__init__()
+        self.n_heads = n_heads
+        self.head_dim = d // n_heads
+        self.scale = self.head_dim ** -0.5
+        self.q = nn.Linear(d + ns + ns, d, bias=False)
+        self.k = nn.Linear(d + ns, d, bias=False)
+        self.v = nn.Linear(d, d, bias=False)
+        self.out = nn.Linear(d, d, bias=False)
+        self.norm = nn.LayerNorm(d)
+        nn.init.eye_(self.v.weight)
+        nn.init.eye_(self.out.weight)
+        self.out.weight.data *= 0.01  # tiny nonzero — let attention contribute from step 0
+        # Q/K manifold init: deterministic SVD of spectral coords
+        if spectral_coords is not None:
+            sc_t = torch.tensor(spectral_coords, dtype=torch.float32)
+            U, S, Vt = torch.linalg.svd(sc_t, full_matrices=False)
+            shared_proj = torch.zeros(d, ns)
+            k_dims = min(d, ns)
+            shared_proj[:k_dims, :] = Vt[:k_dims, :] * (0.1 / (k_dims ** 0.5))
+            with torch.no_grad():
+                self.q.weight.data[:, d:d+ns] = shared_proj
+                self.k.weight.data[:, d:d+ns] = shared_proj
+        self.manifold_gate_logit = nn.Parameter(torch.tensor(-5.0))   # start OFF, learn to use manifold
+        self.nav_gate_logit = nn.Parameter(torch.tensor(-5.0))       # start OFF, learn to use navigation
+
+    def forward(self, h, scn, geo_velocity, mask):
+        B, S, D = h.shape
+        q_in = torch.cat([h, scn, geo_velocity], dim=-1)
+        q = self.q(q_in).view(B, S, self.n_heads, self.head_dim).transpose(1, 2)
+        k_in = torch.cat([h, scn], dim=-1)
+        k = self.k(k_in).view(B, S, self.n_heads, self.head_dim).transpose(1, 2)
+        v = self.v(h).view(B, S, self.n_heads, self.head_dim).transpose(1, 2)
+
+        scores = (q @ k.transpose(-2, -1)) * self.scale
+        manifold_w = 0.05 + 0.95 * torch.sigmoid(self.manifold_gate_logit)
+        proximity = torch.bmm(scn, scn.transpose(1, 2))
+        scores = scores + manifold_w * proximity.unsqueeze(1)
+        nav_w = 0.05 + 0.95 * torch.sigmoid(self.nav_gate_logit)
+        endpoint = F.normalize(scn + 0.4 * geo_velocity, dim=-1)
+        endpoint_sim = torch.bmm(endpoint, scn.transpose(1, 2))
+        navigation = proximity * endpoint_sim
+        scores = scores + nav_w * navigation.unsqueeze(1)
+        scores = scores.masked_fill(mask.unsqueeze(0).unsqueeze(1), -1e4)
+        scores = scores.clamp(-50, 50)
+        attn = F.softmax(scores, dim=-1)
+        out = (attn @ v).transpose(1, 2).contiguous().view(B, S, D)
+        return self.norm(h + self.out(out))
+
+
+class ProbeConvV18(nn.Module):
+    def __init__(self, spectral_coords, precomputed_neighbors, args):
+        super().__init__()
+        D = args.model_dim
+        NS = spectral_coords.shape[1]
+        V = args.vocab_size
+        N_HOPS = args.n_hops
+        SEQ_LEN = args.train_seq_len
+
+        self.D = D
+        self.N_HOPS = N_HOPS
+        self.V = V
+
+        self.register_buffer('sc', torch.tensor(spectral_coords, dtype=torch.float32))
+        self.register_buffer('static_nb', precomputed_neighbors)
+
+        self.init_proj = nn.Sequential(nn.Linear(NS, D), nn.GELU(), nn.Linear(D, D), nn.LayerNorm(D))
+        self.content_emb = nn.Embedding(V, D)
+        self.fuse = nn.Linear(D*2, D)
+        self.pos_emb = nn.Embedding(SEQ_LEN, D)
+
+        self.message_fn = MessageFn(D)
+        self.hop_cells = nn.ModuleList([HopCell(D, NS) for _ in range(N_HOPS)])
+        self.hop_routers = nn.ModuleList([EntropyGeoRouter(D, NS, args.k_nb, V) for _ in range(N_HOPS)])
+        self.hop_norms = nn.ModuleList([nn.LayerNorm(D) for _ in range(N_HOPS)])
+
+        self.output = nn.Linear(D, V)
+        self.manifold_attn = ManifoldGuidedAttention(D, NS, n_heads=args.n_attn_heads, spectral_coords=spectral_coords)
+        self.hop_emb = nn.Embedding(N_HOPS, D)
+        self.h_to_spec = nn.Linear(D, NS, bias=False)
+        nn.init.zeros_(self.h_to_spec.weight)
+
+        self.geo_mixers = nn.ModuleList([GeodesicMixer(D, NS, expansion=args.geo_mixer_expansion) for _ in range(N_HOPS)])
+        self.hop_post_norms = nn.ModuleList([nn.LayerNorm(D) for _ in range(N_HOPS)])
+
+        self.hop_residual_gate = nn.Sequential(
+            nn.Linear(1, 16), nn.GELU(), nn.Linear(16, 1), nn.Sigmoid()
+        )
+        nn.init.constant_(self.hop_residual_gate[2].bias, -3.0)  # suppress residual early, let hops dominate
+
+        self.register_buffer('causal_mask', torch.triu(torch.ones(SEQ_LEN, SEQ_LEN), diagonal=1).bool())
+
+    def compute_entropy(self, h):
+        with torch.no_grad():
+            logits = self.output(h)
+            log_p = F.log_softmax(logits, dim=-1)
+            p = log_p.exp()
+            H = -(p * log_p).sum(-1)
+        return H
+
+    def forward(self, input_ids, target_ids):
+        B, S = input_ids.shape
+        x = input_ids
+        sc = self.sc[x]
+        scn = F.normalize(self.sc, dim=-1)[x]
+        h = self.fuse(torch.cat([self.init_proj(sc), self.content_emb(x)], dim=-1))
+        h = h + self.pos_emb(torch.arange(S, device=x.device)).unsqueeze(0)
+        mask = self.causal_mask[:S, :S]
+
+        pos = torch.arange(S, device=x.device).float()
+        tau = 4.0
+        rec_w = torch.exp(-(pos.unsqueeze(0) - pos.unsqueeze(1)) / tau)
+        rec_w = rec_w.tril(-1)
+        rec_w = rec_w / (rec_w.sum(-1, keepdim=True) + 1e-8)
+        ctx_scn = torch.bmm(rec_w.unsqueeze(0).expand(B,-1,-1), scn)
+        geo_velocity = F.normalize(ctx_scn - scn, dim=-1)
+        geo_velocity = geo_velocity * (pos > 2).float().view(1,S,1)
+        ctx_conf = (pos.clamp(max=16) / 16.0).view(1,S,1).expand(B,-1,-1)
+
+        h_init = h
+
+        prev_velocity = None
+        for hop in range(self.N_HOPS):
+            H = self.compute_entropy(h)
+            if prev_velocity is not None:
+                h = parallel_transport(h, prev_velocity, geo_velocity, self.D, blend=0.2)
+            _, out = self.hop_cells[hop](h, sc, torch.zeros_like(h), self.message_fn)
+            inc = self.hop_routers[hop](
+                out, h, x, scn, mask,
+                self.static_nb, geo_velocity, ctx_conf, H)
+            h, _ = self.hop_cells[hop](h, sc, inc, self.message_fn)
+            h = self.hop_norms[hop](h)
+            h_spec = self.h_to_spec(h)
+            prev_velocity = geo_velocity  # PT fix: save BEFORE h_to_spec updates velocity
+            dynamic_scn = F.normalize(scn + h_spec, dim=-1)
+            ctx_dyn = torch.bmm(rec_w.unsqueeze(0).expand(B,-1,-1), dynamic_scn)
+            geo_velocity = F.normalize(ctx_dyn - dynamic_scn, dim=-1)
+            geo_velocity = geo_velocity * (pos > 2).float().view(1,S,1)
+            h_attn = h + self.hop_emb(torch.tensor(hop, device=x.device))
+            h = self.manifold_attn(h_attn, scn, geo_velocity, mask)
+            h_before_mix = h
+            h_mixed = self.geo_mixers[hop](h, sc)
+            delta = h_mixed - h_before_mix
+            cap = 0.2 * h_before_mix.norm(dim=-1, keepdim=True).clamp_min(1e-6)
+            delta_norm = delta.norm(dim=-1, keepdim=True).clamp_min(1e-6)
+            scale = (cap / delta_norm).clamp(max=1.0)
+            h = h_before_mix + delta * scale
+            h = self.hop_post_norms[hop](h)
+
+        h_spec_final = self.h_to_spec(h)
+        h_spec_init = self.h_to_spec(h_init)
+        displacement = (h_spec_final - h_spec_init).norm(dim=-1, keepdim=True)
+        gate = 0.05 + 0.95 * self.hop_residual_gate(displacement)
+        h = h + gate * h_init
+
+        logits = self.output(h).reshape(-1, self.V)
+        targets = target_ids.reshape(-1)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    try:
+        code = Path(__file__).read_text(encoding="utf-8")
+    except Exception:
+        code = "best_pt_fix"
+    args = Hyperparameters()
+
+    # Distributed setup
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    grad_accum_steps = 1
+    grad_scale = 1.0 / grad_accum_steps
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg, console=True):
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+
+    # Tokenizer + validation
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+
+    # Build manifold (distributed across all GPUs)
+    log0(f"\n=== Building Manifold (distributed, {world_size} GPUs) ===")
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    spectral_coords = build_manifold_distributed(args, rank, world_size, device, log0)
+    NS = spectral_coords.shape[1]
+
+    # Precompute KNN neighbors
+    scn_f = spectral_coords / (np.linalg.norm(spectral_coords, axis=1, keepdims=True) + 1e-8)
+    sim = scn_f @ scn_f.T
+    np.fill_diagonal(sim, -1)
+    precomputed_neighbors = torch.tensor(
+        np.argsort(-sim, axis=1)[:, :args.k_nb].astype(np.int32), dtype=torch.int32)
+    log0(f"  KNN: {precomputed_neighbors.shape}")
+
+    # Model
+    log0(f"\n=== Model Setup ===")
+    base_model = ProbeConvV18(spectral_coords, precomputed_neighbors, args).to(device)
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"  V18 params: {n_params:,}")
+    log0(f"  D={args.model_dim} NS={NS} hops={args.n_hops}")
+
+    # Deterministic torch.compile: disable autotuning to prevent non-deterministic kernel selection
+    compiled_model = torch.compile(base_model, dynamic=False)
+    model = compiled_model
+
+    # Broadcast all params from rank 0 to ensure identical init
+    if distributed:
+        for p in base_model.parameters():
+            dist.broadcast(p.data, src=0)
+        for name, buf in base_model.named_buffers():
+            if buf.is_floating_point():
+                dist.broadcast(buf, src=0)
+
+    # Optimizer — plain AdamW (delta cap + gate floors handle spike prevention)
+    opt = torch.optim.AdamW(base_model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+
+    # Data loader — each GPU loads full batch size independently (different data)
+    # Rank 0 needs full 65K for hop gradients (same as single-GPU Colab)
+    # Rank 1 sees different 65K, contributes averaged gradients for non-hop params only
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+    train_loader.world_size = 1  # each GPU loads full batch, not batch/world_size
+    # Advance rank 1's stream so GPUs read different data
+    if rank > 0:
+        train_loader.stream.take(rank * 1000000)  # skip ahead so ranks see different shards
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_schedule(step, elapsed_ms):
+        """Cosine decay to 10%, hold, then warmdown to 0."""
+        # Phase 1: warmup (steps 0-100)
+        warmup = min(step / 100, 1.0)
+        # Phase 2: cosine decay from 1.0 to 0.1 over steps 0-3400
+        if step < 3400:
+            cosine = 0.5 * (1.0 + math.cos(math.pi * step / 3400))
+            decay = 0.1 + 0.9 * cosine  # decays from 1.0 → 0.1
+        # Phase 3: hold at 10% for steps 3400-5500
+        elif step < 5500:
+            decay = 0.1
+        # Phase 4: linear warmdown from 10% → 0 over steps 5500-6100
+        else:
+            decay = 0.1 * max(1.0 - (step - 5500) / 600.0, 0.0)
+        return warmup * decay
+
+    # Warmup (torch.compile warmup)
+    if args.warmup_steps > 0:
+        initial_state = {n: t.detach().cpu().clone() for n, t in base_model.state_dict().items()}
+        initial_opt = copy.deepcopy(opt.state_dict())
+        model.train()
+        for ws in range(args.warmup_steps):
+            opt.zero_grad(set_to_none=True)
+            for micro in range(grad_accum_steps):
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                    loss = model(x, y)
+                (loss * grad_scale).backward()
+            opt.step()
+            if ws % 10 == 0 or ws + 1 == args.warmup_steps:
+                log0(f"  warmup step {ws+1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_state, strict=True)
+        opt.load_state_dict(initial_opt)
+        opt.zero_grad(set_to_none=True)
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+        train_loader.world_size = 1
+        if rank > 0:
+            train_loader.stream.take(rank * 1000000)
+
+    # Main training loop
+    log0(f"\n=== Training ===")
+    training_time_ms = 0.0
+    stop_after_step = None
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms step:{step}")
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_schedule(step, elapsed_ms)
+        for group in opt.param_groups:
+            group["lr"] = args.lr * scale
+
+        opt.zero_grad(set_to_none=True)
+        train_loss = torch.zeros((), device=device)
+        for micro in range(grad_accum_steps):
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        # Selective gradient strategy:
+        # Hop-chain params use rank 0's LOCAL gradient — averaging kills hop specialization.
+        # Everything else uses averaged gradient — more data helps embeddings/output.
+        if distributed:
+            for name, param in base_model.named_parameters():
+                if param.grad is None:
+                    continue
+                is_hop_param = any(k in name for k in [
+                    'hop_cells', 'hop_routers', 'geo_mixers', 'manifold_attn',
+                    'message_fn', 'h_to_spec', 'hop_norms', 'hop_post_norms',
+                    'hop_emb', 'hop_residual_gate'
+                ])
+                if not is_hop_param:
+                    # Average gradient across GPUs for non-hop params
+                    dist.reduce(param.grad, dst=0, op=dist.ReduceOp.SUM)
+                    if rank == 0:
+                        param.grad.div_(world_size)
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+
+        if not distributed or rank == 0:
+            opt.step()
+
+        # EMA weight accumulation for smoother quantization
+        # Start after hop params freeze (~step 3000) to capture stable weight averages
+        if step >= 3000:
+            if not hasattr(base_model, '_ema_state'):
+                base_model._ema_state = {n: p.data.clone() for n, p in base_model.named_parameters()}
+                base_model._ema_count = 1
+            else:
+                decay = 0.999
+                for n, p in base_model.named_parameters():
+                    base_model._ema_state[n].mul_(decay).add_(p.data, alpha=1 - decay)
+                base_model._ema_count += 1
+
+        if distributed:
+            for param in base_model.parameters():
+                dist.broadcast(param.data, src=0)
+
+        # Track best model after step 3000 — save EMA snapshot (smoother = better quantization)
+        if step >= 3000 and (not distributed or rank == 0):
+            current_loss = train_loss.item()
+            if not hasattr(base_model, '_best_loss') or current_loss < base_model._best_loss:
+                base_model._best_loss = current_loss
+                base_model._best_step = step
+                # Save EMA weights at this point (smoother than raw weights, quantizes better)
+                if hasattr(base_model, '_ema_state'):
+                    base_model._best_state = {n: v.clone() for n, v in base_model._ema_state.items()}
+                else:
+                    base_model._best_state = {n: p.data.clone() for n, p in base_model.named_parameters()}
+                log0(f"  NEW BEST step:{step} loss:{current_loss:.4f} (saved EMA snapshot)")
+
+        step += 1
+        # Diagnostics every 200 steps — outside compiled forward
+        if step % 200 == 0:
+            with torch.no_grad():
+                gb = [base_model.hop_routers[i].geo_gate.bias.item() for i in range(4)]
+                gw = [base_model.hop_routers[i].geo_gate.weight.norm().item() for i in range(4)]
+                mg = base_model.manifold_attn.manifold_gate_logit.item()
+                ng = base_model.manifold_attn.nav_gate_logit.item()
+                spec_w = base_model.h_to_spec.weight.norm().item()
+                spec_max = base_model.h_to_spec.weight.abs().max().item()
+                mix_down = [base_model.geo_mixers[i].down.weight.norm().item() for i in range(4)]
+                mix_up = [base_model.geo_mixers[i].up.weight.norm().item() for i in range(4)]
+                mix_sg = [base_model.geo_mixers[i].spec_gate[0].weight.norm().item() for i in range(4)]
+                attn_out = base_model.manifold_attn.out.weight.norm().item()
+                attn_v = base_model.manifold_attn.v.weight.norm().item()
+                cell_norms = [base_model.hop_cells[i].update[0].weight.norm().item() for i in range(4)]
+                msg_w = base_model.message_fn.net[0].weight.norm().item()
+                resid_b = base_model.hop_residual_gate[2].bias.item()
+                hop_emb_n = base_model.hop_emb.weight.norm().item()
+            log0(
+                f"  DIAG s{step} "
+                f"gb:[{gb[0]:.2f},{gb[1]:.2f},{gb[2]:.2f},{gb[3]:.2f}] "
+                f"gw:[{gw[0]:.2f},{gw[1]:.2f},{gw[2]:.2f},{gw[3]:.2f}] "
+                f"mg:{mg:.2f} ng:{ng:.2f} "
+                f"spec:{spec_w:.1f} smax:{spec_max:.3f} "
+                f"mix_d:[{mix_down[0]:.1f},{mix_down[1]:.1f},{mix_down[2]:.1f},{mix_down[3]:.1f}] "
+                f"mix_u:[{mix_up[0]:.1f},{mix_up[1]:.1f},{mix_up[2]:.1f},{mix_up[3]:.1f}] "
+                f"mix_sg:[{mix_sg[0]:.2f},{mix_sg[1]:.2f},{mix_sg[2]:.2f},{mix_sg[3]:.2f}] "
+                f"attn_o:{attn_out:.2f} attn_v:{attn_v:.2f} "
+                f"cell:[{cell_norms[0]:.1f},{cell_norms[1]:.1f},{cell_norms[2]:.1f},{cell_norms[3]:.1f}] "
+                f"msg:{msg_w:.1f} rb:{resid_b:.2f} hemb:{hop_emb_n:.1f}"
+            )
+        approx_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        if args.train_log_every > 0 and (step <= 10 or step % args.train_log_every == 0):
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_ms:.0f}ms step_avg:{approx_ms/step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            cap_t = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(cap_t, op=dist.ReduceOp.MAX)
+            reached_cap = bool(cap_t.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Serialization + roundtrip validation
+    # Priority: best model (lowest loss after step 5000) > EMA > final weights
+    if hasattr(base_model, '_best_state'):
+        log0(f"  Using BEST model for serialization (step:{base_model._best_step} loss:{base_model._best_loss:.4f})")
+        with torch.no_grad():
+            for n, p in base_model.named_parameters():
+                p.data.copy_(base_model._best_state[n])
+    elif hasattr(base_model, '_ema_state'):
+        with torch.no_grad():
+            for n, p in base_model.named_parameters():
+                p.data.copy_(base_model._ema_state[n])
+        log0(f"  Using EMA weights for serialization (decay=0.999, {base_model._ema_count} steps)")
+
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+
+    quant_obj, quant_stats = quantize_state_dict_int8(base_model.state_dict(), quant_levels=args.quant_levels)
+    quant_buf = io.BytesIO()
+    torch.save(quant_obj, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    quant_blob = zlib.compress(quant_raw, level=9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        ratio = quant_stats["baseline_tensor_bytes"] / max(quant_stats["int8_payload_bytes"], 1)
+        log0(
+            f"Serialized model int8+zlib: {quant_file_bytes} bytes "
+            f"(payload:{quant_stats['int8_payload_bytes']} ratio:{ratio:.2f}x)"
+        )
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    quant_state = torch.load(io.BytesIO(zlib.decompress(quant_blob_disk)), map_location="cpu")
+    base_model.load_state_dict(dequantize_state_dict_int8(quant_state), strict=True)
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    q_val_loss, q_val_bpb = eval_val(
+        args, model, rank, world_size, device, grad_accum_steps,
+        val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+    )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+
+
+
diff --git a/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed27.log b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed27.log
new file mode 100644
index 000000000..3d0b05a74
--- /dev/null
+++ b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed27.log
@@ -0,0 +1,155 @@
+====================================================================================================
+
+=== Building Manifold (distributed, 1 GPUs) ===
+  Rank 0: processing 80/80 shards for manifold
+    rank 0 shard 1/80 (1s)
+    rank 0 shard 6/80 (6s)
+    rank 0 shard 11/80 (11s)
+    rank 0 shard 16/80 (16s)
+    rank 0 shard 21/80 (21s)
+    rank 0 shard 26/80 (26s)
+    rank 0 shard 31/80 (31s)
+    rank 0 shard 36/80 (36s)
+    rank 0 shard 41/80 (42s)
+    rank 0 shard 46/80 (47s)
+    rank 0 shard 51/80 (52s)
+    rank 0 shard 56/80 (58s)
+    rank 0 shard 61/80 (61s)
+    rank 0 shard 66/80 (63s)
+    rank 0 shard 71/80 (66s)
+    rank 0 shard 76/80 (68s)
+  All-reducing manifold stats...
+  Stats accumulated (70s)
+  Force 1: 30,405 spring pairs
+  Force 2: torsion done
+  Force 3: entropic mass done
+  Force 4: 39,052 directed pairs
+  Force 5: syntactic done
+
+  Physics simulation (300D, 5000 steps)...
+    physics step 0 (0s)
+    physics step 1000 (8s)
+    physics step 2000 (16s)
+    physics step 3000 (24s)
+    physics step 4000 (33s)
+  Physics done (42s)
+  Computing Hessian...
+  Hessian: (1024, 256), eigenvalues: 1302.10 → 6.11
+  Manifold ready: (1024, 320) (114s total)
+  KNN: torch.Size([1024, 12])
+
+=== Model Setup ===
+  V18 params: 20,705,679
+  D=500 NS=320 hops=4
+  warmup step 1/20
+  warmup step 11/20
+  warmup step 20/20
+
+=== Training ===
+step:0/50000 val_loss:7.1643 val_bpb:4.2431 train_time:0ms
+step:1/50000 train_loss:7.1640 train_time:53ms step_avg:53.04ms
+step:2/50000 train_loss:7.1629 train_time:137ms step_avg:68.45ms
+step:3/50000 train_loss:7.1178 train_time:237ms step_avg:78.87ms
+step:4/50000 train_loss:7.0163 train_time:336ms step_avg:84.08ms
+step:5/50000 train_loss:6.8846 train_time:436ms step_avg:87.21ms
+step:6/50000 train_loss:6.7035 train_time:536ms step_avg:89.35ms
+step:7/50000 train_loss:6.5403 train_time:636ms step_avg:90.82ms
+step:8/50000 train_loss:6.4198 train_time:737ms step_avg:92.12ms
+step:9/50000 train_loss:6.3152 train_time:836ms step_avg:92.90ms
+step:10/50000 train_loss:6.2303 train_time:936ms step_avg:93.57ms
+  DIAG s200 gb:[-4.07,-3.97,-3.95,-3.98] gw:[0.83,0.70,1.04,0.86] mg:-4.91 ng:-5.40 spec:6.3 smax:0.116 mix_d:[13.9,11.7,13.5,18.4] mix_u:[27.6,26.4,28.5,33.6] mix_sg:[22.43,20.98,34.94,45.39] attn_o:9.39 attn_v:23.31 cell:[50.6,39.4,36.3,38.3] msg:20.3 rb:-2.86 hemb:42.4
+step:200/50000 train_loss:3.9562 train_time:19508ms step_avg:97.54ms
+  DIAG s400 gb:[-4.05,-3.93,-3.89,-3.92] gw:[0.83,0.77,1.15,1.22] mg:-5.08 ng:-5.68 spec:8.8 smax:0.217 mix_d:[19.3,21.9,28.7,30.8] mix_u:[33.1,44.7,49.9,51.4] mix_sg:[31.93,52.91,72.99,79.54] attn_o:13.89 attn_v:25.67 cell:[61.6,53.6,51.2,65.9] msg:24.6 rb:-2.78 hemb:40.9
+step:400/50000 train_loss:3.5292 train_time:38986ms step_avg:97.46ms
+  DIAG s600 gb:[-4.01,-3.90,-3.85,-3.87] gw:[0.86,0.74,1.20,1.26] mg:-5.11 ng:-5.79 spec:10.7 smax:0.309 mix_d:[25.3,31.2,38.9,38.0] mix_u:[42.8,61.1,64.2,61.1] mix_sg:[47.98,78.98,93.57,96.58] attn_o:18.50 attn_v:28.92 cell:[68.7,70.3,64.9,82.9] msg:28.6 rb:-2.73 hemb:39.7
+step:600/50000 train_loss:3.0700 train_time:58467ms step_avg:97.45ms
+  DIAG s800 gb:[-4.00,-3.86,-3.82,-3.84] gw:[0.93,0.88,1.20,1.23] mg:-5.03 ng:-5.80 spec:13.5 smax:0.464 mix_d:[32.1,37.3,45.1,42.3] mix_u:[53.8,71.0,72.9,66.8] mix_sg:[65.83,94.86,106.38,107.52] attn_o:23.77 attn_v:32.98 cell:[73.9,82.6,72.8,93.2] msg:32.3 rb:-2.69 hemb:38.8
+step:800/50000 train_loss:2.5963 train_time:77962ms step_avg:97.45ms
+  DIAG s1000 gb:[-3.98,-3.84,-3.79,-3.80] gw:[0.92,0.95,1.31,1.26] mg:-4.89 ng:-5.69 spec:15.6 smax:0.571 mix_d:[37.5,41.7,49.4,45.4] mix_u:[61.9,78.0,79.2,70.5] mix_sg:[81.32,106.36,114.76,115.01] attn_o:28.47 attn_v:36.35 cell:[77.2,90.2,77.7,100.0] msg:35.3 rb:-2.65 hemb:38.2
+step:1000/50000 train_loss:2.2142 train_time:97455ms step_avg:97.46ms
+  DIAG s1200 gb:[-3.96,-3.81,-3.77,-3.75] gw:[0.93,0.97,1.30,1.34] mg:-4.75 ng:-5.55 spec:17.0 smax:0.606 mix_d:[41.3,44.4,52.5,47.6] mix_u:[67.7,82.3,83.8,73.2] mix_sg:[92.75,114.36,120.34,120.15] attn_o:31.32 attn_v:38.30 cell:[81.2,95.0,80.6,105.0] msg:38.1 rb:-2.61 hemb:37.8
+step:1200/50000 train_loss:1.7835 train_time:116983ms step_avg:97.49ms
+  DIAG s1400 gb:[-3.94,-3.79,-3.75,-3.71] gw:[0.93,1.02,1.46,1.44] mg:-4.63 ng:-5.41 spec:19.0 smax:0.629 mix_d:[45.0,46.8,54.7,49.3] mix_u:[72.9,86.6,87.4,75.3] mix_sg:[101.73,121.05,123.89,123.80] attn_o:33.28 attn_v:39.63 cell:[83.5,99.4,83.7,109.3] msg:39.6 rb:-2.58 hemb:37.4
+step:1400/50000 train_loss:1.4676 train_time:136470ms step_avg:97.48ms
+  DIAG s1600 gb:[-3.92,-3.76,-3.72,-3.67] gw:[0.94,1.06,1.48,1.51] mg:-4.54 ng:-5.29 spec:19.9 smax:0.642 mix_d:[47.0,48.1,56.2,50.6] mix_u:[75.6,88.6,90.0,76.9] mix_sg:[107.74,124.80,126.35,126.43] attn_o:34.67 attn_v:40.51 cell:[84.9,101.3,85.3,112.1] msg:40.5 rb:-2.55 hemb:37.1
+step:1600/50000 train_loss:1.2761 train_time:155968ms step_avg:97.48ms
+  DIAG s1800 gb:[-3.90,-3.73,-3.70,-3.64] gw:[1.01,1.00,1.44,1.52] mg:-4.47 ng:-5.19 spec:20.5 smax:0.654 mix_d:[48.3,48.9,57.2,51.7] mix_u:[77.2,89.7,91.7,78.0] mix_sg:[112.30,127.36,127.66,128.22] attn_o:35.74 attn_v:41.13 cell:[85.6,102.4,85.6,114.0] msg:41.0 rb:-2.52 hemb:36.9
+step:1800/50000 train_loss:1.1772 train_time:175438ms step_avg:97.47ms
+  DIAG s2000 gb:[-3.89,-3.72,-3.68,-3.61] gw:[1.01,0.98,1.44,1.56] mg:-4.42 ng:-5.11 spec:21.0 smax:0.663 mix_d:[49.2,49.5,57.8,52.5] mix_u:[78.2,90.3,92.8,78.8] mix_sg:[115.52,128.88,128.31,129.29] attn_o:36.51 attn_v:41.58 cell:[86.0,103.0,85.8,115.3] msg:41.4 rb:-2.50 hemb:36.7
+step:2000/50000 train_loss:0.9687 train_time:194908ms step_avg:97.45ms
+  DIAG s2200 gb:[-3.88,-3.70,-3.67,-3.58] gw:[1.01,0.95,1.44,1.61] mg:-4.39 ng:-5.06 spec:21.5 smax:0.672 mix_d:[50.0,50.0,58.3,53.1] mix_u:[79.2,91.0,93.6,79.2] mix_sg:[117.94,130.03,128.48,129.95] attn_o:37.05 attn_v:41.89 cell:[86.4,103.6,86.0,116.3] msg:41.7 rb:-2.49 hemb:36.6
+step:2200/50000 train_loss:0.9166 train_time:214386ms step_avg:97.45ms
+  DIAG s2400 gb:[-3.87,-3.68,-3.65,-3.55] gw:[1.00,0.92,1.45,1.64] mg:-4.37 ng:-5.02 spec:21.8 smax:0.675 mix_d:[50.4,50.3,58.5,53.5] mix_u:[79.6,91.2,94.1,79.4] mix_sg:[119.43,130.55,128.41,130.27] attn_o:37.40 attn_v:42.09 cell:[86.5,103.8,86.0,116.8] msg:41.8 rb:-2.47 hemb:36.4
+step:2400/50000 train_loss:0.7785 train_time:233856ms step_avg:97.44ms
+  DIAG s2600 gb:[-3.86,-3.67,-3.65,-3.53] gw:[1.00,0.92,1.44,1.67] mg:-4.37 ng:-4.99 spec:22.0 smax:0.675 mix_d:[50.7,50.5,58.7,53.7] mix_u:[79.8,91.3,94.4,79.5] mix_sg:[120.47,130.85,128.23,130.41] attn_o:37.64 attn_v:42.24 cell:[86.6,103.9,86.0,117.1] msg:41.9 rb:-2.46 hemb:36.4
+step:2600/50000 train_loss:0.7099 train_time:253326ms step_avg:97.43ms
+  DIAG s2800 gb:[-3.86,-3.66,-3.64,-3.52] gw:[0.99,0.92,1.44,1.70] mg:-4.37 ng:-4.98 spec:22.1 smax:0.675 mix_d:[50.9,50.6,58.8,53.9] mix_u:[80.0,91.3,94.6,79.5] mix_sg:[121.16,130.95,127.99,130.43] attn_o:37.80 attn_v:42.34 cell:[86.7,103.9,85.9,117.4] msg:42.0 rb:-2.45 hemb:36.3
+step:2800/50000 train_loss:0.5979 train_time:272803ms step_avg:97.43ms
+  DIAG s3000 gb:[-3.85,-3.65,-3.63,-3.50] gw:[0.99,0.92,1.44,1.74] mg:-4.37 ng:-4.97 spec:22.2 smax:0.674 mix_d:[51.0,50.7,58.8,54.0] mix_u:[80.0,91.3,94.7,79.5] mix_sg:[121.61,130.99,127.74,130.40] attn_o:37.90 attn_v:42.40 cell:[86.7,103.9,85.9,117.5] msg:42.0 rb:-2.44 hemb:36.2
+step:3000/50000 train_loss:0.6305 train_time:292283ms step_avg:97.43ms
+  NEW BEST step:3000 loss:0.6028 (saved EMA snapshot)
+  NEW BEST step:3002 loss:0.5866 (saved EMA snapshot)
+  NEW BEST step:3006 loss:0.5723 (saved EMA snapshot)
+  NEW BEST step:3008 loss:0.5261 (saved EMA snapshot)
+  NEW BEST step:3009 loss:0.5230 (saved EMA snapshot)
+  NEW BEST step:3106 loss:0.5177 (saved EMA snapshot)
+  NEW BEST step:3120 loss:0.5093 (saved EMA snapshot)
+  NEW BEST step:3122 loss:0.5025 (saved EMA snapshot)
+  NEW BEST step:3141 loss:0.4976 (saved EMA snapshot)
+  NEW BEST step:3173 loss:0.4789 (saved EMA snapshot)
+  NEW BEST step:3184 loss:0.4508 (saved EMA snapshot)
+  DIAG s3200 gb:[-3.85,-3.65,-3.63,-3.49] gw:[0.98,0.92,1.44,1.76] mg:-4.38 ng:-4.96 spec:22.2 smax:0.674 mix_d:[51.1,50.8,58.9,54.1] mix_u:[80.0,91.3,94.8,79.5] mix_sg:[121.93,130.98,127.49,130.34] attn_o:37.98 attn_v:42.44 cell:[86.8,103.9,85.8,117.5] msg:42.0 rb:-2.44 hemb:36.2
+step:3200/50000 train_loss:0.4858 train_time:312372ms step_avg:97.62ms
+  DIAG s3400 gb:[-3.85,-3.64,-3.62,-3.48] gw:[0.98,0.92,1.44,1.78] mg:-4.39 ng:-4.96 spec:22.3 smax:0.675 mix_d:[51.1,50.8,58.9,54.1] mix_u:[80.0,91.2,94.9,79.4] mix_sg:[122.14,130.94,127.28,130.28] attn_o:38.02 attn_v:42.47 cell:[86.8,103.9,85.8,117.6] msg:42.0 rb:-2.43 hemb:36.2
+step:3400/50000 train_loss:0.4749 train_time:332445ms step_avg:97.78ms
+  NEW BEST step:3414 loss:0.4455 (saved EMA snapshot)
+  NEW BEST step:3416 loss:0.4434 (saved EMA snapshot)
+  NEW BEST step:3419 loss:0.4170 (saved EMA snapshot)
+  NEW BEST step:3432 loss:0.4086 (saved EMA snapshot)
+  DIAG s3600 gb:[-3.84,-3.63,-3.62,-3.47] gw:[0.98,0.92,1.44,1.78] mg:-4.39 ng:-4.96 spec:22.3 smax:0.674 mix_d:[51.2,50.9,58.9,54.2] mix_u:[80.0,91.2,94.9,79.4] mix_sg:[122.35,130.91,127.08,130.23] attn_o:38.07 attn_v:42.50 cell:[86.8,103.8,85.8,117.7] msg:42.0 rb:-2.43 hemb:36.2
+step:3600/50000 train_loss:0.5213 train_time:352505ms step_avg:97.92ms
+  NEW BEST step:3756 loss:0.3859 (saved EMA snapshot)
+  DIAG s3800 gb:[-3.84,-3.63,-3.62,-3.46] gw:[0.98,0.92,1.44,1.81] mg:-4.40 ng:-4.96 spec:22.3 smax:0.674 mix_d:[51.2,50.9,59.0,54.3] mix_u:[80.0,91.2,95.0,79.4] mix_sg:[122.55,130.87,126.89,130.19] attn_o:38.11 attn_v:42.53 cell:[86.8,103.8,85.7,117.7] msg:42.0 rb:-2.42 hemb:36.1
+step:3800/50000 train_loss:0.4812 train_time:372570ms step_avg:98.04ms
+  DIAG s4000 gb:[-3.84,-3.62,-3.61,-3.45] gw:[0.98,0.93,1.44,1.83] mg:-4.41 ng:-4.96 spec:22.4 smax:0.673 mix_d:[51.2,51.0,59.0,54.3] mix_u:[80.0,91.1,95.0,79.3] mix_sg:[122.70,130.82,126.72,130.12] attn_o:38.14 attn_v:42.55 cell:[86.8,103.8,85.7,117.8] msg:42.0 rb:-2.42 hemb:36.1
+step:4000/50000 train_loss:0.4563 train_time:392589ms step_avg:98.15ms
+  NEW BEST step:4094 loss:0.3751 (saved EMA snapshot)
+  NEW BEST step:4097 loss:0.3668 (saved EMA snapshot)
+  DIAG s4200 gb:[-3.83,-3.62,-3.61,-3.44] gw:[0.98,0.93,1.44,1.83] mg:-4.41 ng:-4.96 spec:22.4 smax:0.673 mix_d:[51.3,51.0,59.0,54.4] mix_u:[80.0,91.1,95.1,79.3] mix_sg:[122.86,130.76,126.53,130.05] attn_o:38.17 attn_v:42.56 cell:[86.8,103.8,85.7,117.8] msg:42.0 rb:-2.41 hemb:36.1
+step:4200/50000 train_loss:0.4480 train_time:412574ms step_avg:98.23ms
+  NEW BEST step:4306 loss:0.3323 (saved EMA snapshot)
+  DIAG s4400 gb:[-3.83,-3.61,-3.60,-3.43] gw:[0.98,0.94,1.44,1.85] mg:-4.42 ng:-4.96 spec:22.4 smax:0.673 mix_d:[51.3,51.0,59.1,54.5] mix_u:[80.0,91.0,95.1,79.2] mix_sg:[123.03,130.70,126.35,130.01] attn_o:38.20 attn_v:42.58 cell:[86.9,103.8,85.7,117.9] msg:42.0 rb:-2.41 hemb:36.0
+step:4400/50000 train_loss:0.4744 train_time:432559ms step_avg:98.31ms
+  DIAG s4600 gb:[-3.83,-3.61,-3.60,-3.42] gw:[0.98,0.95,1.44,1.87] mg:-4.42 ng:-4.96 spec:22.5 smax:0.674 mix_d:[51.4,51.1,59.1,54.5] mix_u:[80.0,91.0,95.1,79.2] mix_sg:[123.21,130.65,126.18,129.94] attn_o:38.23 attn_v:42.60 cell:[86.9,103.8,85.6,118.0] msg:42.0 rb:-2.40 hemb:36.0
+step:4600/50000 train_loss:0.4237 train_time:452627ms step_avg:98.40ms
+  DIAG s4800 gb:[-3.82,-3.60,-3.60,-3.41] gw:[0.98,0.95,1.44,1.89] mg:-4.43 ng:-4.96 spec:22.5 smax:0.672 mix_d:[51.4,51.1,59.2,54.6] mix_u:[80.0,90.9,95.2,79.2] mix_sg:[123.38,130.59,126.02,129.89] attn_o:38.26 attn_v:42.62 cell:[86.9,103.8,85.6,118.1] msg:42.0 rb:-2.40 hemb:36.0
+step:4800/50000 train_loss:0.4266 train_time:472701ms step_avg:98.48ms
+  DIAG s5000 gb:[-3.82,-3.60,-3.59,-3.41] gw:[0.98,0.96,1.44,1.89] mg:-4.44 ng:-4.95 spec:22.6 smax:0.672 mix_d:[51.5,51.2,59.2,54.7] mix_u:[80.0,90.9,95.2,79.2] mix_sg:[123.54,130.54,125.86,129.83] attn_o:38.29 attn_v:42.63 cell:[87.0,103.8,85.6,118.1] msg:42.1 rb:-2.39 hemb:35.9
+step:5000/50000 train_loss:0.4430 train_time:492783ms step_avg:98.56ms
+  DIAG s5200 gb:[-3.82,-3.59,-3.59,-3.40] gw:[0.97,0.97,1.44,1.91] mg:-4.44 ng:-4.95 spec:22.6 smax:0.671 mix_d:[51.5,51.2,59.3,54.8] mix_u:[80.0,90.9,95.3,79.1] mix_sg:[123.71,130.47,125.70,129.78] attn_o:38.32 attn_v:42.65 cell:[87.0,103.9,85.6,118.2] msg:42.1 rb:-2.39 hemb:35.9
+step:5200/50000 train_loss:0.3668 train_time:512900ms step_avg:98.63ms
+  NEW BEST step:5239 loss:0.3249 (saved EMA snapshot)
+  DIAG s5400 gb:[-3.81,-3.59,-3.59,-3.39] gw:[0.97,0.97,1.44,1.93] mg:-4.45 ng:-4.95 spec:22.6 smax:0.671 mix_d:[51.6,51.2,59.3,54.9] mix_u:[79.9,90.8,95.3,79.1] mix_sg:[123.82,130.41,125.55,129.71] attn_o:38.34 attn_v:42.66 cell:[87.1,103.9,85.6,118.3] msg:42.1 rb:-2.38 hemb:35.9
+step:5400/50000 train_loss:0.3684 train_time:532998ms step_avg:98.70ms
+  NEW BEST step:5417 loss:0.3223 (saved EMA snapshot)
+  DIAG s5600 gb:[-3.81,-3.59,-3.58,-3.38] gw:[0.97,0.98,1.44,1.94] mg:-4.45 ng:-4.95 spec:22.7 smax:0.670 mix_d:[51.6,51.3,59.4,55.0] mix_u:[79.9,90.8,95.3,79.1] mix_sg:[123.98,130.34,125.40,129.65] attn_o:38.37 attn_v:42.68 cell:[87.1,103.9,85.6,118.4] msg:42.1 rb:-2.38 hemb:35.8
+step:5600/50000 train_loss:0.5657 train_time:553070ms step_avg:98.76ms
+  NEW BEST step:5701 loss:0.3189 (saved EMA snapshot)
+  NEW BEST step:5771 loss:0.3135 (saved EMA snapshot)
+  DIAG s5800 gb:[-3.81,-3.58,-3.58,-3.37] gw:[0.97,0.99,1.44,1.96] mg:-4.46 ng:-4.95 spec:22.7 smax:0.669 mix_d:[51.6,51.3,59.4,55.1] mix_u:[79.9,90.7,95.4,79.0] mix_sg:[124.08,130.29,125.27,129.60] attn_o:38.39 attn_v:42.70 cell:[87.2,103.9,85.6,118.4] msg:42.1 rb:-2.37 hemb:35.8
+step:5800/50000 train_loss:0.3832 train_time:573177ms step_avg:98.82ms
+  NEW BEST step:5878 loss:0.3097 (saved EMA snapshot)
+  NEW BEST step:5879 loss:0.2686 (saved EMA snapshot)
+  DIAG s6000 gb:[-3.81,-3.58,-3.58,-3.37] gw:[0.97,1.00,1.45,1.98] mg:-4.46 ng:-4.96 spec:22.7 smax:0.668 mix_d:[51.6,51.3,59.5,55.1] mix_u:[79.9,90.7,95.4,79.0] mix_sg:[124.12,130.25,125.18,129.56] attn_o:38.40 attn_v:42.71 cell:[87.2,103.9,85.6,118.5] msg:42.1 rb:-2.37 hemb:35.8
+step:6000/50000 train_loss:0.3158 train_time:593198ms step_avg:98.87ms
+step:6069/50000 val_loss:0.3143 val_bpb:0.1862 train_time:600087ms
+stopping_early: wallclock_cap train_time:600087ms step:6069
+peak memory: 23107 MiB reserved: 44196 MiB
+  Using BEST model for serialization (step:5879 loss:0.2686)
+Serialized model: 84235049 bytes
+Code size: 58477 bytes
+Serialized model int8+zlib: 15642855 bytes (payload:21230070 ratio:3.97x)
+Total submission size int8+zlib: 15701332 bytes
+final_int8_zlib_roundtrip val_loss:0.7334 val_bpb:0.4343 eval_time:34321ms
+final_int8_zlib_roundtrip_exact val_loss:0.73335938 val_bpb:0.43433670
+root@ca5f0ac3b6a0:/workspace/parameter-golf#
\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed42.log b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed42.log
new file mode 100644
index 000000000..16e712a98
--- /dev/null
+++ b/records/track_10min_16mb/2026-03-24_V18_ManifoldGuided_Sparsemax/train_seed42.log
@@ -0,0 +1,175 @@
+====================================================================================================
+
+=== Building Manifold (distributed, 1 GPUs) ===
+  Rank 0: processing 80/80 shards for manifold
+    rank 0 shard 1/80 (0s)
+    rank 0 shard 6/80 (3s)
+    rank 0 shard 11/80 (5s)
+    rank 0 shard 16/80 (8s)
+    rank 0 shard 21/80 (10s)
+    rank 0 shard 26/80 (13s)
+    rank 0 shard 31/80 (15s)
+    rank 0 shard 36/80 (18s)
+    rank 0 shard 41/80 (20s)
+    rank 0 shard 46/80 (23s)
+    rank 0 shard 51/80 (25s)
+    rank 0 shard 56/80 (28s)
+    rank 0 shard 61/80 (30s)
+    rank 0 shard 66/80 (33s)
+    rank 0 shard 71/80 (35s)
+    rank 0 shard 76/80 (38s)
+  All-reducing manifold stats...
+  Stats accumulated (40s)
+  Force 1: 30,405 spring pairs
+  Force 2: torsion done
+  Force 3: entropic mass done
+  Force 4: 39,052 directed pairs
+  Force 5: syntactic done
+
+  Physics simulation (300D, 5000 steps)...
+    physics step 0 (0s)
+    physics step 1000 (9s)
+    physics step 2000 (18s)
+    physics step 3000 (26s)
+    physics step 4000 (35s)
+  Physics done (45s)
+  Computing Hessian...
+  Hessian: (1024, 256), eigenvalues: 1302.31 → 6.10
+  Manifold ready: (1024, 320) (87s total)
+  KNN: torch.Size([1024, 12])
+
+=== Model Setup ===
+  V18 params: 20,705,679
+  D=500 NS=320 hops=4
+  warmup step 1/20
+  warmup step 11/20
+  warmup step 20/20
+
+=== Training ===
+step:0/50000 val_loss:7.0556 val_bpb:4.1787 train_time:0ms
+step:1/50000 train_loss:7.0529 train_time:53ms step_avg:53.00ms
+step:2/50000 train_loss:7.0528 train_time:137ms step_avg:68.50ms
+step:3/50000 train_loss:7.0014 train_time:237ms step_avg:79.09ms
+step:4/50000 train_loss:6.9099 train_time:338ms step_avg:84.58ms
+step:5/50000 train_loss:6.7818 train_time:440ms step_avg:87.92ms
+step:6/50000 train_loss:6.6128 train_time:545ms step_avg:90.89ms
+step:7/50000 train_loss:6.4758 train_time:643ms step_avg:91.83ms
+step:8/50000 train_loss:6.3919 train_time:743ms step_avg:92.88ms
+step:9/50000 train_loss:6.3011 train_time:844ms step_avg:93.79ms
+step:10/50000 train_loss:6.2205 train_time:945ms step_avg:94.48ms
+  DIAG s200 gb:[-4.05,-3.96,-3.95,-3.97] gw:[0.55,0.83,1.05,0.43] mg:-4.94 ng:-5.40 spec:8.3 smax:0.106 mix_d:[12.9,8.7,10.5,14.0] mix_u:[29.9,21.5,24.0,28.4] mix_sg:[22.71,14.66,27.28,34.98] attn_o:8.84 attn_v:23.26 cell:[47.7,39.7,33.3,32.9] msg:18.2 rb:-2.93 hemb:44.4
+step:200/50000 train_loss:4.0922 train_time:19539ms step_avg:97.70ms
+  DIAG s400 gb:[-4.05,-3.92,-3.88,-3.95] gw:[0.77,1.02,1.24,0.53] mg:-5.25 ng:-5.85 spec:10.4 smax:0.168 mix_d:[19.7,12.7,20.3,27.2] mix_u:[39.9,26.6,36.0,48.5] mix_sg:[35.59,27.03,60.26,76.59] attn_o:12.22 attn_v:24.77 cell:[68.8,54.7,46.8,52.8] msg:23.2 rb:-2.91 hemb:43.0
+step:400/50000 train_loss:3.8276 train_time:39060ms step_avg:97.65ms
+  DIAG s600 gb:[-4.06,-3.89,-3.84,-3.92] gw:[0.94,1.13,1.26,0.61] mg:-5.50 ng:-6.16 spec:12.6 smax:0.262 mix_d:[24.5,16.5,29.5,36.9] mix_u:[46.7,31.5,48.0,63.0] mix_sg:[43.59,37.53,87.51,102.44] attn_o:15.53 attn_v:27.13 cell:[82.9,60.3,52.8,67.8] msg:26.7 rb:-2.94 hemb:41.7
+step:600/50000 train_loss:3.5794 train_time:58563ms step_avg:97.61ms
+  DIAG s800 gb:[-4.08,-3.86,-3.82,-3.87] gw:[1.26,1.23,1.22,0.62] mg:-5.70 ng:-6.39 spec:13.9 smax:0.413 mix_d:[27.5,19.4,35.7,43.4] mix_u:[50.6,35.4,56.0,72.1] mix_sg:[47.88,45.00,105.61,117.61] attn_o:18.04 attn_v:29.12 cell:[88.3,62.2,55.2,77.3] msg:29.6 rb:-2.98 hemb:40.7
+step:800/50000 train_loss:3.4078 train_time:78069ms step_avg:97.59ms
+  DIAG s1000 gb:[-4.05,-3.82,-3.78,-3.85] gw:[1.28,1.32,1.29,1.46] mg:-5.85 ng:-6.57 spec:15.3 smax:0.538 mix_d:[32.6,22.6,41.5,49.9] mix_u:[59.1,39.4,63.4,80.1] mix_sg:[54.04,53.45,121.52,130.04] attn_o:20.49 attn_v:31.37 cell:[92.2,63.4,56.4,85.0] msg:31.4 rb:-3.05 hemb:39.9
+step:1000/50000 train_loss:3.2833 train_time:97572ms step_avg:97.57ms
+  DIAG s1200 gb:[-4.03,-3.80,-3.75,-3.81] gw:[1.27,1.34,1.32,1.65] mg:-5.95 ng:-6.70 spec:17.3 smax:0.664 mix_d:[38.2,26.4,46.4,54.9] mix_u:[70.2,45.0,69.6,85.9] mix_sg:[61.23,61.79,133.58,138.52] attn_o:23.22 attn_v:33.94 cell:[95.6,65.0,57.6,90.7] msg:33.2 rb:-3.10 hemb:39.2
+step:1200/50000 train_loss:3.0500 train_time:117073ms step_avg:97.56ms
+  DIAG s1400 gb:[-4.01,-3.78,-3.73,-3.77] gw:[1.27,1.26,1.36,1.65] mg:-5.95 ng:-6.73 spec:18.7 smax:0.717 mix_d:[42.5,30.3,49.2,57.2] mix_u:[78.5,51.8,73.4,88.7] mix_sg:[65.53,67.42,140.08,142.70] attn_o:25.69 attn_v:36.21 cell:[97.1,68.9,58.2,93.4] msg:34.5 rb:-3.11 hemb:38.7
+step:1400/50000 train_loss:2.5067 train_time:136574ms step_avg:97.55ms
+  DIAG s1600 gb:[-3.99,-3.74,-3.69,-3.72] gw:[1.26,1.28,1.41,1.68] mg:-5.91 ng:-6.71 spec:20.5 smax:0.779 mix_d:[45.1,34.2,50.9,58.2] mix_u:[82.7,58.2,75.4,89.7] mix_sg:[67.00,70.53,143.23,144.41] attn_o:27.61 attn_v:37.82 cell:[97.6,70.9,58.6,94.5] msg:34.9 rb:-3.11 hemb:38.3
+step:1600/50000 train_loss:2.0587 train_time:156071ms step_avg:97.54ms
+  DIAG s1800 gb:[-3.97,-3.70,-3.67,-3.69] gw:[1.26,1.28,1.45,1.71] mg:-5.85 ng:-6.66 spec:21.9 smax:0.835 mix_d:[46.6,36.4,51.8,58.6] mix_u:[85.0,61.6,76.6,90.1] mix_sg:[67.49,71.99,145.11,145.30] attn_o:28.81 attn_v:38.77 cell:[97.7,71.5,59.3,94.8] msg:35.2 rb:-3.10 hemb:38.0
+step:1800/50000 train_loss:1.6533 train_time:175567ms step_avg:97.54ms
+  DIAG s2000 gb:[-3.96,-3.67,-3.65,-3.66] gw:[1.25,1.33,1.47,1.75] mg:-5.80 ng:-6.62 spec:22.6 smax:0.808 mix_d:[47.4,37.6,52.5,58.9] mix_u:[86.3,63.6,77.4,90.3] mix_sg:[67.47,72.87,146.20,145.88] attn_o:29.61 attn_v:39.31 cell:[97.8,72.0,59.8,95.0] msg:35.3 rb:-3.09 hemb:37.8
+step:2000/50000 train_loss:1.3530 train_time:195066ms step_avg:97.53ms
+  DIAG s2200 gb:[-3.94,-3.65,-3.63,-3.64] gw:[1.24,1.35,1.49,1.77] mg:-5.76 ng:-6.57 spec:23.0 smax:0.774 mix_d:[47.9,38.3,52.9,59.1] mix_u:[87.0,64.8,77.8,90.4] mix_sg:[67.22,73.33,146.85,146.25] attn_o:30.13 attn_v:39.62 cell:[98.1,72.4,60.2,95.1] msg:35.5 rb:-3.09 hemb:37.6
+step:2200/50000 train_loss:1.1833 train_time:214583ms step_avg:97.54ms
+  DIAG s2400 gb:[-3.93,-3.64,-3.62,-3.62] gw:[1.24,1.38,1.50,1.80] mg:-5.73 ng:-6.54 spec:23.3 smax:0.749 mix_d:[48.3,38.7,53.3,59.3] mix_u:[87.4,65.6,78.2,90.5] mix_sg:[66.97,73.63,147.29,146.48] attn_o:30.49 attn_v:39.82 cell:[98.2,72.8,60.5,95.2] msg:35.6 rb:-3.08 hemb:37.5
+step:2400/50000 train_loss:1.0122 train_time:234088ms step_avg:97.54ms
+  DIAG s2600 gb:[-3.93,-3.63,-3.61,-3.61] gw:[1.24,1.40,1.52,1.81] mg:-5.72 ng:-6.52 spec:23.4 smax:0.737 mix_d:[48.5,39.0,53.5,59.4] mix_u:[87.7,66.0,78.4,90.6] mix_sg:[66.63,73.73,147.53,146.60] attn_o:30.72 attn_v:39.93 cell:[98.3,73.0,60.7,95.3] msg:35.7 rb:-3.08 hemb:37.4
+step:2600/50000 train_loss:0.8743 train_time:253588ms step_avg:97.53ms
+  DIAG s2800 gb:[-3.92,-3.62,-3.60,-3.59] gw:[1.24,1.42,1.53,1.83] mg:-5.71 ng:-6.51 spec:23.6 smax:0.726 mix_d:[48.6,39.1,53.6,59.4] mix_u:[87.8,66.3,78.5,90.5] mix_sg:[66.32,73.71,147.68,146.66] attn_o:30.86 attn_v:39.99 cell:[98.4,73.1,60.8,95.3] msg:35.7 rb:-3.07 hemb:37.3
+step:2800/50000 train_loss:0.7088 train_time:273088ms step_avg:97.53ms
+  DIAG s3000 gb:[-3.91,-3.61,-3.59,-3.58] gw:[1.23,1.44,1.54,1.85] mg:-5.71 ng:-6.50 spec:23.6 smax:0.722 mix_d:[48.7,39.2,53.7,59.5] mix_u:[87.8,66.4,78.6,90.5] mix_sg:[65.95,73.58,147.75,146.68] attn_o:30.96 attn_v:40.03 cell:[98.4,73.1,60.8,95.3] msg:35.8 rb:-3.07 hemb:37.3
+step:3000/50000 train_loss:0.7068 train_time:292587ms step_avg:97.53ms
+  NEW BEST step:3000 loss:0.7062 (saved EMA snapshot)
+  NEW BEST step:3001 loss:0.6974 (saved EMA snapshot)
+  NEW BEST step:3002 loss:0.6965 (saved EMA snapshot)
+  NEW BEST step:3003 loss:0.6848 (saved EMA snapshot)
+  NEW BEST step:3005 loss:0.6690 (saved EMA snapshot)
+  NEW BEST step:3008 loss:0.6262 (saved EMA snapshot)
+  NEW BEST step:3071 loss:0.6130 (saved EMA snapshot)
+  NEW BEST step:3106 loss:0.6118 (saved EMA snapshot)
+  NEW BEST step:3110 loss:0.6029 (saved EMA snapshot)
+  NEW BEST step:3120 loss:0.5897 (saved EMA snapshot)
+  NEW BEST step:3122 loss:0.5799 (saved EMA snapshot)
+  NEW BEST step:3172 loss:0.5794 (saved EMA snapshot)
+  NEW BEST step:3173 loss:0.5675 (saved EMA snapshot)
+  NEW BEST step:3184 loss:0.5561 (saved EMA snapshot)
+  NEW BEST step:3193 loss:0.5522 (saved EMA snapshot)
+  NEW BEST step:3195 loss:0.5497 (saved EMA snapshot)
+  NEW BEST step:3196 loss:0.5470 (saved EMA snapshot)
+  DIAG s3200 gb:[-3.91,-3.61,-3.59,-3.58] gw:[1.23,1.46,1.54,1.86] mg:-5.71 ng:-6.50 spec:23.7 smax:0.718 mix_d:[48.8,39.3,53.8,59.5] mix_u:[87.8,66.5,78.6,90.5] mix_sg:[65.63,73.44,147.77,146.67] attn_o:31.03 attn_v:40.05 cell:[98.4,73.1,60.9,95.3] msg:35.8 rb:-3.07 hemb:37.2
+step:3200/50000 train_loss:0.5699 train_time:313607ms step_avg:98.00ms
+  NEW BEST step:3337 loss:0.5461 (saved EMA snapshot)
+  NEW BEST step:3361 loss:0.5434 (saved EMA snapshot)
+  DIAG s3400 gb:[-3.91,-3.60,-3.58,-3.57] gw:[1.23,1.47,1.55,1.87] mg:-5.72 ng:-6.49 spec:23.7 smax:0.716 mix_d:[48.8,39.3,53.9,59.5] mix_u:[87.8,66.6,78.7,90.5] mix_sg:[65.36,73.29,147.79,146.65] attn_o:31.08 attn_v:40.06 cell:[98.4,73.0,60.9,95.4] msg:35.8 rb:-3.07 hemb:37.2
+step:3400/50000 train_loss:0.5540 train_time:333918ms step_avg:98.21ms
+  NEW BEST step:3403 loss:0.5352 (saved EMA snapshot)
+  NEW BEST step:3407 loss:0.5349 (saved EMA snapshot)
+  NEW BEST step:3411 loss:0.5183 (saved EMA snapshot)
+  NEW BEST step:3414 loss:0.5132 (saved EMA snapshot)
+  NEW BEST step:3419 loss:0.4924 (saved EMA snapshot)
+  NEW BEST step:3432 loss:0.4899 (saved EMA snapshot)
+  NEW BEST step:3577 loss:0.4857 (saved EMA snapshot)
+  DIAG s3600 gb:[-3.90,-3.60,-3.58,-3.57] gw:[1.23,1.49,1.55,1.88] mg:-5.72 ng:-6.49 spec:23.8 smax:0.714 mix_d:[48.8,39.4,53.9,59.6] mix_u:[87.8,66.6,78.7,90.4] mix_sg:[65.12,73.13,147.79,146.63] attn_o:31.12 attn_v:40.07 cell:[98.4,73.0,60.9,95.4] msg:35.8 rb:-3.06 hemb:37.2
+step:3600/50000 train_loss:0.5741 train_time:354248ms step_avg:98.40ms
+  NEW BEST step:3634 loss:0.4849 (saved EMA snapshot)
+  NEW BEST step:3635 loss:0.4796 (saved EMA snapshot)
+  NEW BEST step:3649 loss:0.4768 (saved EMA snapshot)
+  NEW BEST step:3756 loss:0.4625 (saved EMA snapshot)
+  DIAG s3800 gb:[-3.90,-3.60,-3.58,-3.56] gw:[1.23,1.50,1.56,1.89] mg:-5.72 ng:-6.49 spec:23.8 smax:0.712 mix_d:[48.9,39.4,54.0,59.6] mix_u:[87.8,66.7,78.7,90.4] mix_sg:[64.89,73.00,147.80,146.61] attn_o:31.17 attn_v:40.08 cell:[98.4,73.0,61.0,95.4] msg:35.8 rb:-3.06 hemb:37.1
+step:3800/50000 train_loss:0.5410 train_time:374550ms step_avg:98.57ms
+  DIAG s4000 gb:[-3.90,-3.59,-3.57,-3.56] gw:[1.23,1.51,1.56,1.90] mg:-5.73 ng:-6.49 spec:23.8 smax:0.710 mix_d:[48.9,39.4,54.0,59.6] mix_u:[87.8,66.7,78.8,90.4] mix_sg:[64.70,72.86,147.80,146.58] attn_o:31.21 attn_v:40.09 cell:[98.4,73.0,61.0,95.4] msg:35.9 rb:-3.06 hemb:37.1
+step:4000/50000 train_loss:0.4993 train_time:394862ms step_avg:98.72ms
+  NEW BEST step:4074 loss:0.4610 (saved EMA snapshot)
+  NEW BEST step:4082 loss:0.4604 (saved EMA snapshot)
+  NEW BEST step:4083 loss:0.4589 (saved EMA snapshot)
+  NEW BEST step:4090 loss:0.4492 (saved EMA snapshot)
+  NEW BEST step:4094 loss:0.4395 (saved EMA snapshot)
+  NEW BEST step:4097 loss:0.4243 (saved EMA snapshot)
+  DIAG s4200 gb:[-3.89,-3.59,-3.57,-3.55] gw:[1.23,1.52,1.57,1.92] mg:-5.73 ng:-6.49 spec:23.9 smax:0.709 mix_d:[49.0,39.5,54.1,59.6] mix_u:[87.8,66.7,78.8,90.3] mix_sg:[64.51,72.72,147.81,146.55] attn_o:31.25 attn_v:40.09 cell:[98.4,73.1,61.1,95.5] msg:35.9 rb:-3.06 hemb:37.1
+step:4200/50000 train_loss:0.5173 train_time:415184ms step_avg:98.85ms
+  NEW BEST step:4306 loss:0.3846 (saved EMA snapshot)
+  DIAG s4400 gb:[-3.89,-3.59,-3.56,-3.55] gw:[1.22,1.54,1.57,1.93] mg:-5.73 ng:-6.49 spec:23.9 smax:0.707 mix_d:[49.0,39.5,54.1,59.6] mix_u:[87.8,66.8,78.8,90.3] mix_sg:[64.35,72.60,147.82,146.53] attn_o:31.28 attn_v:40.10 cell:[98.4,73.1,61.1,95.5] msg:35.9 rb:-3.06 hemb:37.0
+step:4400/50000 train_loss:0.5122 train_time:435463ms step_avg:98.97ms
+  DIAG s4600 gb:[-3.89,-3.58,-3.56,-3.54] gw:[1.22,1.55,1.58,1.94] mg:-5.74 ng:-6.49 spec:23.9 smax:0.705 mix_d:[49.0,39.5,54.2,59.7] mix_u:[87.8,66.8,78.8,90.3] mix_sg:[64.17,72.46,147.83,146.50] attn_o:31.32 attn_v:40.10 cell:[98.4,73.1,61.1,95.6] msg:35.9 rb:-3.06 hemb:37.0
+step:4600/50000 train_loss:0.4579 train_time:456901ms step_avg:99.33ms
+  DIAG s4800 gb:[-3.88,-3.58,-3.56,-3.54] gw:[1.22,1.55,1.59,1.94] mg:-5.74 ng:-6.49 spec:24.0 smax:0.703 mix_d:[49.1,39.5,54.2,59.7] mix_u:[87.8,66.8,78.9,90.2] mix_sg:[64.01,72.35,147.85,146.49] attn_o:31.36 attn_v:40.11 cell:[98.4,73.1,61.2,95.6] msg:36.0 rb:-3.05 hemb:37.0
+step:4800/50000 train_loss:0.4717 train_time:477296ms step_avg:99.44ms
+  DIAG s5000 gb:[-3.88,-3.57,-3.55,-3.53] gw:[1.22,1.57,1.59,1.96] mg:-5.75 ng:-6.49 spec:24.0 smax:0.702 mix_d:[49.1,39.6,54.3,59.7] mix_u:[87.8,66.9,78.9,90.2] mix_sg:[63.86,72.23,147.87,146.47] attn_o:31.39 attn_v:40.11 cell:[98.4,73.1,61.2,95.7] msg:36.0 rb:-3.05 hemb:36.9
+step:5000/50000 train_loss:0.4729 train_time:497605ms step_avg:99.52ms
+  NEW BEST step:5143 loss:0.3802 (saved EMA snapshot)
+  DIAG s5200 gb:[-3.88,-3.57,-3.55,-3.53] gw:[1.22,1.57,1.60,1.96] mg:-5.75 ng:-6.49 spec:24.0 smax:0.700 mix_d:[49.1,39.6,54.3,59.8] mix_u:[87.7,66.9,78.9,90.2] mix_sg:[63.71,72.11,147.88,146.45] attn_o:31.43 attn_v:40.12 cell:[98.4,73.1,61.3,95.8] msg:36.1 rb:-3.05 hemb:36.9
+step:5200/50000 train_loss:0.3953 train_time:517895ms step_avg:99.60ms
+  NEW BEST step:5239 loss:0.3548 (saved EMA snapshot)
+  DIAG s5400 gb:[-3.87,-3.57,-3.54,-3.52] gw:[1.21,1.58,1.60,1.97] mg:-5.76 ng:-6.49 spec:24.1 smax:0.699 mix_d:[49.2,39.6,54.4,59.8] mix_u:[87.7,66.9,79.0,90.1] mix_sg:[63.57,72.00,147.91,146.43] attn_o:31.46 attn_v:40.12 cell:[98.4,73.2,61.3,95.8] msg:36.1 rb:-3.05 hemb:36.9
+step:5400/50000 train_loss:0.4204 train_time:538168ms step_avg:99.66ms
+  NEW BEST step:5417 loss:0.3497 (saved EMA snapshot)
+  DIAG s5600 gb:[-3.87,-3.56,-3.54,-3.52] gw:[1.21,1.59,1.61,1.98] mg:-5.76 ng:-6.49 spec:24.1 smax:0.697 mix_d:[49.2,39.7,54.5,59.9] mix_u:[87.7,66.9,79.0,90.1] mix_sg:[63.45,71.89,147.93,146.41] attn_o:31.50 attn_v:40.13 cell:[98.4,73.2,61.4,95.9] msg:36.1 rb:-3.05 hemb:36.8
+step:5600/50000 train_loss:0.5551 train_time:558471ms step_avg:99.73ms
+  NEW BEST step:5701 loss:0.3441 (saved EMA snapshot)
+  NEW BEST step:5748 loss:0.3435 (saved EMA snapshot)
+  NEW BEST step:5766 loss:0.3370 (saved EMA snapshot)
+  NEW BEST step:5771 loss:0.3250 (saved EMA snapshot)
+  DIAG s5800 gb:[-3.87,-3.56,-3.54,-3.52] gw:[1.21,1.60,1.61,1.99] mg:-5.77 ng:-6.49 spec:24.1 smax:0.696 mix_d:[49.2,39.7,54.5,59.9] mix_u:[87.7,67.0,79.0,90.1] mix_sg:[63.31,71.79,147.93,146.39] attn_o:31.52 attn_v:40.13 cell:[98.5,73.2,61.4,96.0] msg:36.2 rb:-3.05 hemb:36.8
+step:5800/50000 train_loss:0.4053 train_time:578813ms step_avg:99.80ms
+  NEW BEST step:5814 loss:0.3168 (saved EMA snapshot)
+  NEW BEST step:5879 loss:0.2797 (saved EMA snapshot)
+  DIAG s6000 gb:[-3.87,-3.56,-3.54,-3.51] gw:[1.21,1.60,1.62,2.00] mg:-5.78 ng:-6.50 spec:24.1 smax:0.695 mix_d:[49.2,39.7,54.5,59.9] mix_u:[87.7,67.0,79.0,90.1] mix_sg:[63.20,71.69,147.92,146.37] attn_o:31.54 attn_v:40.14 cell:[98.5,73.2,61.4,96.0] msg:36.2 rb:-3.05 hemb:36.8
+step:6000/50000 train_loss:0.3201 train_time:599053ms step_avg:99.84ms
+step:6010/50000 val_loss:0.3339 val_bpb:0.1978 train_time:600067ms
+stopping_early: wallclock_cap train_time:600067ms step:6010
+peak memory: 23108 MiB reserved: 44196 MiB
+  Using BEST model for serialization (step:5879 loss:0.2797)
+Serialized model: 84235049 bytes
+Code size: 58466 bytes
+Serialized model int8+zlib: 15393903 bytes (payload:21230070 ratio:3.97x)
+Total submission size int8+zlib: 15452369 bytes
+final_int8_zlib_roundtrip val_loss:0.7446 val_bpb:0.4410 eval_time:34313ms
+final_int8_zlib_roundtrip_exact val_loss:0.74460095 val_bpb:0.44099459
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