Revert "Implement shared event-trajectory reasoning backbone"

This reverts commit 06f29c0f0a.
Revert "Add nano and tiny model presets"
2026-07-23 16:05:00 +08:00 · 2026-07-23 16:03:29 +08:00 · 2026-07-23 15:01:33 +08:00 · 2026-07-23 14:23:34 +08:00 · 2026-07-22 16:14:52 +08:00 · 2026-07-22 14:44:31 +08:00
21 changed files with 650 additions and 1194 deletions
--- a/MODEL_ARCHITECTURES.md
+++ b/MODEL_ARCHITECTURES.md
@@ -1,64 +0,0 @@
-# Model architectures
-
-DeepHealth uses one codebase for both supported history-block architectures.
-Select the architecture explicitly when starting a training run:
-
-| `model_architecture` | History block | Checkpoint fingerprint |
-| --- | --- | --- |
-| `transformer_ffn_v1` | Temporal attention + SwiGLU FFN | `blocks.*.mlp.w1/w2/w3` and `blocks.*.ln2` |
-| `traj_mixer_v5` | Temporal attention + TrajMixer | `blocks.*.mlp.intra_*`, `gate_proj`, and `output_proj` |
-
-`transformer_ffn_v1` is the CLI default; pass `traj_mixer_v5` explicitly for
-TrajMixer runs.
-
-## Training
-
-Next-step example:
-
-```powershell
-python train_next_step.py --model_architecture traj_mixer_v5 --n_layer 12
-```
-
-All-future example:
-
-```powershell
-python train_all_future.py --model_architecture transformer_ffn_v1 --n_layer 12
-```
-
-New runs are separated by architecture:
-
-```text
-runs/
-  transformer_ffn_v1/
-    <run_name>/
-  traj_mixer_v5/
-    <run_name>/
-```
-
-Use `--runs_root` to place this structure under a different root. Existing run
-directories are not moved or renamed.
-
-Each generated `train_config.json` records `model_architecture`, total parameter
-count, and trainable parameter count.
-
-Both training entry points use the single `--n_layer` option to set the number
-of history backbone blocks. The same value is passed to `DeepHealth.n_layer`
-and saved as `n_layer` in `train_config.json`; it must be at least 1.
-
-## Architecture validation
-
-Evaluation resolves the architecture before constructing the model and always
-loads weights with `strict=True`.
-
- Every config must include an explicit `model_architecture` marker.
- Checkpoint fingerprints are used to validate that the selected architecture
-  matches the stored weights.
- A config marker that conflicts with the checkpoint fingerprint raises an
-  error instead of silently choosing one architecture.
- Unsupported historical TrajMixer markers such as `traj_mixer_v2`,
-  `traj_mixer_v3`, and `traj_mixer_v4` are rejected.
- Checkpoints and configs created before architecture markers were introduced
-  are intentionally unsupported.
-
-Project code should use the architecture factory rather than instantiate a
-history block directly.
--- a/TrajMixer_设计方案.md
+++ b/TrajMixer_设计方案.md
@@ -0,0 +1,333 @@
+# TrajMixer Block 最终设计方案
+
+> 状态：**Frozen implementation baseline**
+>
+> 版本：**v1.0**
+>
+> 固化日期：**2026-07-22**
+
+本文档是 TrajMixer 后续实现与实验的唯一结构基线。除显式标记为消融项的配置外，所有实现均应遵循本文档；若结构发生变化，应先更新版本和实验记录。
+
+## 1. 目标
+
+在保持原始 Delphi Transformer Attention 结构不变的前提下，用轻量、可并行的轨迹交互模块替换 FFN。
+
+保持不变的组件包括：
+
+- 原始 causal mask；
+- 原始 TimeRoPE / Relative Time Attention Bias；
+- 原始 Multi-Head Attention，包括 \(W_Q/W_K/W_V/W_O\)；
+- 原始序列建模与训练目标。
+
+TrajMixer 不修改 Attention，只替换每个 Transformer block 中的 FFN residual branch。
+
+## 2. Block 总体结构
+
+概念结构：
+
+```text
+PreNorm Causal Multi-Head Attention
+→ Residual
+→ Standard Mixer PreNorm
+→ Group-wise Feature Alignment
+→ SwiGLU Cross-Group Mixer
+→ Residual
+```
+
+完整计算为：
+
+\[
+U = X^{(l)} + \operatorname{Dropout}\!\left(
+\operatorname{CausalMHA}\left(
+\operatorname{LN}_{\mathrm{attn}}(X^{(l)}),
+\text{time information}
+\right)\right),
+\]
+
+\[
+N = \operatorname{LN}_{\mathrm{mixer}}(U),
+\]
+
+\[
+\Delta = \operatorname{TrajMixer}(N),
+\]
+
+\[
+X^{(l+1)} = U + \operatorname{Dropout}(\Delta).
+\]
+
+首版中的 \(\operatorname{LN}_{\mathrm{mixer}}\) 是作用于完整 \(d=120\) 维 residual representation 的标准 LayerNorm。
+
+## 3. Latent Trajectory Group 定义
+
+Attention 输出经过 \(W_O\) 后仍是标准 residual representation：
+
+\[
+N\in\mathbb{R}^{B\times L\times d},\qquad d=120.
+\]
+
+将 hidden dimension 划分为与 Attention head 数量相同的 group 数量：
+
+\[
+n_{\mathrm{group}}:=n_{\mathrm{head}}=10,
+\qquad d_{\mathrm{group}}=\frac{d}{n_{\mathrm{head}}}=12,
+\]
+
+`n_group` 不再是独立超参数，代码统一使用 `n_head` 确定 residual group 数量。二者只共享数量；这些 residual groups 在语义和张量来源上仍不等同于原始 Attention heads。
+
+并 reshape 为：
+
+\[
+N_{\mathrm{group}}in
+\mathbb{R}^{B\times L\times n_{\mathrm{group}}\times d_{\mathrm{group}}}.
+\]
+
+这些 group 是 residual space 中的 **latent trajectory groups**，不等同于原始 Attention heads。本文中的 group、trajectory group 均指这一 residual-channel partition。
+
+## 4. Group-wise Feature Alignment
+
+为缓解不同 group 内部坐标不对齐的问题，每个 group 使用独立的小矩阵：
+
+\[
+B_i\in\mathbb{R}^{d_{\mathrm{group}}\times d_{\mathrm{group}}},
+\qquad i=1,\ldots,n_{\mathrm{group}}.
+\]
+
+对每个 group 内的特征进行可学习对齐：
+
+\[
+Z_{b,t,i,:}=N_{\mathrm{group},b,t,i,:}B_i.
+\]
+
+因此：
+
+\[
+Z\in
+\mathbb{R}^{B\times L\times n_{\mathrm{group}}\times d_{\mathrm{group}}}.
+\]
+
+首版实现约定：
+
+- \(B_i\) 不带 bias；
+- \(B_i\) 使用单位矩阵初始化；
+- Alignment 只作用于 Mixer residual branch，不改变 Attention residual stream；
+- 首版不增加逆变换或额外的 group 内输出投影。
+
+Alignment 每层权重参数量为：
+
+\[
+n_{\mathrm{group}}d_{\mathrm{group}}^2
+=10\times12^2
+=1{,}440.
+\]
+
+## 5. SwiGLU Cross-Group Mixer
+
+Mixer 只沿 group 维度交互，不沿序列维度交互，因此不会引入时间递归或未来信息泄漏。
+
+对于每个 group 内特征维度：
+
+\[
+r=1,\ldots,d_{\mathrm{group}},
+\]
+
+定义：
+
+\[
+A_g^{(r)},A_v^{(r)}
+\in\mathbb{R}^{n_{\mathrm{group}}\times h_{\mathrm{group}}},
+\]
+
+\[
+A_o^{(r)}
+\in\mathbb{R}^{h_{\mathrm{group}}\times n_{\mathrm{group}}}.
+\]
+
+隐藏宽度不再独立配置，固定为：
+
+\[
+h_{\mathrm{group}}=4n_{\mathrm{head}}
+=4n_{\mathrm{group}}.
+\]
+
+当前 \(n_{\mathrm{head}}=10\)，因此 \(h_{\mathrm{group}}=40\)。
+
+对固定的 batch、时间位置和内部特征维度 \(r\)，将：
+
+\[
+Z_{b,t,:,r}\in\mathbb{R}^{n_{\mathrm{group}}}
+\]
+
+视为 row vector，计算：
+
+\[
+G_{b,t,:,r}=Z_{b,t,:,r}A_g^{(r)},
+\]
+
+\[
+V_{b,t,:,r}=Z_{b,t,:,r}A_v^{(r)},
+\]
+
+\[
+M_{b,t,:,r}=\operatorname{SiLU}(G_{b,t,:,r})\odot V_{b,t,:,r},
+\]
+
+\[
+Y_{b,t,:,r}=M_{b,t,:,r}A_o^{(r)}.
+\]
+
+其中：
+
+- gate 分支控制信息写入；
+- value 分支提供交互内容；
+- output matrix 将隐藏 group 表示投影回原始 group 数量；
+- hidden group 表示固定扩展为 group 数量的 4 倍。
+
+所有 \(r\) 的输出组合为：
+
+\[
+Y\in
+\mathbb{R}^{B\times L\times n_{\mathrm{group}}\times d_{\mathrm{group}}},
+\]
+
+再 reshape 为：
+
+\[
+\Delta\in\mathbb{R}^{B\times L\times d}.
+\]
+
+## 6. 参数张量与无歧义索引
+
+建议的实现存储形状为：
+
+```text
+group_align: [n_group, d_group, d_group]
+gate_proj:   [d_group, n_group, hidden_group]
+value_proj:  [d_group, n_group, hidden_group]
+output_proj: [d_group, hidden_group, n_group]
+```
+
+对应的索引公式为：
+
+\[
+G_{b,t,q,r}
+=\sum_i Z_{b,t,i,r}\,A_{g,r,i,q},
+\]
+
+\[
+V_{b,t,q,r}
+=\sum_i Z_{b,t,i,r}\,A_{v,r,i,q},
+\]
+
+\[
+Y_{b,t,i,r}
+=\sum_q
+\left[\operatorname{SiLU}(G_{b,t,q,r})V_{b,t,q,r}\right]
+A_{o,r,q,i}.
+\]
+
+首版的三个 Mixer projection 均不带 bias。
+
+## 7. Mixer Hidden Width 与参数量
+
+Mixer 的 group 维变换为：
+
+\[
+n_{\mathrm{group}}
+\rightarrow
+h_{\mathrm{group}}
+\rightarrow
+n_{\mathrm{group}}.
+\]
+
+固定 \(h_{\mathrm{group}}=4n_{\mathrm{group}}=40\) 时，Mixer 每层权重参数量为：
+
+\[
+3d_{\mathrm{group}}n_{\mathrm{group}}h_{\mathrm{group}}
+=3\times12\times10\times40
+=14{,}400.
+\]
+
+加上 Group Feature Alignment 后，TrajMixer residual branch 每层共有：
+
+\[
+14{,}400+1{,}440=15{,}840
+\]
+
+个主要权重参数。作为对照，原始 \(120\rightarrow480\rightarrow120\) FFN 每层约有 115,800 个参数。
+
+参数对照口径说明：上面的 115,800 对应结构方案中的标准两层 FFN。当前代码库在 TrajMixer 替换前实际使用的是隐藏宽度 300 的全维度 SwiGLU（gate/value/output 三个线性层），每层共有 108,720 个参数（含 bias）。代码实验和 checkpoint 参数量比较必须以 108,720 作为历史实现基线，不能与概念方案中的标准 FFN 参数量混用。
+
+## 8. LayerNorm 基线与消融
+
+为保持与原始 Transformer 的可比性，首版固定使用：
+
+```text
+原始 FFN baseline：FFN + 标准 LayerNorm
+TrajMixer baseline：Mixer + 标准 LayerNorm
+```
+
+以下配置不属于首版主实验，只作为独立消融：
+
+```text
+Mixer + Group-wise LayerNorm
+```
+
+不得将 Group-wise LayerNorm 的结果直接作为“仅替换 FFN”的对照结果。
+
+## 9. 初始化
+
+首版初始化约定：
+
+- Group Alignment \(B_i\)：单位矩阵初始化；
+- \(A_g/A_v\)：Xavier uniform 初始化；
+- \(A_o\)：均值为 0、标准差为 \(10^{-3}\) 的正态初始化；
+- Dropout 概率沿用原始 FFN residual branch 的配置。
+
+小初始化的 \(A_o\) 使新增分支在训练初期接近恒等残差更新，同时允许模型逐步学习轨迹交互。
+
+## 10. 核心设计思想
+
+**Attention**：负责从历史疾病序列中选择并整合相关信息。
+
+**Group Feature Alignment**：负责学习不同 latent trajectory groups 的内部特征对齐。
+
+**Cross-Group Mixer**：负责不同潜在疾病轨迹之间的非线性门控交互。
+
+整个模块保持：
+
+- 无时间递归；
+- 序列维度完全并行；
+- 参数量远低于原始 FFN；
+- 保留 Transformer 的因果历史建模能力；
+- 不把 residual groups 误解释为原始 Attention heads。
+
+## 11. 首版固定配置
+
+```yaml
+model_architecture: traj_mixer_v2
+d_model: 120
+n_head: 10  # 同时决定 residual group 数量
+d_group: 12
+hidden_group_rule: 4 * n_head  # 不单独配置
+attention: unchanged
+attention_output_projection: unchanged
+mixer_norm: standard_layer_norm
+group_alignment: per_group_12x12
+group_alignment_bias: false
+group_alignment_init: identity
+mixer_bias: false
+gate_value_init: xavier_uniform
+output_init_std: 0.001
+group_wise_layer_norm: false
+```
+
+训练时必须将 `model_architecture: traj_mixer_v2`、`model_parameter_count` 和 `trainable_parameter_count` 写入 `train_config.json`，并在训练日志中显式打印总参数量与可训练参数量。本分支的评估和导出入口只接受带有该架构标识、且 checkpoint 中包含 TrajMixer 参数张量的模型；其他版本或分支生成的模型应直接拒绝加载。
+
+必须满足：
+
+\[
+d=n_{\mathrm{group}}d_{\mathrm{group}}.
+\]
+
+后续实现、单元测试、参数量核验和主实验均以以上配置为默认基线。
--- a/backbones.py
+++ b/backbones.py
@@ -4,12 +4,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F

-from model_architectures import (
-    TRAJ_MIXER_ARCHITECTURE,
-    TRANSFORMER_FFN_ARCHITECTURE,
-    resolve_model_architecture,
-)
-

 class TimeRoPE(nn.Module):
    def __init__(self, dim: int, base: float = 10000.0):
@@ -117,10 +111,7 @@ class TemporalAttention(nn.Module):

        # Layer-specific projection from shared RBF basis activations to per-head attention bias.
        self.rbf_proj = nn.Linear(n_rbf_bases, n_head, bias=False)
-        # Keep the initial RBF attention bias exactly zero through the
-        # zero-initialized projection, while leaving that projection with a
-        # live gradient from the first optimization step.
-        self.time_bias_scale = nn.Parameter(torch.tensor(1.0))
+        self.time_bias_scale = nn.Parameter(torch.tensor(0.0))

        self.resid_drop = nn.Dropout(dropout)
        self.reset_parameters()
@@ -185,42 +176,8 @@ class TemporalAttention(nn.Module):
        return self.resid_drop(self.out_proj(out))


-class SwiGLU(nn.Module):
-    def __init__(
-        self,
-        n_embd: int,
-        hidden_dim: int | None = None,
-        dropout: float = 0.0,
-        bias: bool = True,
-    ):
-        super().__init__()
-        hidden_dim = hidden_dim if hidden_dim is not None else int(
-            n_embd * 2.5)
-
-        self.w1 = nn.Linear(n_embd, hidden_dim, bias=bias)  # gate path
-        self.w2 = nn.Linear(n_embd, hidden_dim, bias=bias)  # value path
-        # output projection
-        self.w3 = nn.Linear(hidden_dim, n_embd, bias=bias)
-        self.drop = nn.Dropout(dropout)
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        """GPT-style parameter initialization for MLP paths."""
-        nn.init.normal_(self.w1.weight, mean=0.0, std=0.02)
-        nn.init.normal_(self.w2.weight, mean=0.0, std=0.02)
-        nn.init.normal_(self.w3.weight, mean=0.0, std=0.02)
-        if self.w1.bias is not None:
-            nn.init.zeros_(self.w1.bias)
-            nn.init.zeros_(self.w2.bias)
-            nn.init.zeros_(self.w3.bias)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """``(B, L, n_embd) -> (B, L, n_embd)``."""
-        return self.drop(self.w3(F.silu(self.w1(x)) * self.w2(x)))
-
-
 class TrajMixer(nn.Module):
-    """PreNorm gated mixing within and across latent trajectory groups.
+    """Lightweight gated interaction across latent residual-space groups.

    The groups are contiguous partitions of the post-``W_O`` residual
    representation. They are deliberately not treated as attention heads.
@@ -244,26 +201,19 @@ class TrajMixer(nn.Module):
                f"n_embd must be divisible by n_head, got {n_embd} and {n_head}"
            )
        self.n_embd = n_embd
+        # The residual-group count is tied to n_head, but the resulting groups
+        # are still residual-space partitions rather than attention heads.
        self.n_group = n_head
        self.d_group = n_embd // n_head
-        self.intra_hidden = 4 * self.d_group
        self.hidden_group = 4 * n_head

-        self.norm = nn.LayerNorm(self.n_embd)
-
-        self.intra_gate_proj = nn.Parameter(
-            torch.empty(self.n_group, self.d_group, self.intra_hidden)
-        )
-        self.intra_value_proj = nn.Parameter(
-            torch.empty(self.n_group, self.d_group, self.intra_hidden)
-        )
-        self.intra_output_proj = nn.Parameter(
-            torch.empty(self.n_group, self.intra_hidden, self.d_group)
-        )
-        self.intra_gate_logits = nn.Parameter(
-            torch.empty(self.n_group, self.d_group)
+        # Per-group feature alignment: [group, input feature, output feature].
+        self.group_align = nn.Parameter(
+            torch.empty(self.n_group, self.d_group, self.d_group)
        )

+        # Per-feature cross-group projections. The feature index is kept
+        # independent, exactly as specified by the TrajMixer baseline.
        self.gate_proj = nn.Parameter(
            torch.empty(self.d_group, self.n_group, self.hidden_group)
        )
@@ -277,116 +227,57 @@ class TrajMixer(nn.Module):
        self.reset_parameters()

    def reset_parameters(self) -> None:
-        for group_idx in range(self.n_group):
-            nn.init.xavier_uniform_(self.intra_gate_proj[group_idx])
-            nn.init.xavier_uniform_(self.intra_value_proj[group_idx])
-            nn.init.xavier_uniform_(self.intra_output_proj[group_idx])
-        nn.init.constant_(
-            self.intra_gate_logits,
-            math.log(0.1 / 0.9),
-        )
+        with torch.no_grad():
+            identity = torch.eye(
+                self.d_group,
+                dtype=self.group_align.dtype,
+                device=self.group_align.device,
+            )
+            self.group_align.copy_(identity.unsqueeze(0).expand_as(self.group_align))

+        # Initialise each feature-specific matrix independently so Xavier's
+        # fan-in/fan-out calculation sees a two-dimensional matrix.
        for feature_idx in range(self.d_group):
            nn.init.xavier_uniform_(self.gate_proj[feature_idx])
            nn.init.xavier_uniform_(self.value_proj[feature_idx])
        nn.init.normal_(self.output_proj, mean=0.0, std=1e-3)

-    def _intra_mix(self, grouped: torch.Tensor) -> torch.Tensor:
-        """Mix features independently inside each residual-space group."""
-        intra_gate = torch.einsum(
-            "blgd,gdh->blgh", grouped, self.intra_gate_proj
-        )
-        intra_value = torch.einsum(
-            "blgd,gdh->blgh", grouped, self.intra_value_proj
-        )
-        intra_hidden = F.silu(intra_gate) * intra_value
-        return torch.einsum(
-            "blgh,ghd->blgd", intra_hidden, self.intra_output_proj
-        )
-
-    def _cross_mix(self, grouped: torch.Tensor) -> torch.Tensor:
-        """Mix groups independently for each within-group coordinate."""
-        gate = torch.einsum(
-            "blgr,rgh->blhr", grouped, self.gate_proj
-        )
-        value = torch.einsum(
-            "blgr,rgh->blhr", grouped, self.value_proj
-        )
-        hidden = F.silu(gate) * value
-        return torch.einsum(
-            "blhr,rhg->blgr", hidden, self.output_proj
-        )
-
    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Apply one full-width PreNorm and one outer residual update."""
+        """Map ``(B, L, n_embd)`` to an equally shaped residual update."""
        if x.ndim != 3:
-            raise ValueError(
-                f"TrajMixer expects a 3D tensor, got shape {tuple(x.shape)}"
-            )
+            raise ValueError(f"TrajMixer expects a 3D tensor, got shape {tuple(x.shape)}")
        if x.size(-1) != self.n_embd:
            raise ValueError(
                f"Expected hidden size {self.n_embd}, got {x.size(-1)}"
            )

        batch_size, seq_len, _ = x.shape
-        grouped = self.norm(x).reshape(
+        grouped = x.reshape(
            batch_size, seq_len, self.n_group, self.d_group
        )
-
-        intra_output = self._intra_mix(grouped)
-        intra_gate = torch.sigmoid(self.intra_gate_logits).view(
-            1, 1, self.n_group, self.d_group
+        aligned = torch.einsum(
+            "blgd,gde->blge", grouped, self.group_align
        )
-        mixed_input = grouped + intra_gate * intra_output

-        update = self._cross_mix(mixed_input).reshape(
-            batch_size, seq_len, self.n_embd
+        gate = torch.einsum(
+            "blgr,rgh->blhr", aligned, self.gate_proj
        )
-        return x + self.drop(update)
+        value = torch.einsum(
+            "blgr,rgh->blhr", aligned, self.value_proj
+        )
+        hidden = F.silu(gate) * value
+        mixed = torch.einsum(
+            "blhr,rhg->blgr", hidden, self.output_proj
+        )
+        return self.drop(mixed.reshape(batch_size, seq_len, self.n_embd))


-class TransformerFFNBlock(nn.Module):
+class GPTBlock(nn.Module):
    def __init__(
        self,
        n_embd: int,
        n_head: int,

-        attn_dropout: float = 0.0,
-        mlp_dropout: float = 0.0,
-        use_time_rope: bool = False,
-        use_rbf_bias: bool = False,
-        n_rbf_bases: int = 16,
-    ):
-        super().__init__()
-        self.attn = TemporalAttention(
-            n_embd=n_embd,
-            n_head=n_head,
-            n_rbf_bases=n_rbf_bases,
-            dropout=attn_dropout,
-            use_time_rope=use_time_rope,
-            use_rbf_bias=use_rbf_bias,
-        )
-        self.mlp = SwiGLU(n_embd=n_embd, dropout=mlp_dropout)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        rope_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
-        rbf_cache: torch.Tensor | None = None,
-        attn_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        x = x + self.attn(self.ln1(x), rope_cache, rbf_cache, attn_mask)
-        x = x + self.mlp(self.ln2(x))
-        return x
-
-
-class TrajMixerBlock(nn.Module):
-    def __init__(
-        self,
-        n_embd: int,
-        n_head: int,
        attn_dropout: float = 0.0,
        mlp_dropout: float = 0.0,
        use_time_rope: bool = False,
@@ -408,6 +299,7 @@ class TrajMixerBlock(nn.Module):
            dropout=mlp_dropout,
        )
        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)

    def forward(
        self,
@@ -417,38 +309,8 @@ class TrajMixerBlock(nn.Module):
        attn_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        x = x + self.attn(self.ln1(x), rope_cache, rbf_cache, attn_mask)
-        return self.mlp(x)
-
-
-def build_backbone_block(
-    model_architecture: str,
-    *,
-    n_embd: int,
-    n_head: int,
-    attn_dropout: float = 0.0,
-    mlp_dropout: float = 0.0,
-    use_time_rope: bool = False,
-    use_rbf_bias: bool = False,
-    n_rbf_bases: int = 16,
-) -> nn.Module:
-    """Build one history block for a supported model architecture."""
-    architecture = resolve_model_architecture(model_architecture)
-    block_class: type[nn.Module]
-    if architecture == TRANSFORMER_FFN_ARCHITECTURE:
-        block_class = TransformerFFNBlock
-    elif architecture == TRAJ_MIXER_ARCHITECTURE:
-        block_class = TrajMixerBlock
-    else:  # pragma: no cover - guarded by resolve_model_architecture.
-        raise ValueError(f"Unsupported model architecture: {architecture!r}")
-    return block_class(
-        n_embd=n_embd,
-        n_head=n_head,
-        attn_dropout=attn_dropout,
-        mlp_dropout=mlp_dropout,
-        use_time_rope=use_time_rope,
-        use_rbf_bias=use_rbf_bias,
-        n_rbf_bases=n_rbf_bases,
-    )
+        x = x + self.mlp(self.ln2(x))
+        return x


 class TokenAutoDiscretization(nn.Module):
--- a/delphi2m_auc_report.py
+++ b/delphi2m_auc_report.py
@@ -1,183 +0,0 @@
-"""Build Delphi2M-style sex-specific AUC reports.
-
-The Delphi2M evaluation code uses 0.1 years for the no-gap evaluation.  The
-published report displays that point as 0 months, while retaining the actual
-0.1-year evaluation period in this project's report output.
-"""
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Optional
-
-import numpy as np
-import pandas as pd
-
-
-DEFAULT_DELPHI2M_PERIODS_YEARS = (0.1, 1.0, 5.0, 10.0)
-
-_CHAPTER_SHORT_NAMES = {
-    "I": "I. Infectious Diseases",
-    "II": "II. Neoplasms",
-    "III": "III. Blood & Immune Disorders",
-    "IV": "IV. Metabolic Diseases",
-    "V": "V. Mental Disorders",
-    "VI": "VI. Nervous System Diseases",
-    "VII": "VII. Eye Diseases",
-    "VIII": "VIII. Ear Diseases",
-    "IX": "IX. Circulatory Diseases",
-    "X": "X. Respiratory Diseases",
-    "XI": "XI. Digestive Diseases",
-    "XII": "XII. Skin Diseases",
-    "XIII": "XIII. Musculoskeletal Diseases",
-    "XIV": "XIV. Genitourinary Diseases",
-    "XV": "XV. Pregnancy & Childbirth",
-    "XVI": "XVI. Perinatal Conditions",
-    "XVII": "XVII. Congenital Abnormalities",
-    "XVIII": "XVIII. Symptoms & Signs",
-    "XIX": "XIX. Injury & Poisoning",
-    "XX": "XX. External Causes",
-    "XXI": "XXI. Health Services",
-    "XXII": "XXII. Special Purposes",
-    "Death": "Death",
-    "Unmapped": "Unmapped",
-}
-
-
-def _is_no_gap(period_years: float) -> bool:
-    return bool(np.isclose(float(period_years), 0.1, rtol=0.0, atol=1e-8))
-
-
-def _canonical_period_years(period_years: float) -> float:
-    value = float(period_years)
-    for canonical in DEFAULT_DELPHI2M_PERIODS_YEARS:
-        if np.isclose(value, canonical, rtol=0.0, atol=1e-6):
-            return float(canonical)
-    return value
-
-
-def _gap_months(period_years: float) -> int:
-    if _is_no_gap(period_years):
-        return 0
-    return int(round(float(period_years) * 12.0))
-
-
-def _gap_label(period_years: float) -> str:
-    if _is_no_gap(period_years):
-        return "No gap"
-    value = float(period_years)
-    value_text = f"{value:g}"
-    unit = "year" if np.isclose(value, 1.0) else "years"
-    return f"{value_text} {unit}"
-
-
-def _load_chapter_by_code(
-    chapter_mapping_path: Optional[str | Path] = None,
-) -> Dict[str, str]:
-    if chapter_mapping_path is None:
-        chapter_mapping_path = Path(__file__).with_name(
-            "icd10_chapter_organ_mapping.csv"
-        )
-    path = Path(chapter_mapping_path)
-    if not path.exists():
-        return {}
-
-    mapping = pd.read_csv(
-        path,
-        usecols=["code", "icd10_chapter"],
-        dtype={"code": str, "icd10_chapter": str},
-    )
-    mapping["code"] = mapping["code"].str.strip()
-    mapping["chapter"] = (
-        mapping["icd10_chapter"]
-        .str.strip()
-        .map(_CHAPTER_SHORT_NAMES)
-        .fillna("Unmapped")
-    )
-    return dict(zip(mapping["code"], mapping["chapter"]))
-
-
-def build_delphi2m_auc_report(
-    df_unpooled: pd.DataFrame,
-    *,
-    period_col: str,
-    chapter_mapping_path: Optional[str | Path] = None,
-) -> pd.DataFrame:
-    """Aggregate age strata by sex and return a Delphi2M-style AUC report.
-
-    Required input columns are ``token``, ``label_code``, ``sex``,
-    ``auc_delong``, and the supplied ``period_col`` (``offset`` or
-    ``horizon``).  The output begins with the five columns used by Delphi2M
-    Fig. 2e and then records the actual evaluation period and ICD-10 code.
-    """
-    required = {"token", "label_code", "sex", "auc_delong", period_col}
-    missing = sorted(required - set(df_unpooled.columns))
-    if missing:
-        raise ValueError(
-            "Cannot build Delphi2M AUC report; missing columns: "
-            + ", ".join(missing)
-        )
-
-    source = df_unpooled.loc[
-        :,
-        ["token", "label_code", "sex", "auc_delong", period_col],
-    ].copy()
-    source["sex"] = source["sex"].astype(str).str.strip().str.lower()
-    source = source[source["sex"].isin(["female", "male"])]
-    source["auc_delong"] = pd.to_numeric(
-        source["auc_delong"], errors="coerce"
-    )
-    source[period_col] = pd.to_numeric(source[period_col], errors="coerce")
-    source = source.dropna(subset=[period_col, "auc_delong"])
-    source[period_col] = source[period_col].map(_canonical_period_years)
-
-    if source.empty:
-        raise ValueError("Cannot build Delphi2M AUC report from empty AUC data.")
-
-    grouped = (
-        source.groupby(
-            ["token", "label_code", period_col, "sex"],
-            dropna=False,
-            as_index=False,
-        )
-        .agg(auc=("auc_delong", "mean"))
-    )
-    report = (
-        grouped.pivot(
-            index=["token", "label_code", period_col],
-            columns="sex",
-            values="auc",
-        )
-        .reset_index()
-        .rename_axis(columns=None)
-        .rename(columns={"female": "Female", "male": "Male"})
-    )
-    for col in ["Female", "Male"]:
-        if col not in report.columns:
-            report[col] = np.nan
-
-    chapter_by_code = _load_chapter_by_code(chapter_mapping_path)
-    report["chapter"] = (
-        report["label_code"].astype(str).map(chapter_by_code).fillna("Unmapped")
-    )
-    report["Gap, months"] = report[period_col].map(_gap_months).astype("Int64")
-    report["Gap label"] = report[period_col].map(_gap_label)
-    report["icd10"] = pd.to_numeric(report["token"], errors="coerce").astype(
-        "Int64"
-    )
-
-    report = report.sort_values(
-        ["icd10", period_col], kind="stable", ignore_index=True
-    )
-    return report.loc[
-        :,
-        [
-            "Gap, months",
-            "chapter",
-            "icd10",
-            "Female",
-            "Male",
-            period_col,
-            "Gap label",
-            "label_code",
-        ],
-    ]
--- a/evaluate_auc.py
+++ b/evaluate_auc.py
@@ -7,8 +7,7 @@ This script follows the logic of the Delphi evaluation script supplied by the us
     at least `offset` years before the target time;
  3. run model inference by disease chunks to avoid materializing all logits;
  4. compute AUC separately by sex and age bracket;
-  5. average age-bracket AUCs within each sex and write a Delphi2M-style
-     Female/Male report.
+  5. aggregate age brackets with DeLong variance.

 Efficiency notes:
  - transformer/readout inference is executed once and cached;
@@ -40,13 +39,12 @@ from torch.utils.data import DataLoader, Subset
 from tqdm.auto import tqdm

 from dataset import HealthDataset
-from delphi2m_auc_report import (
-    DEFAULT_DELPHI2M_PERIODS_YEARS,
-    build_delphi2m_auc_report,
-)
 from eval_data import load_sequence_eval_dataset, sequence_eval_collate_fn
-from model_architectures import resolve_model_architecture
-from models import DeepHealth
+from models import (
+    DeepHealth,
+    validate_traj_mixer_config,
+    validate_traj_mixer_state_dict,
+)
 from readouts import build_readout
 from targets import PAD_IDX, CHECKUP_IDX, NO_EVENT_IDX

@@ -314,24 +312,20 @@ def split_indices(n: int, train_ratio: float, val_ratio: float, test_ratio: floa
    return idx[:n_train], idx[n_train:n_train + n_val], idx[n_train + n_val:]


-def build_model_from_dataset(
-    args: argparse.Namespace,
-    cfg: Dict[str, Any],
-    dataset: HealthDataset,
-    state_dict: Optional[Dict[str, Any]] = None,
-) -> DeepHealth:
+def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], dataset: HealthDataset) -> DeepHealth:
+    validate_traj_mixer_config(cfg)
    model_target_mode = str(cfg_get(
        args, cfg, "model_target_mode", "next_token")).lower()
    if model_target_mode not in {"next_token", "all_future"}:
        raise ValueError(
            f"model_target_mode must be next_token or all_future, got {model_target_mode!r}"
        )
-    model_architecture = resolve_model_architecture(cfg, state_dict)
    return DeepHealth(
        vocab_size=dataset.vocab_size,
        n_embd=int(cfg_get(args, cfg, "n_embd", 120)),
        n_head=int(cfg_get(args, cfg, "n_head", 10)),
-        n_layer=int(cfg["n_layer"]),
+        n_hist_layer=int(cfg_get(args, cfg, "n_hist_layer", 12)),
+        n_tab_layer=int(cfg_get(args, cfg, "n_tab_layer", 4)),
        n_types=dataset.n_types,
        n_cont_types=dataset.n_cont_types,
        n_categories=dataset.n_categories,
@@ -342,7 +336,6 @@ def build_model_from_dataset(
        time_mode=str(cfg_get(args, cfg, "time_mode", "relative")),
        dist_mode=str(cfg_get(args, cfg, "dist_mode", "exponential")),
        dropout=float(cfg_get(args, cfg, "dropout", 0.0)),
-        model_architecture=model_architecture,
    )


@@ -390,7 +383,7 @@ def resolve_dist_mode_for_checkpoint(cfg_dist_mode: str, state_dict: Dict[str, A


 def load_model_state(
-    model: DeepHealth,
+    model: torch.nn.Module,
    checkpoint_path: str,
    device: torch.device,
    state_dict: Optional[Dict[str, Any]] = None,
@@ -398,7 +391,7 @@ def load_model_state(
    state = state_dict if state_dict is not None else load_checkpoint_state_dict(
        checkpoint_path, map_location=device)

-    resolve_model_architecture(model.model_architecture, state)
+    validate_traj_mixer_state_dict(state)
    model.load_state_dict(state, strict=True)


@@ -1171,23 +1164,30 @@ def evaluate_auc_pipeline(
    df_auc_unpooled["label_code"] = df_auc_unpooled["token"].map(
        dataset.label_id_to_code)

-    print(
-        "Building Delphi2M-style report: mean AUC across age strata, "
-        "reported separately for Female and Male."
+    print("Using DeLong method to calculate AUC confidence intervals.")
+    grouped = df_auc_unpooled.groupby(
+        ["token", "label_code", "offset"], dropna=False, as_index=False)
+    df_auc = grouped.agg(
+        auc=("auc_delong", "mean"),
+        n_strata=("auc_delong", "size"),
+        n_diseased=("n_diseased", "sum"),
+        n_healthy=("n_healthy", "sum"),
+        auc_variance_sum=("auc_variance_delong", "sum"),
    )
-    df_report = build_delphi2m_auc_report(
-        df_auc_unpooled,
-        period_col="offset",
+    df_auc["auc_variance_delong"] = (
+        df_auc["auc_variance_sum"]
+        / (df_auc["n_strata"].clip(lower=1).astype(np.float64) ** 2)
    )
+    df_auc = df_auc.drop(columns=["auc_variance_sum"])

    if output_path is not None:
        out_dir = Path(output_path)
        out_dir.mkdir(parents=True, exist_ok=True)
-        report_path = out_dir / "df_auc_delphi2m_report.csv"
-        df_report.to_csv(report_path, index=False)
-        print(f"Saved Delphi2M-style AUC report: {report_path}")
+        df_auc.to_csv(out_dir / "df_both.csv", index=False)
+        df_auc_unpooled.to_csv(
+            out_dir / "df_auc_unpooled.csv", index=False)

-    return df_auc_unpooled, df_report
+    return df_auc_unpooled, df_auc


 # ---------------------------------------------------------------------------
@@ -1237,18 +1237,8 @@ def make_auc_offsets(args: argparse.Namespace, cfg: Dict[str, Any]) -> List[floa
    if explicit_offsets is not None:
        base_offsets = explicit_offsets
    else:
-        next_token_offset = float(
-            cfg_get(
-                args,
-                cfg,
-                "offset",
-                DEFAULT_DELPHI2M_PERIODS_YEARS[0],
-            )
-        )
-        base_offsets = [
-            next_token_offset,
-            *DEFAULT_DELPHI2M_PERIODS_YEARS[1:],
-        ]
+        next_token_offset = float(cfg_get(args, cfg, "offset", 0.1))
+        base_offsets = [next_token_offset, 1.0, 5.0, 10.0]

    offsets: List[float] = []
    seen = set()
@@ -1296,9 +1286,9 @@ def main() -> None:
    parser.add_argument("--filter_min_total", type=int, default=None,
                        help="Minimum metadata count for disease selection; default 0.")
    parser.add_argument("--offset", type=float, default=None,
-                        help="Next-token prediction offset in years; 0.1 is Delphi2M no gap and is evaluated alongside 1, 5, and 10 years by default.")
+                        help="Next-token prediction offset in years; preserved and evaluated alongside 1, 5, and 10 years by default.")
    parser.add_argument("--offsets", type=str, default=None,
-                        help="Comma-separated prediction offsets in years. Overrides the default set of 0.1,1,5,10.")
+                        help="Comma-separated prediction offsets in years. Overrides the default set of offset,1,5,10.")
    parser.add_argument("--age_start", type=float, default=None)
    parser.add_argument("--age_stop", type=float, default=None)
    parser.add_argument("--age_step", type=float, default=None)
@@ -1379,19 +1369,14 @@ def main() -> None:
    cfg = dict(cfg)
    cfg["dist_mode"] = dist_mode
    cfg["model_target_mode"] = model_target_mode
-    model_architecture = resolve_model_architecture(cfg, state_dict)
-    cfg["model_architecture"] = model_architecture
    print(f"Resolved dist_mode for evaluation: {dist_mode}")
-    print(f"Resolved model architecture: {model_architecture}")
    print(f"Model target mode for AUC: {model_target_mode}")
    print(
        "AUC score semantics: evaluate_auc.py uses disease-specific eta/logit scores; "
        "dist_mode affects model loading but is not converted to horizon-specific risk probability."
    )

-    model = build_model_from_dataset(
-        args, cfg, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg, dataset).to(device)
    load_model_state(model, str(model_ckpt_path),
                     device, state_dict=state_dict)
    model.eval()
--- a/evaluate_auc_v2.py
+++ b/evaluate_auc_v2.py
@@ -3,9 +3,6 @@
 This script supports DeepHealth fixed-horizon risk scores for exponential,
 Weibull, and mixed all-future distributions.

-The default horizons are 0.1, 1, 5, and 10 years.  As in Delphi2M, 0.1 years
-is reported as the no-gap evaluation.
-
 Landmark querying depends on the model target mode saved in train_config.json:
    - next_token: insert a <NO_EVENT> token at landmark age and read it out;
    - all_future: pass landmark age directly as t_query.
@@ -31,13 +28,12 @@ from torch.utils.data import DataLoader, Dataset
 from tqdm.auto import tqdm

 from dataset import HealthDataset
-from delphi2m_auc_report import (
-    DEFAULT_DELPHI2M_PERIODS_YEARS,
-    build_delphi2m_auc_report,
-)
 from eval_data import load_sequence_eval_dataset
-from model_architectures import resolve_model_architecture
-from models import DeepHealth
+from models import (
+    DeepHealth,
+    validate_traj_mixer_config,
+    validate_traj_mixer_state_dict,
+)
 from readouts import build_readout
 from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX

@@ -185,24 +181,20 @@ def resolve_dist_mode_for_checkpoint(cfg_dist_mode: str, state_dict: Dict[str, A
    return mode if mode in {"exponential", "weibull", "mixed"} else "exponential"


-def build_model_from_dataset(
-    args: argparse.Namespace,
-    cfg: Dict[str, Any],
-    dataset: HealthDataset,
-    state_dict: Optional[Dict[str, Any]] = None,
-) -> DeepHealth:
+def build_model_from_dataset(args: argparse.Namespace, cfg: Dict[str, Any], dataset: HealthDataset) -> DeepHealth:
+    validate_traj_mixer_config(cfg)
    model_target_mode = str(cfg_get(
        args, cfg, "model_target_mode", "next_token")).lower()
    if model_target_mode not in {"next_token", "all_future"}:
        raise ValueError(
            f"model_target_mode must be next_token or all_future, got {model_target_mode!r}"
        )
-    model_architecture = resolve_model_architecture(cfg, state_dict)
    return DeepHealth(
        vocab_size=dataset.vocab_size,
        n_embd=int(cfg_get(args, cfg, "n_embd", 120)),
        n_head=int(cfg_get(args, cfg, "n_head", 10)),
-        n_layer=int(cfg["n_layer"]),
+        n_hist_layer=int(cfg_get(args, cfg, "n_hist_layer", 12)),
+        n_tab_layer=int(cfg_get(args, cfg, "n_tab_layer", 4)),
        n_types=dataset.n_types,
        n_cont_types=dataset.n_cont_types,
        n_categories=dataset.n_categories,
@@ -213,12 +205,11 @@ def build_model_from_dataset(
        time_mode=str(cfg_get(args, cfg, "time_mode", "relative")),
        dist_mode=str(cfg_get(args, cfg, "dist_mode", "exponential")),
        dropout=float(cfg_get(args, cfg, "dropout", 0.0)),
-        model_architecture=model_architecture,
    )


-def load_model_state(model: DeepHealth, state_dict: Dict[str, Any]) -> None:
-    resolve_model_architecture(model.model_architecture, state_dict)
+def load_model_state(model: torch.nn.Module, state_dict: Dict[str, Any]) -> None:
+    validate_traj_mixer_state_dict(state_dict)
    model.load_state_dict(state_dict, strict=True)


@@ -339,6 +330,44 @@ def _first_existing_column(df: pd.DataFrame, candidates: Sequence[str]) -> Optio
    return None


+def build_metadata_for_merge(dataset: HealthDataset, labels_meta: Optional[pd.DataFrame]) -> pd.DataFrame:
+    base_rows = []
+    for token, code in dataset.label_id_to_code.items():
+        token = int(token)
+        code_text = str(code)
+        if token in SPECIAL_TOKENS or code_text.startswith("<"):
+            continue
+        base_rows.append({"token": token, "label_code": code_text})
+    base = pd.DataFrame(base_rows)
+    if labels_meta is None or labels_meta.empty:
+        return base
+
+    meta = labels_meta.copy()
+    code_col = _first_existing_column(
+        meta, ["Name", "code", "ICD10", "icd10", "label", "token", "disease_code"])
+    if code_col is not None:
+        meta["_label_code"] = meta[code_col].astype(
+            str).map(lambda s: s.split()[0].strip())
+        merged = base.merge(meta, left_on="label_code",
+                            right_on="_label_code", how="left")
+        return merged.drop(columns=["_label_code"], errors="ignore")
+
+    if "index" in meta.columns:
+        idx = pd.to_numeric(meta["index"], errors="coerce")
+        has_no_event = (
+            NO_EVENT_IDX in dataset.label_id_to_code
+            and dataset.label_id_to_code.get(NO_EVENT_IDX) == "<NO_EVENT>"
+        )
+        if has_no_event:
+            idx = idx.where(idx < NO_EVENT_IDX, idx + 1)
+        meta["_index_int"] = idx.astype("Int64")
+        merged = base.merge(meta, left_on="token",
+                            right_on="_index_int", how="left")
+        return merged.drop(columns=["_index_int"], errors="ignore")
+
+    return base
+
+
 def _metadata_count_map(dataset: HealthDataset, labels_meta: Optional[pd.DataFrame]) -> Dict[int, float]:
    if labels_meta is None or labels_meta.empty or "count" not in labels_meta.columns:
        return {}
@@ -1078,6 +1107,7 @@ def evaluate_landmark_auc(
    loader: DataLoader,
    landmark_dataset: LandmarkDataset,
    output_path: Path,
+    labels_meta: Optional[pd.DataFrame],
    disease_ids: Sequence[int],
    disease_chunk_size: int,
    score_mode: str,
@@ -1094,6 +1124,7 @@ def evaluate_landmark_auc(
    use_amp: bool,
    hidden_cache_dtype: str,
    logit_batch_size: int,
+    meta_info: Dict[str, Any],
 ) -> Tuple[pd.DataFrame, pd.DataFrame]:
    model.eval().to(device)

@@ -1210,21 +1241,54 @@ def evaluate_landmark_auc(
    df_unpooled["label_code"] = df_unpooled["token"].map(
        landmark_dataset.dataset.label_id_to_code)

-    print(
-        "Building Delphi2M-style report: mean AUC across landmark-age "
-        "strata, reported separately for Female and Male."
+    for k, v in meta_info.items():
+        df_unpooled[k] = v
+
+    meta_table = build_metadata_for_merge(landmark_dataset.dataset, labels_meta)
+    df_unpooled = df_unpooled.merge(
+        meta_table, on=["token", "label_code"], how="left")
+
+    grouped = df_unpooled.groupby(
+        ["token", "label_code", "horizon"], dropna=False, as_index=False)
+    df_merged = grouped.agg(
+        auc=("auc_delong", "mean"),
+        n_strata=("auc_delong", "size"),
+        n_diseased=("n_diseased", "sum"),
+        n_healthy=("n_healthy", "sum"),
+        auc_variance_sum=("auc_variance_delong", "sum"),
    )
-    df_report = build_delphi2m_auc_report(
-        df_unpooled,
-        period_col="horizon",
+    df_merged["auc_variance_delong"] = (
+        df_merged["auc_variance_sum"]
+        / (df_merged["n_strata"].clip(lower=1).astype(np.float64) ** 2)
    )
+    df_merged = df_merged.drop(columns=["auc_variance_sum"])
+
+    keep_meta = [
+        c for c in [
+            "model_ckpt_path",
+            "config_path",
+            "target_mode",
+            "model_target_mode",
+            "dist_mode",
+            "time_mode",
+            "attn_mask_mode",
+            "readout_name",
+            "landmark_query_mode",
+            "landmark_token_mode",
+            "score_mode",
+            "eval_split",
+        ]
+        if c in df_unpooled.columns
+    ]
+    for col in keep_meta:
+        df_merged[col] = meta_info[col]

    output_path.mkdir(parents=True, exist_ok=True)
-    report_path = output_path / "df_auc_landmark_delphi2m_report.csv"
-    df_report.to_csv(report_path, index=False)
-    print(f"Saved Delphi2M-style landmark AUC report: {report_path}")
+    df_unpooled.to_csv(
+        output_path / "df_auc_landmark_unpooled.csv", index=False)
+    df_merged.to_csv(output_path / "df_auc_landmark.csv", index=False)

-    return df_unpooled, df_report
+    return df_unpooled, df_merged


 def main() -> None:
@@ -1250,12 +1314,7 @@ def main() -> None:
    parser.add_argument("--landmark_start", type=float, default=None)
    parser.add_argument("--landmark_stop", type=float, default=None)
    parser.add_argument("--landmark_step", type=float, default=None)
-    parser.add_argument(
-        "--horizons",
-        type=str,
-        default=None,
-        help="Comma-separated horizons in years; defaults to 0.1,1,5,10, where 0.1 is Delphi2M no gap.",
-    )
+    parser.add_argument("--horizons", type=str, default=None)

    parser.add_argument("--min_cases", type=int, default=None)
    parser.add_argument("--min_history_events", type=int, default=None)
@@ -1375,9 +1434,8 @@ def main() -> None:
            "Landmark ages are empty. Check landmark_start/landmark_stop/landmark_step.")

    horizons = np.asarray(
-        parse_float_list(
-            cfg_get(args, cfg, "horizons", "0.1,1,5,10")
-        ) or list(DEFAULT_DELPHI2M_PERIODS_YEARS),
+        parse_float_list(cfg_get(args, cfg, "horizons", "1,5,10")) or [
+            1.0, 5.0, 10.0],
        dtype=np.float32,
    )
    if horizons.size == 0:
@@ -1400,17 +1458,12 @@ def main() -> None:

    cfg_model = dict(cfg)
    cfg_model["dist_mode"] = dist_mode
-    model_architecture = resolve_model_architecture(cfg_model, state_dict)
-    cfg_model["model_architecture"] = model_architecture
-    print(f"Resolved model architecture: {model_architecture}")

    device = resolve_eval_device(args.device)
    if device.type == "cuda":
        torch.backends.cudnn.benchmark = True

-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)

    if (
        model_target_mode == "next_token"
@@ -1473,6 +1526,8 @@ def main() -> None:
        if model_target_mode == "next_token"
        else "direct_t_query"
    )
+    score_mode_out = f"{landmark_query_mode}_{score_mode}"
+
    num_workers_auc = int(
        cfg_get(args, cfg, "num_workers_auc", max(1, (os.cpu_count() or 2) - 1)))
    auc_task_chunk_size = int(cfg_get(args, cfg, "auc_task_chunk_size", 0))
@@ -1504,11 +1559,27 @@ def main() -> None:
    print(f"AUC workers: {num_workers_auc}")
    print(f"Output path: {output_path}")

+    meta_info = {
+        "score_mode": score_mode_out,
+        "eval_split": eval_split,
+        "model_ckpt_path": str(model_ckpt_path),
+        "config_path": str(config_path),
+        "target_mode": str(target_mode),
+        "model_target_mode": str(model_target_mode),
+        "dist_mode": str(dist_mode),
+        "time_mode": str(time_mode),
+        "attn_mask_mode": str(attn_mask_mode),
+        "readout_name": str(readout_name),
+        "landmark_query_mode": landmark_query_mode,
+        "landmark_token_mode": "no_event" if model_target_mode == "next_token" else "none",
+    }
+
    evaluate_landmark_auc(
        model=model,
        loader=loader,
        landmark_dataset=landmark_dataset,
        output_path=output_path,
+        labels_meta=labels_meta,
        disease_ids=disease_ids,
        disease_chunk_size=disease_chunk_size,
        score_mode=score_mode,
@@ -1525,6 +1596,7 @@ def main() -> None:
        use_amp=use_amp,
        hidden_cache_dtype=hidden_cache_dtype,
        logit_batch_size=logit_batch_size,
+        meta_info=meta_info,
    )


--- a/evaluate_event_free_survival.py
+++ b/evaluate_event_free_survival.py
@@ -649,9 +649,7 @@ def main() -> None:
    cfg_model = dict(cfg)
    cfg_model["dist_mode"] = dist_mode
    device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
    load_model_state(model, state_dict)
    model.eval()

--- a/evaluate_extra_info_attribution.py
+++ b/evaluate_extra_info_attribution.py
@@ -758,9 +758,7 @@ def main() -> None:
    cfg_model = dict(cfg)
    cfg_model["dist_mode"] = dist_mode
    device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
    load_model_state(model, state_dict)
    model.eval()

--- a/evaluate_single_disease_mortality_attribution.py
+++ b/evaluate_single_disease_mortality_attribution.py
@@ -553,9 +553,7 @@ def main() -> None:
    device = resolve_eval_device(args.device)
    selected_token_mask = np.zeros(int(dataset.vocab_size), dtype=bool)
    selected_token_mask[np.asarray(scanned_disease_tokens, dtype=np.int64)] = True
-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
    load_model_state(model, state_dict)
    model.eval()

--- a/export_tquery_logits_hidden.py
+++ b/export_tquery_logits_hidden.py
@@ -180,9 +180,7 @@ def main() -> None:
    cfg_model = dict(cfg)
    cfg_model["dist_mode"] = dist_mode
    device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
    load_model_state(model, state_dict)
    model.eval()

--- a/export_weibull_death_parameter_stats.py
+++ b/export_weibull_death_parameter_stats.py
@@ -381,9 +381,7 @@ def main() -> None:
    cfg_model = dict(cfg)
    cfg_model["dist_mode"] = dist_mode
    device = resolve_eval_device(args.device)
-    model = build_model_from_dataset(
-        args, cfg_model, dataset, state_dict=state_dict
-    ).to(device)
+    model = build_model_from_dataset(args, cfg_model, dataset).to(device)
    load_model_state(model, state_dict)
    model.eval()

--- a/model_architectures.py
+++ b/model_architectures.py
@@ -1,131 +0,0 @@
-"""Model-architecture identifiers and checkpoint validation helpers."""
-
-from __future__ import annotations
-
-import re
-from collections.abc import Mapping
-
-
-TRANSFORMER_FFN_ARCHITECTURE = "transformer_ffn_v1"
-TRAJ_MIXER_ARCHITECTURE = "traj_mixer_v5"
-DEFAULT_MODEL_ARCHITECTURE = TRANSFORMER_FFN_ARCHITECTURE
-SUPPORTED_MODEL_ARCHITECTURES = (
-    TRANSFORMER_FFN_ARCHITECTURE,
-    TRAJ_MIXER_ARCHITECTURE,
-)
-
-
-_FFN_STATE_KEY = re.compile(
-    r"(?:^|\.)blocks\.\d+\.mlp\.w[123]\.(?:weight|bias)$"
-)
-_TRAJ_MIXER_STATE_KEY = re.compile(
-    r"(?:^|\.)blocks\.\d+\.mlp\.(?:"
-    r"norm\.(?:weight|bias)|"
-    r"intra_gate_proj|"
-    r"intra_value_proj|"
-    r"intra_output_proj|"
-    r"intra_gate_logits|"
-    r"gate_proj|"
-    r"value_proj|"
-    r"output_proj"
-    r")$"
-)
-
-
-def _validate_model_architecture(model_architecture: object) -> str:
-    if not isinstance(model_architecture, str):
-        raise ValueError(
-            "model_architecture must be one of "
-            f"{SUPPORTED_MODEL_ARCHITECTURES}, got {model_architecture!r}"
-        )
-    if model_architecture not in SUPPORTED_MODEL_ARCHITECTURES:
-        raise ValueError(
-            f"Unsupported model_architecture={model_architecture!r}; "
-            f"expected one of {SUPPORTED_MODEL_ARCHITECTURES}."
-        )
-    return model_architecture
-
-
-def detect_model_architecture_from_state_dict(
-    state_dict: Mapping[str, object],
-) -> str:
-    """Infer the architecture from block parameter names.
-
-    Detection deliberately accepts any ``blocks.<index>`` prefix rather than
-    assuming that block zero is present.
-    """
-
-    if not isinstance(state_dict, Mapping):
-        raise TypeError(
-            "state_dict must be a mapping, got "
-            f"{type(state_dict).__name__}"
-        )
-
-    has_ffn = False
-    has_traj_mixer = False
-    for raw_key in state_dict:
-        key = str(raw_key)
-        has_ffn = has_ffn or _FFN_STATE_KEY.search(key) is not None
-        has_traj_mixer = (
-            has_traj_mixer
-            or _TRAJ_MIXER_STATE_KEY.search(key) is not None
-        )
-        if has_ffn and has_traj_mixer:
-            raise ValueError(
-                "Checkpoint contains both Transformer FFN and TrajMixer "
-                "block parameters; its model architecture is ambiguous."
-            )
-
-    if has_ffn:
-        return TRANSFORMER_FFN_ARCHITECTURE
-    if has_traj_mixer:
-        return TRAJ_MIXER_ARCHITECTURE
-    raise ValueError(
-        "Could not detect model architecture from checkpoint parameters. "
-        "Expected a blocks.<index>.mlp FFN or TrajMixer parameter."
-    )
-
-
-def resolve_model_architecture(
-    config_or_marker: Mapping[str, object] | str | None = None,
-    state_dict: Mapping[str, object] | None = None,
-) -> str:
-    """Resolve and cross-check a configured and checkpoint architecture.
-
-    Every saved run must provide an explicit architecture marker. Checkpoint
-    parameter names are used only to verify that the marker describes the
-    weights being loaded.
-    """
-
-    if isinstance(config_or_marker, Mapping):
-        configured = config_or_marker.get("model_architecture")
-    elif isinstance(config_or_marker, str) or config_or_marker is None:
-        configured = config_or_marker
-    else:
-        raise TypeError(
-            "config_or_marker must be a config mapping, string, or None, got "
-            f"{type(config_or_marker).__name__}"
-        )
-
-    resolved_config = (
-        _validate_model_architecture(configured)
-        if configured is not None
-        else None
-    )
-    detected = (
-        detect_model_architecture_from_state_dict(state_dict)
-        if state_dict is not None
-        else None
-    )
-
-    if resolved_config is None:
-        raise ValueError(
-            "model_architecture is required; expected one of "
-            f"{SUPPORTED_MODEL_ARCHITECTURES}."
-        )
-    if detected is not None and resolved_config != detected:
-        raise ValueError(
-            "Configured model architecture conflicts with checkpoint: "
-            f"config={resolved_config!r}, checkpoint={detected!r}."
-        )
-    return resolved_config
--- a/models.py
+++ b/models.py
@@ -1,3 +1,4 @@
+from collections.abc import Mapping
 from dataclasses import dataclass

 import torch
@@ -6,15 +7,41 @@ import torch.nn.functional as F

 from backbones import (
    AgeSinusoidalEncoding,
+    GPTBlock,
    GaussianRBFTimeBasis,
    TimeRoPE,
    TokenAutoDiscretization,
-    build_backbone_block,
 )
-from model_architectures import resolve_model_architecture
 from targets import PAD_IDX


+TRAJ_MIXER_ARCHITECTURE = "traj_mixer_v2"
+
+
+def validate_traj_mixer_config(config: Mapping[str, object]) -> None:
+    actual = config.get("model_architecture")
+    if actual != TRAJ_MIXER_ARCHITECTURE:
+        raise ValueError(
+            "This branch only accepts models trained with the TrajMixer "
+            f"architecture marker {TRAJ_MIXER_ARCHITECTURE!r}; got {actual!r}."
+        )
+
+
+def validate_traj_mixer_state_dict(state_dict: Mapping[str, object]) -> None:
+    required_keys = {
+        "blocks.0.mlp.group_align",
+        "blocks.0.mlp.gate_proj",
+        "blocks.0.mlp.value_proj",
+        "blocks.0.mlp.output_proj",
+    }
+    missing = sorted(required_keys.difference(state_dict))
+    if missing:
+        raise ValueError(
+            "Checkpoint is not a TrajMixer checkpoint; missing required "
+            f"parameters: {', '.join(missing)}"
+        )
+
+
@dataclass
 class DeepHealthOutput:
    hidden: torch.Tensor
@@ -148,7 +175,8 @@ class DeepHealth(nn.Module):
        vocab_size: int,
        n_embd: int,
        n_head: int,
-        n_layer: int,
+        n_hist_layer: int,
+        n_tab_layer: int,
        n_types: int,
        n_cont_types: int,
        n_categories: int,
@@ -160,7 +188,6 @@ class DeepHealth(nn.Module):
        dist_mode: str = "exponential",  # "exponential", "weibull" or "mixed"
        extra_pool_reduce: str = "mean",
        dropout: float = 0.0,
-        model_architecture: str | None = None,
    ):
        super().__init__()
        if target_mode not in ["next_token", "all_future"]:
@@ -174,9 +201,6 @@ class DeepHealth(nn.Module):
                "dist_mode must be either 'exponential', 'weibull' or 'mixed'")
        if extra_pool_reduce not in {"mean", "sum"}:
            raise ValueError("extra_pool_reduce must be either 'mean' or 'sum'")
-        if n_layer < 1:
-            raise ValueError(f"n_layer must be >= 1, got {n_layer}")
-        model_architecture = resolve_model_architecture(model_architecture)
        self.token_embedding = nn.Embedding(vocab_size, n_embd, padding_idx=0)
        self.gender_embedding = nn.Embedding(
            2, n_embd)  # Assuming binary gender
@@ -193,8 +217,6 @@ class DeepHealth(nn.Module):
        self.time_mode = time_mode
        self.dist_mode = dist_mode
        self.extra_pool_reduce = extra_pool_reduce
-        self.model_architecture = model_architecture
-        self.n_layer = n_layer
        self.n_embd = n_embd
        self.vocab_size = vocab_size
        nn.init.normal_(self.token_embedding.weight, mean=0.0, std=0.02)
@@ -214,28 +236,26 @@ class DeepHealth(nn.Module):
        if time_mode == "absolute":
            self.age_encoding = AgeSinusoidalEncoding(n_embd)
            self.blocks = nn.ModuleList([
-                build_backbone_block(
-                    model_architecture,
+                GPTBlock(
                    n_embd=n_embd,
                    n_head=n_head,
                    use_time_rope=False,
                    use_rbf_bias=False,
                    mlp_dropout=dropout,
-                ) for _ in range(n_layer)
+                ) for _ in range(n_hist_layer)
            ])
            self.rope = None
            self.rbf = None
        elif time_mode == "relative":
            self.age_encoding = None
            self.blocks = nn.ModuleList([
-                build_backbone_block(
-                    model_architecture,
+                GPTBlock(
                    n_embd=n_embd,
                    n_head=n_head,
                    use_time_rope=True,
                    use_rbf_bias=True,
                    mlp_dropout=dropout,
-                ) for _ in range(n_layer)
+                ) for _ in range(n_hist_layer)
            ])
            self.rope = TimeRoPE(n_embd // n_head)
            self.rbf = GaussianRBFTimeBasis(n_bases=16, max_time_diff=40.0)
--- a/run_missing_evaluations.sh
+++ b/run_missing_evaluations.sh
@@ -1,11 +1,10 @@
 #!/usr/bin/env bash
 set -euo pipefail

-# Run all non-wrapper evaluation scripts for every completed current-format
-# experiment under runs/. The script is written for Linux servers with bash 4.2.
+# Run all non-wrapper evaluation scripts for every completed experiment under
+# runs/.  The script is written for Linux servers with bash 4.2.

 cd "$(dirname "${BASH_SOURCE[0]}")"
-shopt -s globstar nullglob

 PYTHON_BIN="${PYTHON_BIN:-python}"
 DEVICE="${DEVICE:-cuda}"
@@ -82,20 +81,6 @@ run_dir_result_if_missing() {
  run_command "$@"
 }

-run_file_result_if_missing() {
-  local label="$1"
-  local result_dir="$2"
-  local required="$3"
-  shift 3
-
-  if [[ -s "${result_dir}/${required}" ]]; then
-    echo "    skip ${label}: found ${result_dir}/${required}"
-    return 0
-  fi
-
-  run_command "$@"
-}
-
 run_has_extra_info() {
  "${PYTHON_BIN}" - "$1" <<'PY'
 import json
@@ -130,30 +115,8 @@ raise SystemExit(0 if mode == "all_future" else 1)
 PY
 }

-run_has_current_model_config() {
-  "${PYTHON_BIN}" - "$1" <<'PY'
-import json
-import sys
-from pathlib import Path
-
-cfg_path = Path(sys.argv[1]) / "train_config.json"
-try:
-    cfg = json.loads(cfg_path.read_text(encoding="utf-8"))
-    n_layer = int(cfg.get("n_layer", 0))
-except Exception:
-    raise SystemExit(1)
-
-supported = {"transformer_ffn_v1", "traj_mixer_v5"}
-raise SystemExit(
-    0
-    if cfg.get("model_architecture") in supported and n_layer >= 1
-    else 1
-)
-PY
-}
-
-for config_path in runs/**/train_config.json; do
-  run_path="${config_path%/train_config.json}"
+for run_path in runs/*; do
+  [[ -d "${run_path}" ]] || continue

  echo "==> ${run_path}"
  if [[ ! -f "${run_path}/train_config.json" ]]; then
@@ -164,10 +127,6 @@ for config_path in runs/**/train_config.json; do
    echo "    skip run: missing best_model.pt"
    continue
  fi
-  if ! run_has_current_model_config "${run_path}"; then
-    echo "    skip run: config lacks current model_architecture/n_layer fields"
-    continue
-  fi

  common=()
  while IFS= read -r arg; do common+=("${arg}"); done < <(common_args_with_device "${run_path}")
@@ -178,16 +137,18 @@ for config_path in runs/**/train_config.json; do
  cpu_reduce_extra=()
  while IFS= read -r arg; do cpu_reduce_extra+=("${arg}"); done < <(cpu_reduce_args)

-  run_file_result_if_missing \
+  run_dir_result_if_missing \
    "evaluate_auc.py" \
    "${run_path}" \
-    "df_auc_delphi2m_report.csv" \
+    "df_both.csv" \
+    "df_auc_unpooled.csv" \
    "${PYTHON_BIN}" evaluate_auc.py "${common[@]}" "${auc_extra[@]}"

-  run_file_result_if_missing \
+  run_dir_result_if_missing \
    "evaluate_auc_v2.py" \
    "${run_path}" \
-    "df_auc_landmark_delphi2m_report.csv" \
+    "df_auc_landmark.csv" \
+    "df_auc_landmark_unpooled.csv" \
    "${PYTHON_BIN}" evaluate_auc_v2.py "${common[@]}" "${auc_extra[@]}"

  if ! run_is_all_future "${run_path}"; then
--- a/run_missing_training_runs.sh
+++ b/run_missing_training_runs.sh
@@ -10,8 +10,7 @@ set -euo pipefail
 #   all_future + relative time + mixed death/risk head
 #
 # This script only launches those missing training jobs. It intentionally does
-# not call evaluate_*.py and does not add extra random seeds. Set
-# MODEL_ARCHITECTURE=traj_mixer_v5 to run the TrajMixer variant.
+# not call evaluate_*.py and does not add extra random seeds.

 cd "$(dirname "${BASH_SOURCE[0]}")"

@@ -19,8 +18,6 @@ PYTHON_BIN="${PYTHON_BIN:-python}"
 DEVICE="${DEVICE:-cuda}"
 NUM_WORKERS="${NUM_WORKERS:-4}"
 PROGRESS_INTERVAL="${PROGRESS_INTERVAL:-20}"
-MODEL_ARCHITECTURE="${MODEL_ARCHITECTURE:-transformer_ffn_v1}"
-N_LAYER="${N_LAYER:-12}"

 TIME_MODE="relative"
 DIST_MODE="mixed"
@@ -39,8 +36,8 @@ COMMON_ARGS=(
  --min_future_events 1
  --n_embd 120
  --n_head 10
-  --n_layer "${N_LAYER}"
-  --model_architecture "${MODEL_ARCHITECTURE}"
+  --n_hist_layer 12
+  --n_tab_layer 4
  --n_bins 16
  --extra_pool_reduce mean
  --dropout 0.0
@@ -60,23 +57,15 @@ COMMON_ARGS=(

 already_trained() {
  local extra_file="$1"
-  "${PYTHON_BIN}" - "$TIME_MODE" "$DIST_MODE" "$extra_file" "$SEED" "$VALIDATION_QUERY_SEED" "$MODEL_ARCHITECTURE" "$N_LAYER" <<'PY'
+  "${PYTHON_BIN}" - "$TIME_MODE" "$DIST_MODE" "$extra_file" "$SEED" "$VALIDATION_QUERY_SEED" <<'PY'
 import json
 import sys
 from pathlib import Path

-(
-    time_mode,
-    dist_mode,
-    extra_file,
-    seed,
-    validation_query_seed,
-    model_architecture,
-    n_layer,
-) = sys.argv[1:8]
+time_mode, dist_mode, extra_file, seed, validation_query_seed = sys.argv[1:6]
 extra_name = Path(extra_file).name

-for config_path in Path("runs").rglob("train_config.json"):
+for config_path in Path("runs").glob("*/train_config.json"):
    try:
        cfg = json.loads(config_path.read_text(encoding="utf-8"))
    except Exception:
@@ -89,8 +78,6 @@ for config_path in Path("runs").rglob("train_config.json"):

    if (
        cfg.get("model_target_mode") == "all_future"
-        and cfg.get("model_architecture") == model_architecture
-        and int(cfg.get("n_layer", -1)) == int(n_layer)
        and cfg.get("time_mode") == time_mode
        and cfg.get("dist_mode") == dist_mode
        and Path(str(cfg.get("extra_info_types_file", ""))).name == extra_name
@@ -113,7 +100,7 @@ train_if_missing() {
    return 2
  fi

-  echo "==> Checking ${label}: ${MODEL_ARCHITECTURE} n_layer=${N_LAYER} ${TIME_MODE} ${DIST_MODE} all_future with ${extra_file}"
+  echo "==> Checking ${label}: ${TIME_MODE} ${DIST_MODE} all_future with ${extra_file}"
  if existing_run="$(already_trained "$extra_file")"; then
    echo "    skip: already trained at ${existing_run}"
    return 0
--- a/test_model_architectures.py
+++ b/test_model_architectures.py
@@ -1,247 +0,0 @@
-import unittest
-
-import torch
-
-from backbones import (
-    SwiGLU,
-    TrajMixer,
-    TrajMixerBlock,
-    TransformerFFNBlock,
-    build_backbone_block,
-)
-from model_architectures import (
-    TRAJ_MIXER_ARCHITECTURE,
-    TRANSFORMER_FFN_ARCHITECTURE,
-    detect_model_architecture_from_state_dict,
-    resolve_model_architecture,
-)
-from models import DeepHealth
-
-
-def _build_block(model_architecture: str):
-    return build_backbone_block(
-        model_architecture,
-        n_embd=12,
-        n_head=3,
-        use_time_rope=False,
-        use_rbf_bias=False,
-        mlp_dropout=0.0,
-    )
-
-
-def _as_model_state_dict(block: torch.nn.Module) -> dict[str, torch.Tensor]:
-    return {
-        f"blocks.0.{name}": value.detach().clone()
-        for name, value in block.state_dict().items()
-    }
-
-
-def _build_model(
-    model_architecture: str | None,
-    *,
-    n_layer: int = 1,
-) -> DeepHealth:
-    return DeepHealth(
-        vocab_size=8,
-        n_embd=12,
-        n_head=3,
-        n_layer=n_layer,
-        n_types=2,
-        n_cont_types=0,
-        n_categories=2,
-        cont_type_ids=[],
-        time_mode="absolute",
-        model_architecture=model_architecture,
-    )
-
-
-class ModelArchitectureFactoryTest(unittest.TestCase):
-    def test_factory_builds_both_architectures_with_expected_topology(self) -> None:
-        ffn_block = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
-        self.assertIsInstance(ffn_block, TransformerFFNBlock)
-        self.assertIsInstance(ffn_block.mlp, SwiGLU)
-        self.assertTrue(hasattr(ffn_block, "ln1"))
-        self.assertTrue(hasattr(ffn_block, "ln2"))
-
-        traj_block = _build_block(TRAJ_MIXER_ARCHITECTURE)
-        self.assertIsInstance(traj_block, TrajMixerBlock)
-        self.assertIsInstance(traj_block.mlp, TrajMixer)
-        self.assertTrue(hasattr(traj_block, "ln1"))
-        self.assertFalse(hasattr(traj_block, "ln2"))
-
-    def test_both_architectures_forward_and_backward(self) -> None:
-        for architecture in (
-            TRANSFORMER_FFN_ARCHITECTURE,
-            TRAJ_MIXER_ARCHITECTURE,
-        ):
-            with self.subTest(architecture=architecture):
-                torch.manual_seed(0)
-                block = _build_block(architecture)
-                x = torch.randn(2, 5, 12, requires_grad=True)
-
-                output = block(x)
-                self.assertEqual(output.shape, x.shape)
-                output.square().mean().backward()
-
-                self.assertIsNotNone(x.grad)
-                self.assertTrue(torch.isfinite(x.grad).all())
-                self.assertGreater(x.grad.abs().sum().item(), 0.0)
-                self.assertIsNotNone(block.attn.qkv.weight.grad)
-                self.assertGreater(
-                    block.attn.qkv.weight.grad.abs().sum().item(),
-                    0.0,
-                )
-
-                if architecture == TRANSFORMER_FFN_ARCHITECTURE:
-                    branch_parameters = (
-                        block.mlp.w1.weight,
-                        block.mlp.w2.weight,
-                        block.mlp.w3.weight,
-                    )
-                else:
-                    branch_parameters = (
-                        block.mlp.intra_gate_proj,
-                        block.mlp.intra_value_proj,
-                        block.mlp.output_proj,
-                    )
-                for parameter in branch_parameters:
-                    self.assertIsNotNone(parameter.grad)
-                    self.assertTrue(torch.isfinite(parameter.grad).all())
-                    self.assertGreater(parameter.grad.abs().sum().item(), 0.0)
-
-    def test_unknown_architecture_is_rejected(self) -> None:
-        with self.assertRaises(ValueError):
-            _build_block("unknown_architecture")
-        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
-            _build_model(None)
-
-    def test_deephealth_rejects_fewer_than_one_layer(self) -> None:
-        for n_layer in (0, -1):
-            with self.subTest(n_layer=n_layer):
-                with self.assertRaisesRegex(ValueError, "n_layer must be >= 1"):
-                    _build_model(
-                        TRANSFORMER_FFN_ARCHITECTURE,
-                        n_layer=n_layer,
-                    )
-
-    def test_deephealth_uses_factory_and_strictly_reloads_both_models(self) -> None:
-        for architecture, block_class in (
-            (TRANSFORMER_FFN_ARCHITECTURE, TransformerFFNBlock),
-            (TRAJ_MIXER_ARCHITECTURE, TrajMixerBlock),
-        ):
-            with self.subTest(architecture=architecture):
-                model = _build_model(architecture)
-                self.assertEqual(model.model_architecture, architecture)
-                self.assertIsInstance(model.blocks[0], block_class)
-                self.assertEqual(
-                    detect_model_architecture_from_state_dict(
-                        model.state_dict()
-                    ),
-                    architecture,
-                )
-
-                reloaded = _build_model(architecture)
-                incompatible = reloaded.load_state_dict(
-                    model.state_dict(),
-                    strict=True,
-                )
-                self.assertEqual(incompatible.missing_keys, [])
-                self.assertEqual(incompatible.unexpected_keys, [])
-
-
-class ModelArchitectureResolutionTest(unittest.TestCase):
-    def setUp(self) -> None:
-        self.ffn_block = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
-        self.traj_block = _build_block(TRAJ_MIXER_ARCHITECTURE)
-        self.ffn_state = _as_model_state_dict(self.ffn_block)
-        self.traj_state = _as_model_state_dict(self.traj_block)
-
-    def test_state_dict_detection_recognizes_both_architectures(self) -> None:
-        self.assertEqual(
-            detect_model_architecture_from_state_dict(self.ffn_state),
-            TRANSFORMER_FFN_ARCHITECTURE,
-        )
-        self.assertEqual(
-            detect_model_architecture_from_state_dict(self.traj_state),
-            TRAJ_MIXER_ARCHITECTURE,
-        )
-
-    def test_explicit_markers_resolve_when_checkpoint_matches(self) -> None:
-        for architecture, state_dict in (
-            (TRANSFORMER_FFN_ARCHITECTURE, self.ffn_state),
-            (TRAJ_MIXER_ARCHITECTURE, self.traj_state),
-        ):
-            with self.subTest(architecture=architecture):
-                self.assertEqual(
-                    resolve_model_architecture(
-                        {"model_architecture": architecture},
-                        state_dict,
-                    ),
-                    architecture,
-                )
-
-    def test_architecture_marker_is_required_for_checkpoint_loading(self) -> None:
-        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
-            resolve_model_architecture({}, self.ffn_state)
-        with self.assertRaisesRegex(ValueError, "model_architecture is required"):
-            resolve_model_architecture(None, self.traj_state)
-
-    def test_explicit_marker_conflicting_with_state_dict_is_rejected(self) -> None:
-        conflicts = (
-            (TRANSFORMER_FFN_ARCHITECTURE, self.traj_state),
-            (TRAJ_MIXER_ARCHITECTURE, self.ffn_state),
-        )
-        for architecture, state_dict in conflicts:
-            with self.subTest(architecture=architecture):
-                with self.assertRaises(ValueError):
-                    resolve_model_architecture(
-                        {"model_architecture": architecture},
-                        state_dict,
-                    )
-
-    def test_unknown_marker_and_ambiguous_state_dict_are_rejected(self) -> None:
-        with self.assertRaises(ValueError):
-            resolve_model_architecture(
-                {"model_architecture": "traj_mixer_v4"}
-            )
-
-        ambiguous_state = dict(self.ffn_state)
-        ambiguous_state.update(self.traj_state)
-        with self.assertRaises(ValueError):
-            detect_model_architecture_from_state_dict(ambiguous_state)
-
-        with self.assertRaises(ValueError):
-            detect_model_architecture_from_state_dict(
-                {"token_embedding.weight": torch.empty(2, 2)}
-            )
-
-    def test_ffn_block_schema_is_stable_and_strictly_loadable(self) -> None:
-        expected_keys = {
-            "attn.time_bias_scale",
-            "attn.qkv.weight",
-            "attn.out_proj.weight",
-            "attn.rbf_proj.weight",
-            "mlp.w1.weight",
-            "mlp.w1.bias",
-            "mlp.w2.weight",
-            "mlp.w2.bias",
-            "mlp.w3.weight",
-            "mlp.w3.bias",
-            "ln1.weight",
-            "ln1.bias",
-            "ln2.weight",
-            "ln2.bias",
-        }
-        state = self.ffn_block.state_dict()
-        self.assertSetEqual(set(state), expected_keys)
-        self.assertEqual(tuple(state["mlp.w1.weight"].shape), (30, 12))
-        self.assertEqual(tuple(state["mlp.w3.weight"].shape), (12, 30))
-
-        reloaded = _build_block(TRANSFORMER_FFN_ARCHITECTURE)
-        incompatible = reloaded.load_state_dict(state, strict=True)
-        self.assertEqual(incompatible.missing_keys, [])
-        self.assertEqual(incompatible.unexpected_keys, [])
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/test_temporal_attention.py
+++ b/test_temporal_attention.py
@@ -1,46 +0,0 @@
-import unittest
-
-import torch
-
-from backbones import TemporalAttention
-
-
-class TemporalAttentionTest(unittest.TestCase):
-    def test_zero_rbf_bias_has_live_projection_gradient(self) -> None:
-        torch.manual_seed(0)
-        attention = TemporalAttention(
-            n_embd=12,
-            n_head=3,
-            use_time_rope=False,
-            use_rbf_bias=True,
-        )
-        features = torch.randn(2, 4, 4, 16)
-        target = torch.randn(2, 4, 4, 3)
-
-        initial_bias = (
-            attention.time_bias_scale.tanh()
-            * attention.rbf_proj(features)
-        )
-        torch.testing.assert_close(initial_bias, torch.zeros_like(initial_bias))
-
-        (initial_bias * target).sum().backward()
-        projection_grad = attention.rbf_proj.weight.grad
-        self.assertIsNotNone(projection_grad)
-        self.assertGreater(projection_grad.abs().sum().item(), 0.0)
-
-        with torch.no_grad():
-            attention.rbf_proj.weight.add_(projection_grad, alpha=-1e-3)
-        attention.zero_grad(set_to_none=True)
-        updated_bias = (
-            attention.time_bias_scale.tanh()
-            * attention.rbf_proj(features)
-        )
-        (updated_bias * target).sum().backward()
-
-        scale_grad = attention.time_bias_scale.grad
-        self.assertIsNotNone(scale_grad)
-        self.assertGreater(scale_grad.abs().item(), 0.0)
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/test_traj_mixer.py
+++ b/test_traj_mixer.py
@@ -2,7 +2,13 @@ import unittest

 import torch

-from backbones import TrajMixer
+from backbones import GPTBlock, TrajMixer
+from models import (
+    TRAJ_MIXER_ARCHITECTURE,
+    validate_traj_mixer_config,
+    validate_traj_mixer_state_dict,
+)
+from train_util import get_model_parameter_counts


 class TrajMixerTest(unittest.TestCase):
@@ -15,113 +21,20 @@ class TrajMixerTest(unittest.TestCase):

        x = torch.randn(2, 7, 120)
        self.assertEqual(mixer(x).shape, x.shape)
-        self.assertEqual(sum(p.numel() for p in mixer.parameters()), 32_040)
-        self.assertEqual(tuple(mixer.norm.normalized_shape), (120,))
-        self.assertEqual(tuple(mixer.intra_gate_logits.shape), (10, 12))
-        torch.testing.assert_close(
-            torch.sigmoid(mixer.intra_gate_logits.detach()),
-            torch.full((10, 12), 0.1),
-        )
-        self.assertEqual(mixer.intra_hidden, 48)
-        self.assertEqual(
-            tuple(mixer.intra_gate_proj.shape),
-            (10, 12, 48),
-        )
-        self.assertEqual(
-            tuple(mixer.intra_value_proj.shape),
-            (10, 12, 48),
-        )
-        self.assertEqual(
-            tuple(mixer.intra_output_proj.shape),
-            (10, 48, 12),
-        )
+        self.assertEqual(sum(p.numel() for p in mixer.parameters()), 15_840)
+
+        expected = torch.eye(12).expand(10, 12, 12)
+        torch.testing.assert_close(mixer.group_align.detach(), expected)
        self.assertEqual(mixer.hidden_group, 40)
        self.assertEqual(tuple(mixer.gate_proj.shape), (12, 10, 40))
        self.assertEqual(tuple(mixer.value_proj.shape), (12, 10, 40))
        self.assertEqual(tuple(mixer.output_proj.shape), (12, 40, 10))

-    def test_zero_final_output_projection_makes_mixer_identity(self) -> None:
-        torch.manual_seed(0)
-        mixer = TrajMixer(12, n_head=3, dropout=0.0)
-        with torch.no_grad():
-            mixer.output_proj.zero_()
-        x = torch.randn(2, 5, 12)
-        torch.testing.assert_close(mixer(x), x)
-
-    def test_forward_matches_single_outer_residual_formula(self) -> None:
-        torch.manual_seed(0)
-        mixer = TrajMixer(12, n_head=3, dropout=0.0)
-        mixer.eval()
-        x = torch.randn(2, 5, 12)
-
-        grouped = mixer.norm(x).reshape(2, 5, 3, 4)
-        intra_output = mixer._intra_mix(grouped)
-        static_gate = torch.sigmoid(mixer.intra_gate_logits).view(
-            1, 1, 3, 4
-        )
-        mixed_input = grouped + static_gate * intra_output
-        update = mixer._cross_mix(mixed_input).reshape(2, 5, 12)
-
-        torch.testing.assert_close(mixer(x), x + update)
-
-    def test_intra_stage_is_independent_across_groups(self) -> None:
-        torch.manual_seed(0)
-        mixer = TrajMixer(12, n_head=3, dropout=0.0)
-        mixer.eval()
-
-        grouped = torch.randn(2, 4, 3, 4)
-        changed = grouped.clone()
-        changed[:, :, 1, :] += torch.randn_like(changed[:, :, 1, :])
-
-        original_out = mixer._intra_mix(grouped)
-        changed_out = mixer._intra_mix(changed)
-        unchanged_groups = torch.tensor([0, 2])
-        torch.testing.assert_close(
-            original_out.index_select(2, unchanged_groups),
-            changed_out.index_select(2, unchanged_groups),
-        )
-
-    def test_cross_stage_mixes_groups_without_mixing_coordinates(self) -> None:
-        mixer = TrajMixer(6, n_head=3, dropout=0.0)
-        mixer.eval()
-        with torch.no_grad():
-            mixer.gate_proj.zero_()
-            mixer.value_proj.zero_()
-            mixer.output_proj.zero_()
-
-            # Coordinate 0 reads group 0 through hidden unit 0 and writes it
-            # into group 1. Coordinate 1 must remain independent.
-            mixer.gate_proj[0, 0, 0] = 1.0
-            mixer.value_proj[0, 0, 0] = 1.0
-            mixer.output_proj[0, 0, 1] = 1.0
-
-        grouped = torch.tensor(
-            [[[
-                [-1.0, 4.0],
-                [0.0, 5.0],
-                [1.0, 6.0],
-            ]]]
-        )
-        changed = grouped.clone()
-        changed[0, 0, 0, 0] = 2.0
-
-        original_out = mixer._cross_mix(grouped)
-        changed_out = mixer._cross_mix(changed)
-
-        self.assertNotEqual(
-            original_out[0, 0, 1, 0].item(),
-            changed_out[0, 0, 1, 0].item(),
-        )
-        torch.testing.assert_close(
-            original_out[..., 1],
-            changed_out[..., 1],
-        )
-
    def test_mixer_does_not_mix_sequence_positions(self) -> None:
        torch.manual_seed(0)
-        mixer = TrajMixer(12, n_head=3, dropout=0.0)
+        mixer = TrajMixer(120, n_head=10, dropout=0.0)
        mixer.eval()
-        x = torch.randn(2, 5, 12)
+        x = torch.randn(2, 5, 120)
        changed = x.clone()
        changed[:, 3, :] += torch.randn_like(changed[:, 3, :])

@@ -133,34 +46,62 @@ class TrajMixerTest(unittest.TestCase):
            changed_out.index_select(1, unchanged_positions),
        )

-    def test_gradients_reach_every_projection_family(self) -> None:
+    def test_gradients_reach_all_projection_families(self) -> None:
        torch.manual_seed(1)
-        mixer = TrajMixer(12, n_head=3, dropout=0.0)
-        x = torch.randn(2, 4, 12, requires_grad=True)
+        mixer = TrajMixer(120, n_head=10, dropout=0.0)
+        x = torch.randn(2, 4, 120, requires_grad=True)

        mixer(x).square().mean().backward()

        self.assertIsNotNone(x.grad)
-        self.assertTrue(torch.isfinite(x.grad).all())
-        self.assertGreater(x.grad.abs().sum().item(), 0.0)
-        projection_names = (
-            "intra_gate_proj",
-            "intra_value_proj",
-            "intra_output_proj",
-            "gate_proj",
-            "value_proj",
-            "output_proj",
-        )
-        for name in projection_names:
-            parameter = getattr(mixer, name)
+        for name, parameter in mixer.named_parameters():
            self.assertIsNotNone(parameter.grad, name)
            self.assertTrue(torch.isfinite(parameter.grad).all(), name)
-            self.assertGreater(parameter.grad.abs().sum().item(), 0.0, name)
+
+    def test_gpt_block_defaults_to_traj_mixer_and_standard_layer_norm(self) -> None:
+        block = GPTBlock(n_embd=120, n_head=10)
+        self.assertIsInstance(block.mlp, TrajMixer)
+        self.assertIsInstance(block.ln2, torch.nn.LayerNorm)
+        self.assertEqual(tuple(block.ln2.normalized_shape), (120,))
+
+        x = torch.randn(2, 6, 120)
+        self.assertEqual(block(x).shape, x.shape)
+
+    def test_architecture_marker_is_required(self) -> None:
+        validate_traj_mixer_config(
+            {"model_architecture": TRAJ_MIXER_ARCHITECTURE}
+        )
+        with self.assertRaisesRegex(ValueError, "only accepts models trained"):
+            validate_traj_mixer_config({})
+        with self.assertRaisesRegex(ValueError, "only accepts models trained"):
+            validate_traj_mixer_config({"model_architecture": "delphi_swiglu"})
+
+    def test_checkpoint_must_contain_traj_mixer_parameters(self) -> None:
+        block = GPTBlock(n_embd=120, n_head=10)
+        state_dict = {
+            f"blocks.0.{key}": value
+            for key, value in block.state_dict().items()
+        }
+        validate_traj_mixer_state_dict(state_dict)
+
+        state_dict.pop("blocks.0.mlp.group_align")
+        with self.assertRaisesRegex(ValueError, "not a TrajMixer checkpoint"):
+            validate_traj_mixer_state_dict(state_dict)

    def test_invalid_group_partition_is_rejected(self) -> None:
        with self.assertRaisesRegex(ValueError, "divisible"):
            TrajMixer(n_embd=121, n_head=10)

+    def test_parameter_counts_match_traj_mixer_parameters(self) -> None:
+        mixer = TrajMixer(n_embd=120, n_head=10)
+        self.assertEqual(
+            get_model_parameter_counts(mixer),
+            {
+                "model_parameter_count": 15_840,
+                "trainable_parameter_count": 15_840,
+            },
+        )
+

 if __name__ == "__main__":
    unittest.main()
--- a/train_all_future.py
+++ b/train_all_future.py
@@ -27,11 +27,7 @@ from tqdm.auto import tqdm

 from dataset import AllFutureHealthDataset, all_future_collate_fn
 from losses import build_loss
-from model_architectures import (
-    DEFAULT_MODEL_ARCHITECTURE,
-    SUPPORTED_MODEL_ARCHITECTURES,
-)
-from models import DeepHealth
+from models import TRAJ_MIXER_ARCHITECTURE, DeepHealth
 from targets import CHECKUP_IDX, PAD_IDX
 from train_util import (
    configure_torch_for_training,
@@ -69,7 +65,6 @@ def parse_args() -> argparse.Namespace:

    parser.add_argument("--data_prefix", type=str, default="ukb")
    parser.add_argument("--labels_file", type=str, default="labels.csv")
-    parser.add_argument("--runs_root", type=str, default="runs")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--extra_info_types_file", type=str, default=None)

@@ -85,7 +80,8 @@ def parse_args() -> argparse.Namespace:

    parser.add_argument("--n_embd", type=int, default=120)
    parser.add_argument("--n_head", type=int, default=10)
-    parser.add_argument("--n_layer", type=int, default=12)
+    parser.add_argument("--n_hist_layer", type=int, default=12)
+    parser.add_argument("--n_tab_layer", type=int, default=4)
    parser.add_argument("--n_bins", type=int, default=16)
    parser.add_argument("--extra_pool_reduce", type=str, default="mean",
                        choices=["mean", "sum"])
@@ -94,12 +90,6 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument("--dist_mode", type=str, default="exponential",
                        choices=["exponential", "weibull", "mixed"])
    parser.add_argument("--dropout", type=float, default=0.0)
-    parser.add_argument(
-        "--model_architecture",
-        type=str,
-        default=DEFAULT_MODEL_ARCHITECTURE,
-        choices=SUPPORTED_MODEL_ARCHITECTURES,
-    )

    parser.add_argument("--batch_size", type=int, default=128)
    parser.add_argument("--base_lr", type=float, default=3e-4)
@@ -158,7 +148,8 @@ def build_model(args: argparse.Namespace, dataset: AllFutureHealthDataset) -> De
        vocab_size=dataset.vocab_size,
        n_embd=args.n_embd,
        n_head=args.n_head,
-        n_layer=args.n_layer,
+        n_hist_layer=args.n_hist_layer,
+        n_tab_layer=args.n_tab_layer,
        n_types=dataset.n_types,
        n_cont_types=dataset.n_cont_types,
        n_categories=dataset.n_categories,
@@ -169,7 +160,6 @@ def build_model(args: argparse.Namespace, dataset: AllFutureHealthDataset) -> De
        time_mode=args.time_mode,
        dist_mode=args.dist_mode,
        dropout=args.dropout,
-        model_architecture=args.model_architecture,
    )


@@ -309,7 +299,7 @@ def build_metadata(
        "dataset_class": "AllFutureHealthDataset",
        "collate_fn": "all_future_collate_fn",
        "model_class": "DeepHealth",
-        "model_architecture": args.model_architecture,
+        "model_architecture": TRAJ_MIXER_ARCHITECTURE,
        "model_target_mode": "all_future",
        "target_mode": "all_future",
        "dist_mode": args.dist_mode,
@@ -347,14 +337,12 @@ def main() -> None:
    configure_torch_for_training(device)

    run_dir, run_name = create_unique_run_dir(
-        lambda timestamp: f"{args.time_mode}_{args.dist_mode}_all_future_pure_disease_{timestamp}",
-        runs_root=Path(args.runs_root) / args.model_architecture,
+        lambda timestamp: f"{args.time_mode}_{args.dist_mode}_all_future_pure_disease_{timestamp}"
    )
    logger = setup_logging(run_dir)

    logger.info(f"Starting all-future training run: {run_name}")
    logger.info(f"Device: {device}")
-    logger.info(f"Model architecture: {args.model_architecture}")
    logger.info(f"extra_info_types: {format_extra_info_types(args.extra_info_types)}")

    logger.info("Loading all-future datasets...")
--- a/train_next_step.py
+++ b/train_next_step.py
@@ -24,11 +24,7 @@ from tqdm.auto import tqdm

 from dataset import HealthDataset, collate_fn
 from losses import build_loss
-from model_architectures import (
-    DEFAULT_MODEL_ARCHITECTURE,
-    SUPPORTED_MODEL_ARCHITECTURES,
-)
-from models import DeepHealth, DeepHealthOutput
+from models import TRAJ_MIXER_ARCHITECTURE, DeepHealth, DeepHealthOutput
 from readouts import build_readout
 from targets import CHECKUP_IDX, NO_EVENT_IDX, PAD_IDX
 from train_util import (
@@ -66,7 +62,6 @@ def parse_args() -> argparse.Namespace:

    parser.add_argument("--data_prefix", type=str, default="ukb")
    parser.add_argument("--labels_file", type=str, default="labels.csv")
-    parser.add_argument("--runs_root", type=str, default="runs")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--extra_info_types_file", type=str, default=None)
    parser.add_argument("--no_event_interval_years", type=float, default=5.0)
@@ -81,19 +76,14 @@ def parse_args() -> argparse.Namespace:

    parser.add_argument("--n_embd", type=int, default=120)
    parser.add_argument("--n_head", type=int, default=10)
-    parser.add_argument("--n_layer", type=int, default=12)
+    parser.add_argument("--n_hist_layer", type=int, default=12)
+    parser.add_argument("--n_tab_layer", type=int, default=4)
    parser.add_argument("--n_bins", type=int, default=16)
    parser.add_argument("--extra_pool_reduce", type=str, default="mean",
                        choices=["mean", "sum"])
    parser.add_argument("--time_mode", type=str, default="relative",
                        choices=["relative", "absolute"])
    parser.add_argument("--dropout", type=float, default=0.0)
-    parser.add_argument(
-        "--model_architecture",
-        type=str,
-        default=DEFAULT_MODEL_ARCHITECTURE,
-        choices=SUPPORTED_MODEL_ARCHITECTURES,
-    )

    parser.add_argument("--target_mode", type=str, default="uts",
                        choices=["delphi2m", "uts"])
@@ -163,7 +153,8 @@ def build_model(args: argparse.Namespace, dataset: HealthDataset) -> DeepHealth:
        vocab_size=dataset.vocab_size,
        n_embd=args.n_embd,
        n_head=args.n_head,
-        n_layer=args.n_layer,
+        n_hist_layer=args.n_hist_layer,
+        n_tab_layer=args.n_tab_layer,
        n_types=dataset.n_types,
        n_cont_types=dataset.n_cont_types,
        n_categories=dataset.n_categories,
@@ -174,7 +165,6 @@ def build_model(args: argparse.Namespace, dataset: HealthDataset) -> DeepHealth:
        time_mode=args.time_mode,
        dist_mode="exponential",
        dropout=args.dropout,
-        model_architecture=args.model_architecture,
    )


@@ -495,7 +485,7 @@ def build_metadata(
        "dataset_class": "NextStepHealthDataset",
        "collate_fn": "next_step_collate_fn",
        "model_class": "DeepHealth",
-        "model_architecture": args.model_architecture,
+        "model_architecture": TRAJ_MIXER_ARCHITECTURE,
        "model_target_mode": "next_token",
        "target_mode": args.target_mode,
        "dist_mode": "exponential",
@@ -533,14 +523,12 @@ def main() -> None:
        lambda timestamp: (
            f"{args.time_mode}_exponential_next_token_{args.target_mode}_"
            f"gap_{args.no_event_interval_years:g}y_{timestamp}"
-        ),
-        runs_root=Path(args.runs_root) / args.model_architecture,
+        )
    )
    logger = setup_logging(run_dir)

    logger.info(f"Starting next-step training run: {run_name}")
    logger.info(f"Device: {device}")
-    logger.info(f"Model architecture: {args.model_architecture}")
    logger.info(f"extra_info_types: {format_extra_info_types(args.extra_info_types)}")
    logger.info(f"readout={args.readout_name}, target_mode={args.target_mode}")

--- a/train_util.py
+++ b/train_util.py
@@ -301,7 +301,7 @@ def build_optimizer(args: Any, model: DeepHealth) -> AdamW:


 def get_model_parameter_counts(model: torch.nn.Module) -> Dict[str, int]:
-    """Return stable total and trainable parameter counts."""
+    """Return stable parameter-count fields for logs and train_config.json."""
    return {
        "model_parameter_count": sum(
            parameter.numel() for parameter in model.parameters()
Author	SHA1	Message	Date
Jiarui Li	85352dae0f	Revert "Implement shared event-trajectory reasoning backbone" This reverts commit `06f29c0f0a`.	2026-07-23 16:05:00 +08:00
Jiarui Li	22faee7c51	Revert "Add nano and tiny model presets" This reverts commit `6f7b5be405`.	2026-07-23 16:03:29 +08:00
Jiarui Li	6f7b5be405	Add nano and tiny model presets	2026-07-23 15:01:33 +08:00
Jiarui Li	06f29c0f0a	Implement shared event-trajectory reasoning backbone	2026-07-23 14:23:34 +08:00
Jiarui Li	68a6a3df88	Scale TrajMixer hidden width with head count	2026-07-22 16:14:52 +08:00
Jiarui Li	978c88a4ed	Log and save model parameter counts	2026-07-22 14:44:31 +08:00
Jiarui Li	db0947ce9d	Implement TrajMixer block	2026-07-22 11:52:44 +08:00