ml: 全流程代码与运行手册（数据打包/可微运动学与对拍/MeanFlow+WTA 模型/训练/闭环仿真/APG/挖错循环/ONNX 导出）；webapp 策略运行时 ik-policy.js + ?policy=1 接入；tests/policy_bench.cjs；ik-solver 增 pairList/clusterExport/criticalPairs/pairGap/leverTable

ml/model/meanflow.py

@@ -0,0 +1,34 @@
+"""MeanFlow 损失（Geng et al. 2025；MP1 配方：25% 样本 r≠t，自适应损失加权）。
+约定：t=0 为数据、t=1 为噪声；z_t = (1−t)·x + t·ε，瞬时速度 v = ε − x；网络输出平均速度 u(z,r,t)。
+目标 u_tgt = v − (t − r)·du/dt，du/dt = v·∂_z u + ∂_t u 用 torch.func.jvp 一次前向得到（切向量 (v, 0, 1)）。"""
+import torch
+from torch.func import jvp
+
+
+def sample_rt(B, device, dtype, p_neq=0.25, mu=-0.4, sigma=1.0):
+    t = torch.sigmoid(torch.randn(B, device=device, dtype=dtype) * sigma + mu)
+    r = torch.sigmoid(torch.randn(B, device=device, dtype=dtype) * sigma + mu)
+    t, r = torch.maximum(t, r), torch.minimum(t, r)
+    eq = torch.rand(B, device=device) >= p_neq
+    r = torch.where(eq, t, r)
+    return r, t
+
+
+def meanflow_loss(model, mem, x, p_neq=0.25, ada_p=1.0, ada_c=1e-3):
+    B = x.shape[0]
+    r, t = sample_rt(B, x.device, x.dtype, p_neq)
+    e = torch.randn_like(x)
+    z = (1 - t)[:, None, None] * x + t[:, None, None] * e
+    v = e - x
+    fn = lambda z_, r_, t_: model.decode(mem, z_, r_, t_)
+    u, dudt = jvp(fn, (z, r, t), (v, torch.zeros_like(r), torch.ones_like(t)))
+    u_tgt = (v - (t - r)[:, None, None] * dudt).detach()
+    err = (u - u_tgt).pow(2).mean(dim=(1, 2))
+    w = 1.0 / (err.detach() + ada_c).pow(ada_p)
+    return (w * err).mean(), err.mean().detach()
+
+
+def one_step(model, mem, e):
+    B = e.shape[0]
+    one = torch.ones(B, device=e.device, dtype=e.dtype)
+    return e - model.decode(mem, e, torch.zeros_like(one), one)