Adaptive Neural Infrastructure
with Memory Accumulation

The Fix — Two Orthogonal Levers

Public systems demonstrate several combinations. Architecture and memory claims should follow dated documentation; do not infer undisclosed proprietary model internals.

Sample Code

import torch
import torch.nn as nn
import torch.nn.functional as F


class Expert(nn.Module):
    def __init__(self, d):
        super().__init__()
        self.fc1 = nn.Linear(d, 4*d)
        self.fc2 = nn.Linear(4*d, d)

    def forward(self, x):
        return self.fc2(F.gelu(self.fc1(x)))


class MoEFFN(nn.Module):
    def __init__(self, d, n_experts=8, top_k=2):
        super().__init__()
        self.top_k   = top_k
        self.router  = nn.Linear(d, n_experts, bias=False)
        self.experts = nn.ModuleList([
            Expert(d) for _ in range(n_experts)
        ])

    def forward(self, x):                 # x: (B, T, d)
        scores = self.router(x)            # (B, T, N)

        # Pick top-k experts per token
        weights, idx = scores.topk(self.top_k, dim=-1)
        weights = F.softmax(weights, dim=-1)

        # Sparse dispatch: send each token only
        # to its chosen experts
        out = torch.zeros_like(x)
        for e in range(len(self.experts)):
            for k in range(self.top_k):
                mask = (idx[..., k] == e)
                if mask.any():
                    out[mask] += (
                        weights[..., k:k+1][mask]
                        * self.experts[e](x[mask])
                    )
        return out


# Drop-in replacement for the FFN sublayer
# in a standard transformer block:
#   self.ffn = MoEFFN(d=128, n_experts=8, top_k=2)

Real production MoE adds load-balancing loss (so all experts get used) and capacity factor (token dropping when an expert overflows). Omitted for clarity.

Keeping Experts Busy — Load-Balancing Loss

Left alone, the router tends to collapse: a few experts get every token, the rest never train. Switch Transformer adds:

L_aux = α · N · Σᵢ fᵢ · Pᵢ          (α ≈ 0.01)

• fᵢ = share of tokens sent to expert i
• Pᵢ = average router probability for expert i

probs = F.softmax(scores, dim=-1)                 # (B, T, N)
f = F.one_hot(idx[..., 0], N).float().mean((0, 1))  # token share
P = probs.mean((0, 1))                            # mean prob
loss = lm_loss + alpha * N * (f * P).sum()

Sample Code — A Minimal SSM Layer

class MinimalSSM(nn.Module):
    """Linear recurrent layer (simplified Mamba-style)."""

    def __init__(self, d_model, d_state=16):
        super().__init__()
        self.d_state = d_state

        # Learnable state-transition matrices
        self.A_log = nn.Parameter(torch.randn(d_state))
        self.B = nn.Linear(d_model, d_state, bias=False)
        self.C = nn.Linear(d_state, d_model, bias=False)

    def forward(self, x):                 # x: (B, T, d_model)
        B, T, _ = x.shape
        A = -torch.exp(self.A_log)        # stable, negative

        h = torch.zeros(B, self.d_state, device=x.device)
        outputs = []

        for t in range(T):
            # Recurrence — same shape every step
            h = torch.exp(A) * h + self.B(x[:, t])
            y = self.C(h)
            outputs.append(y)

        return torch.stack(outputs, dim=1)


# Drop-in replacement for self-attention.
# Notice: no QK^T matrix → no O(N²) cost.
# 'h' carries all information about the past in d_state floats.

Real Mamba: parallel scan for fast training, hardware-aware kernel, selective parameters that gate input flow. This minimal version is the conceptual core.

The Knob: How Long Is the Memory?

Half-life in steps = ln 0.5 / ln a.

• a = 0.5 → 1 step.  a = 0.99 → ≈ 69 steps.
• Real SSMs run thousands of these in parallel, each with its own a — some forget fast, some remember long.
• Mamba makes the decay depend on the input, so it can choose to hold on to an important token.

Sample Code — Memory Bank with Gated Write

class MemoryBank(nn.Module):
    """Persistent KV bank — read by attention, write by gate."""

    def __init__(self, d_model, capacity=256):
        super().__init__()
        self.capacity = capacity
        self.d_model  = d_model

        # Slots stored as buffers (saved with state_dict)
        self.register_buffer("keys", torch.zeros(capacity, d_model))
        self.register_buffer("vals", torch.zeros(capacity, d_model))
        self.register_buffer("size", torch.tensor(0))

        self.write_gate = nn.Linear(d_model, 1)

    def read(self, q):                          # q: (B, T, d)
        if self.size == 0:
            return torch.zeros_like(q)
        K = self.keys[:self.size]                # (M, d)
        V = self.vals[:self.size]
        scores = q @ K.T / (self.d_model ** 0.5)   # (B, T, M)
        return F.softmax(scores, dim=-1) @ V

    def write(self, x):                         # x: (N, d) flattened
        importance = torch.sigmoid(self.write_gate(x))
        keep = (importance > 0.5).squeeze(-1)
        new = x[keep]
        n = min(new.size(0), self.capacity - self.size)
        if n == 0: return
        self.keys[self.size:self.size+n] = new[:n]
        self.vals[self.size:self.size+n] = new[:n]
        self.size += n

    def save(self, path):
        torch.save({"keys": self.keys, "vals": self.vals,
                    "size": self.size}, path)

The bank can be saved/loaded across sessions — that's what makes it persistent. Real systems add eviction policies, sharding, learned compression.

The AnimaBlock

class AnimaBlock(nn.Module):
    """
    Transformer block with:
      - adaptive compute (MoE FFN)
      - accumulating memory (external bank)
    """

    def __init__(self, d_model, n_heads=4,
                 n_experts=8, top_k=2,
                 mem_capacity=256):
        super().__init__()
        self.ln1    = nn.LayerNorm(d_model)
        self.attn   = nn.MultiheadAttention(
                          d_model, n_heads, batch_first=True)
        self.ln2    = nn.LayerNorm(d_model)
        self.ffn    = MoEFFN(d_model, n_experts, top_k)
        self.memory = MemoryBank(d_model, mem_capacity)

    def forward(self, x):
        # 1. Recall: pull relevant past memory
        recall = self.memory.read(x)
        h = self.ln1(x + recall)

        # 2. Causal attention sublayer (True = blocked)
        T = x.size(1)
        causal = torch.triu(torch.ones(T, T, dtype=torch.bool,
                            device=x.device), diagonal=1)
        attn_out, _ = self.attn(h, h, h, attn_mask=causal,
                                need_weights=False)
        x = x + attn_out

        # 3. Adaptive FFN sublayer
        x = x + self.ffn(self.ln2(x))

        # 4. Persist what mattered
        with torch.no_grad():
            self.memory.write(x.reshape(-1, x.size(-1)))

        return x


# Stack them like normal transformer blocks:
blocks = nn.ModuleList([
    AnimaBlock(d_model=128) for _ in range(6)
])

# Forward pass:
for block in blocks:
    x = block(x)

Writes run under no_grad, so saved slots are detached — no gradients flow through memory across batches. Without the causal mask a decoder would peek at future tokens during training.

Why Bother

• Vanilla transformers spend equal compute on every token and forget everything between sessions.
• Adaptive infrastructure routes compute where it's needed — sparse activation gives you a big model at a small model's cost.
• Memory accumulation persists state across time — shorter latency, longer effective context, true cross-session memory.
• The two are orthogonal; modern systems combine them.