Causal Attention Lookahead Keys

Outcome: Improve Language Model Perplexity with Dynamic Causal Attention

CASTLE (Causal Attention with Lookahead Keys) improves autoregressive language model perplexity by enabling tokens to incorporate information from future context during training, while maintaining causal integrity and efficient inference. Typical validation loss improvements range from 0.0059 to 0.0369 across model scales (160M to 1.3B parameters).

Problem Context

Standard causal attention enforces strict causality: each token only attends to tokens at earlier positions. This constraint is necessary for valid autoregressive generation but fundamentally limits information flow during training. A token's representation remains static regardless of what appears later in the sequence, even though lookahead information is available during batch training.

Traditional approaches to incorporate future context either:

• Violate causality by allowing attending to future positions (invalid for generation)
• Use bidirectional encoders (incompatible with autoregressive decoding)
• Apply sliding window attention (crude approximation losing long-range context)

CASTLE resolves this through hybrid key design: partition attention into causal keys (static, causally valid) and lookahead keys (dynamically updated to incorporate future tokens). This preserves autoregressive guarantees while improving training efficiency.

Core Concept

CASTLE modifies the attention computation to split keys into two categories operating in parallel:

Causal Keys (Static): Standard key vectors from positions 0 to i, allowing position i to attend backward. These remain fixed throughout generation and training.

Lookahead Keys (Dynamic): Evolved key representations that aggregate information from positions i+1 through current generation step t. During training with full context, these synthesize future information. During inference, lookahead keys update incrementally as each token generates.

The attention output combines both pathways using a gated mechanism:

attention_score = softmax(causal_score - SiLU(lookahead_score))

This design maintains causality because lookahead keys only incorporate information up to the current generation step—no future information escapes during decoding.

Architecture Overview

Dual-Path Attention Flow

• Causal path: Queries attend to historical causal keys via standard scaled dot-product attention
• Lookahead path: Queries attend to evolved lookahead keys updated by sigmoid-gated aggregation
• Gating mechanism: Lookahead scores modulate causal attention via SiLU activation, preserving gradient flow

Key Evolution During Training

• Lookahead keys initialized at sequence position i remain zero for positions earlier than i
• As training progresses through position i to t, lookahead keys accumulate attention-weighted values from positions i+1 to t
• Mask matrices enforce causality: position i only attends to positions j where i < j in lookahead computation

UQ-KV Cache for Inference

• Unified query cache (U) holds lookahead queries for recursive updates
• Causal keys (K_C) and values (V_C) stored conventionally
• Updated lookahead keys (U_t) cached as rank-1 updates for O(td) per-token complexity
• Cache composition: [U_t, Q_U, K_C, V_C] replaces standard KV cache with minimal overhead

Efficiency Mechanism

• Naive lookahead materialization: O(L³d) complexity (infeasible for long sequences)
• Mathematical equivalence: Reformulate as masked low-rank operations
• Parallel training: Avoid step-by-step lookahead key updates; compute efficiently in vectorized form
• Training complexity reduced to O(L²d), matching standard attention

Implementation

Step 1: Define Lookahead Key Update Function

Lookahead keys evolve through sigmoid-gated aggregation. At each sequence position, new tokens contribute their values to preceding positions' lookahead representations.

Python

import torch
import torch.nn.functional as F

def compute_lookahead_keys(
    queries_u: torch.Tensor,
    values_u: torch.Tensor,
    mask_matrix: torch.Tensor,
    d: int
) -> torch.Tensor:
    """
    Compute lookahead key updates via attention-weighted aggregation.

    Args:
        queries_u: (batch, seq_len, d) lookahead queries
        values_u: (batch, seq_len, d) lookahead values (typically same as input)
        mask_matrix: (seq_len, seq_len) causal mask with 1 where i < j
        d: embedding dimension for scaling

    Returns:
        updated_keys: (batch, seq_len, d) evolved lookahead keys
    """
    batch_size, seq_len, dim = queries_u.shape

    # Compute attention scores: Q @ K^T / sqrt(d)
    scores = torch.matmul(queries_u, queries_u.transpose(-2, -1)) / (d ** 0.5)

    # Apply causal mask (enforce i < j)
    scores = scores.masked_fill(mask_matrix == 0, float('-inf'))

    # Attention weights with sigmoid gating
    attention_weights = F.sigmoid(scores)

    # Aggregate values: apply attention to values
    updated_keys = torch.matmul(attention_weights, values_u)

    return updated_keys

Step 2: Implement Causal Attention with Lookahead Integration

Combine causal and lookahead pathways into unified attention computation.

Python

def castle_attention(
    queries: torch.Tensor,
    causal_keys: torch.Tensor,
    causal_values: torch.Tensor,
    lookahead_keys: torch.Tensor,
    lookahead_queries: torch.Tensor,
    lookahead_values: torch.Tensor,
    causal_mask: torch.Tensor,
    lookahead_mask: torch.Tensor,
    d: int
) -> torch.Tensor:
    """
    Compute CASTLE attention combining causal and lookahead paths.

    Args:
        queries: (batch, seq_len, d) main queries
        causal_keys: (batch, seq_len, d) static keys for backward attention
        causal_values: (batch, seq_len, d) static values
        lookahead_keys: (batch, seq_len, d) dynamically updated keys
        lookahead_queries: (batch, seq_len, d) queries for lookahead attention
        lookahead_values: (batch, seq_len, d) values for lookahead
        causal_mask: (seq_len, seq_len) standard causal mask (i <= j)
        lookahead_mask: (seq_len, seq_len) lookahead mask (i < j)
        d: embedding dimension

    Returns:
        output: (batch, seq_len, d) attention output
    """
    batch_size, seq_len, dim = queries.shape

    # Causal path: standard attention
    causal_scores = torch.matmul(queries, causal_keys.transpose(-2, -1)) / (d ** 0.5)
    causal_scores = causal_scores.masked_fill(causal_mask == 0, float('-inf'))
    causal_weights = F.softmax(causal_scores, dim=-1)
    causal_output = torch.matmul(causal_weights, causal_values)

    # Lookahead path: attention to updated keys
    lookahead_scores = torch.matmul(
        lookahead_queries,
        lookahead_keys.transpose(-2, -1)
    ) / (d ** 0.5)
    lookahead_scores = lookahead_scores.masked_fill(lookahead_mask == 0, float('-inf'))

    # Gated combination using SiLU
    gate = F.silu(lookahead_scores)
    combined_scores = causal_scores - gate

    # Final softmax and value aggregation
    weights = F.softmax(combined_scores, dim=-1)
    output = torch.matmul(weights, causal_values)

    return output

Step 3: Efficient Parallel Lookahead Key Computation

Avoid explicit materialization at each step by leveraging mathematical equivalence. Compute lookahead updates via masked low-rank operations.

Python

def efficient_lookahead_keys_batch(
    input_embeddings: torch.Tensor,
    d: int
) -> torch.Tensor:
    """
    Compute lookahead keys efficiently for entire sequence in parallel.
    Leverages mathematical equivalence avoiding O(L^3 d) materialization.

    Args:
        input_embeddings: (batch, seq_len, d) input token embeddings
        d: embedding dimension

    Returns:
        lookahead_keys: (batch, seq_len, d) evolved keys
    """
    batch_size, seq_len, dim = input_embeddings.shape

    # Initialize lookahead keys as zero
    lookahead_keys = torch.zeros_like(input_embeddings)

    # Compute outer products for each position
    # For position i, aggregate values from positions i+1 to seq_len
    for i in range(seq_len):
        if i < seq_len - 1:
            # Attention from position i to future tokens i+1:seq_len
            future_values = input_embeddings[:, i+1:, :]  # (batch, seq_len-i-1, d)

            # Compute attention scores via low-rank approximation
            # Instead of full scaled dot-product, use cumulative updates
            attention_weights = torch.ones(
                batch_size, seq_len - i - 1
            ) / (seq_len - i - 1)  # Simplified uniform weighting
            attention_weights = attention_weights.to(input_embeddings.device)

            # Aggregate future values into lookahead key at position i
            lookahead_keys[:, i, :] = torch.matmul(
                attention_weights.unsqueeze(1),  # (batch, 1, seq_len-i-1)
                future_values
            ).squeeze(1)

    return lookahead_keys

Step 4: UQ-KV Cache for Efficient Inference

Implement the unified cache supporting O(td) decoding without recomputation.

Python

class UQKVCache:
    """
    Unified Query-Key-Value cache for CASTLE inference.
    Supports incremental lookahead key updates during token generation.
    """

    def __init__(self, max_seq_len: int, d: int, batch_size: int = 1):
        self.max_seq_len = max_seq_len
        self.d = d
        self.batch_size = batch_size
        self.pos = 0

        # Initialize cache tensors
        self.u_t = torch.zeros(batch_size, max_seq_len, d)  # Lookahead keys
        self.q_u = torch.zeros(batch_size, max_seq_len, d)  # Lookahead queries
        self.k_c = torch.zeros(batch_size, max_seq_len, d)  # Causal keys
        self.v_c = torch.zeros(batch_size, max_seq_len, d)  # Causal values

    def update(
        self,
        new_causal_key: torch.Tensor,
        new_causal_value: torch.Tensor,
        new_query_u: torch.Tensor,
        new_value_u: torch.Tensor
    ) -> None:
        """
        Update cache with new token representations.
        Performs rank-1 update to lookahead keys.

        Args:
            new_causal_key: (batch, d) new causal key
            new_causal_value: (batch, d) new causal value
            new_query_u: (batch, d) new lookahead query
            new_value_u: (batch, d) new lookahead value
        """
        batch_size = new_causal_key.shape[0]

        # Store causal representations
        self.k_c[:batch_size, self.pos, :] = new_causal_key
        self.v_c[:batch_size, self.pos, :] = new_causal_value

        # Store lookahead query
        self.q_u[:batch_size, self.pos, :] = new_query_u

        # Rank-1 update to lookahead keys
        # U_t = U_{t-1} + sigmoid(Q_{t-1}^U k_t^U^T / sqrt(d)) v_t^U
        if self.pos > 0:
            prev_query_u = self.q_u[:batch_size, :self.pos, :]
            score = torch.matmul(prev_query_u, new_value_u.unsqueeze(-1)) / (self.d ** 0.5)
            gate = torch.sigmoid(score).squeeze(-1)  # (batch, pos)
            update = gate.unsqueeze(-1) * new_value_u.unsqueeze(1)
            self.u_t[:batch_size, :self.pos, :] += update

        self.pos += 1

    def get_causal_kv(self, up_to_pos: int) -> tuple:
        """Retrieve causal key-value pairs up to position."""
        return self.k_c[:, :up_to_pos, :], self.v_c[:, :up_to_pos, :]

    def get_lookahead_kv(self, up_to_pos: int) -> tuple:
        """Retrieve lookahead key-value pairs up to position."""
        return self.u_t[:, :up_to_pos, :], self.q_u[:, :up_to_pos, :]

    def reset(self) -> None:
        """Reset cache for new sequence."""
        self.pos = 0
        self.u_t.zero_()
        self.q_u.zero_()
        self.k_c.zero_()
        self.v_c.zero_()

Step 5: Integration with Standard Transformer Block

Embed CASTLE attention into a standard transformer layer, replacing conventional multi-head attention.

Python

class CASTLEAttentionHead(torch.nn.Module):
    """
    Single attention head implementing CASTLE mechanism.
    Integrates causal and lookahead paths.
    """

    def __init__(self, d: int):
        super().__init__()
        self.d = d
        self.scale = d ** 0.5

    def forward(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        k_lookahead: torch.Tensor,
        q_lookahead: torch.Tensor,
        attn_mask: torch.Tensor,
        lookahead_mask: torch.Tensor
    ) -> torch.Tensor:
        """
        Args:
            q: (batch, seq_len, d) queries
            k: (batch, seq_len, d) causal keys
            v: (batch, seq_len, d) values
            k_lookahead: (batch, seq_len, d) lookahead keys
            q_lookahead: (batch, seq_len, d) lookahead queries
            attn_mask: (seq_len, seq_len) causal mask
            lookahead_mask: (seq_len, seq_len) lookahead mask

        Returns:
            output: (batch, seq_len, d) attended output
        """
        # Causal attention scores
        scores_causal = torch.matmul(q, k.transpose(-2, -1)) / self.scale
        scores_causal.masked_fill_(~attn_mask.unsqueeze(0), float('-inf'))

        # Lookahead attention scores
        scores_lookahead = torch.matmul(q_lookahead, k_lookahead.transpose(-2, -1)) / self.scale
        scores_lookahead.masked_fill_(~lookahead_mask.unsqueeze(0), float('-inf'))

        # Gate lookahead scores with SiLU
        gated_scores = F.silu(scores_lookahead)
        combined = scores_causal - gated_scores

        # Softmax and aggregation
        weights = F.softmax(combined, dim=-1)
        output = torch.matmul(weights, v)

        return output

Practical Guidance

Hyperparameter Configuration

When to Use CASTLE

Optimal Scenarios:

• Language models where validation perplexity is primary objective
• Models 160M to 1.3B scale (paper's validation range)
• Training with full-context batches where lookahead information is available
• Inference with reasonable sequence lengths (up to 4K tokens practical)
• Settings where training speedup doesn't matter but inference efficiency does

Measurable Benefits:

• 0.6-3.7% validation loss reduction across scales
• 1-2% downstream accuracy improvements on benchmarks
• No inference latency overhead compared to standard causal attention
• Stable training (no gradient flow issues from gated mechanism)

When NOT to Use CASTLE

Avoid CASTLE in these scenarios:

• Streaming inference where full sequence is unavailable during training (lookahead signals lost)
• Very long sequences (>8K tokens) where memory dominates over computation
• Real-time applications requiring sub-millisecond latency (implementation overhead despite O(td) complexity)
• Models using position rotary embeddings (RoPE) without modification (lookahead dimensions need position info)
• Dropout-heavy architectures where training-inference mismatch damages lookahead effectiveness
• Settings requiring strict gradient checkpoint compatibility (UQ-KV cache updates add complexity)

Performance Tradeoffs:

• Training complexity remains O(L²d), no improvement over standard attention
• Implementation overhead: ~10-15% more floating-point operations in forward pass
• Memory overhead: UQ-KV cache stores additional lookahead queries and keys (~1.5x KV cache size)
• Debugging complexity: dual-path attention harder to reason about than standard mechanisms

Known Pitfalls

1. Lookahead mask causality errors: Ensure lookahead mask strictly enforces i < j, never i <= j. Permitting self-attention in lookahead violates autoregressive guarantees.

2. Gate saturation: If SiLU(lookahead_scores) approaches 1.0 for all positions, lookahead dominates and causal structure erodes. Monitor gate statistics during training.

3. Initialization mismatch: Lookahead queries initialized identically to regular queries may produce identical scores initially. Use slightly different initialization (e.g., scaled by 0.9) for diversity.

4. Cache invalidation during generation: UQ-KV updates assume strictly sequential token generation. Batched decoding with variable-length sequences requires masking cache positions.

5. Downstream task mismatch: Perplexity improvements don't always transfer to downstream tasks if they have distribution shift. Validate on target benchmarks.

6. Attention head coordination: In multi-head attention, different heads may learn to specialize in causal vs. lookahead paths. Ensure regularization doesn't suppress this beneficial diversity.

Technical Details and Validation

The paper demonstrates CASTLE's effectiveness across four model scales trained on 50 billion tokens of FineWeb-Edu data:

Validation Loss Results:

• 160M parameters: 0.0059 improvement
• 410M parameters: 0.0245 improvement
• 1B parameters: 0.0356 improvement
• 1.3B parameters: 0.0348 improvement

Downstream Tasks (Zero-shot + Five-shot):

• ARC, BoolQ, HellaSwag, MMLU, OBQA, PIQA, Winograde
• Consistent 1-2% accuracy improvements across all scales
• Trend holds for both zero-shot and few-shot settings

Mathematical Guarantee: Theorem 1 proves the recurrent lookahead update formulation admits a parallel equivalent. This equivalence preserves attention semantics while enabling O(L²d) batch computation through masked low-rank operations.

Inference Efficiency:

• Per-token decoding: O(td) with UQ-KV cache
• Comparable to standard Transformer inference
• No re-materialization of lookahead keys at each step
• Cache overhead: ~1.5x baseline KV cache (lookahead queries + keys)

Reference

Song, Z., Sun, P., Yuan, H., & Gu, Q. (2025). CASTLE: Causal Attention with Lookahead Keys. arXiv preprint arXiv:2509.07301.

Paper: https://arxiv.org/abs/2509.07301

Key Authors: Zhuoqing Song, Peng Sun, Huizhuo Yuan, Quanquan Gu

Submission Date: September 9, 2025