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Evict unnecessary KV cache entries using trainable lookahead tokens and LoRA modules that predict token importance without drafting responses. Achieve 14.5× speedup over draft-based methods with 2% eviction overhead.
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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
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| name | lookaheadkv-predictive-cache-eviction |
| title | LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2603.10899 |
| keywords | ["KV Cache","Eviction","Long Context","Inference Optimization","Attention"] |
| description | Evict unnecessary KV cache entries using trainable lookahead tokens and LoRA modules that predict token importance without drafting responses. Achieve 14.5× speedup over draft-based methods with 2% eviction overhead. |
Long-context inference requires managing KV cache efficiently. Standard approaches either cache everything (wasteful) or draft responses to predict importance (expensive). LookaheadKV predicts token importance using lightweight learnable modules: lookahead soft tokens with selective LoRA adapters that learn to forecast attention patterns without generating responses.
This sidesteps the accuracy-latency tradeoff of existing methods through cheap, predictive importance scoring.
LookaheadKV operates through three mechanisms:
This achieves importance prediction comparable to actual generation at negligible overhead (<2%).
Create trainable components for predictive importance estimation.
import torch
import torch.nn as nn
class LookaheadTokens(nn.Module):
def __init__(self, num_lookahead=8, hidden_dim=4096):
super().__init__()
self.num_lookahead = num_lookahead
self.hidden_dim = hidden_dim
# Trainable lookahead embeddings
self.lookahead_embeddings = nn.Parameter(
torch.randn(num_lookahead, hidden_dim)
)
():
batch_size = input_sequence.shape[]
lookahead = .lookahead_embeddings.unsqueeze().expand(
batch_size, -, -
)
augmented = torch.cat([input_sequence, lookahead], dim=)
augmented
Selective low-rank modules that activate only for lookahead tokens.
class LookaheadLoRA(nn.Module):
def __init__(self, hidden_dim=4096, lora_rank=16, num_lookahead=8):
super().__init__()
self.hidden_dim = hidden_dim
self.lora_rank = lora_rank
self.num_lookahead = num_lookahead
# LoRA projections
self.lora_down = nn.Linear(hidden_dim, lora_rank)
self.lora_up = nn.Linear(lora_rank, hidden_dim)
# Gate for selective activation (lookahead tokens only)
self.selection_gate = nn.Parameter(
torch.zeros(1, 1, 1) # Will be filled during forward
)
def forward(self, hidden_states, lookahead_mask):
"""
Apply LoRA modifications selectively to lookahead tokens.
hidden_states: (batch, seq_len + num_lookahead, hidden_dim)
lookahead_mask: (batch, seq_len + num_lookahead) boolean mask
"""
# Project down
projected = self.lora_down(hidden_states) # (batch, seq, lora_rank)
# Project up
adapted = self.lora_up(projected) # (batch, seq, hidden_dim)
# Apply gate: only modify lookahead positions
gate = lookahead_mask.unsqueeze(-1).float() # (batch, seq, 1)
modified = hidden_states + gate * adapted * 0.01 # Scale down LoRA contribution
return modified
Learn to predict token importance from lookahead outputs.
class ImportancePredictorTraining:
def __init__(self, model, lookahead_tokens, lora_adapters):
self.model = model
self.lookahead_tokens = lookahead_tokens
self.lora_adapters = lora_adapters
def train_step(self, input_ids, target_length=32000):
"""
Train lookahead components to predict importance.
Importance ground truth: attention head sums across generated response.
"""
# Forward pass with lookahead
augmented_input = self.lookahead_tokens(self.model.embed(input_ids))
# Apply LoRA
lookahead_mask = self._create_lookahead_mask(augmented_input)
adapted = self.lora_adapters(augmented_input, lookahead_mask)
# Run through model layers
hidden_states = adapted
for layer in self.model.layers:
hidden_states = layer(hidden_states)
# Extract lookahead token outputs
lookahead_outputs = hidden_states[:, -self.lookahead_tokens.num_lookahead:, :]
# Predict importance scores
importance_predictions = self._predict_importance(lookahead_outputs)
# Generate full response to get ground-truth importance
with torch.no_grad():
full_generation = self.model.generate(
input_ids,
max_new_tokens=100
)
# Compute ground-truth importance
ground_truth_importance = self._compute_ground_truth_importance(
full_generation,
target_length
)
# KL divergence loss
loss = torch.nn.functional.kl_div(
torch.log_softmax(importance_predictions, dim=-1),
torch.softmax(ground_truth_importance, dim=-1),
reduction='batchmean'
)
return loss
def _create_lookahead_mask(self, hidden_states):
"""Mark which positions are lookahead tokens."""
batch_size, seq_len = hidden_states.shape[:2]
num_lookahead = self.lookahead_tokens.num_lookahead
mask = torch.zeros(batch_size, seq_len, dtype=torch.bool)
mask[:, -num_lookahead:] = True
return mask
def _predict_importance(self, lookahead_outputs):
"""Predict token importance from lookahead."""
# MLP head: (num_lookahead, hidden_dim) -> (seq_len, 1)
predictor = nn.Sequential(
nn.Linear(lookahead_outputs.shape[-1], 256),
nn.ReLU(),
nn.Linear(256, lookahead_outputs.shape[1]) # Predict importance for all tokens
)
predictions = predictor(lookahead_outputs)
return predictions
def _compute_ground_truth_importance(self, generation, target_length):
"""Compute importance from actual attention patterns."""
# Sum attention heads across generated response
importance = torch.zeros(target_length)
# (Simplified; real implementation aggregates attention weights)
return importance
Use predicted importance scores to evict low-importance tokens.
class KVCacheWithImportanceEviction:
def __init__(self, model, max_cache_size=32000):
self.model = model
self.max_cache_size = max_cache_size
self.cache = {}
def forward_with_eviction(
self,
input_ids,
importance_predictor,
eviction_ratio=0.5
):
"""
Forward pass with KV cache eviction based on predicted importance.
"""
batch_size, seq_len = input_ids.shape
# Get predicted importance scores
importance_scores = importance_predictor(input_ids)
# Determine which tokens to keep
num_to_keep = int(seq_len * (1 - eviction_ratio))
_, keep_indices = torch.topk(importance_scores, num_to_keep, dim=1)
# Filter inputs and cache
filtered_input = input_ids[:, keep_indices]
# Forward pass on filtered tokens
output = self.model(filtered_input)
return output
def profile_eviction_overhead(self, batch_size=1, seq_len=32000):
"""Measure eviction pipeline overhead."""
import time
# Lookahead token processing
start = time.time()
lookahead_time = time.time() - start
# LoRA adaptation
start = time.time()
lora_time = time.time() - start
# Importance prediction
start = time.time()
pred_time = time.time() - start
total_overhead = (lookahead_time + lora_time + pred_time) / (
batch_size * seq_len
)
print(f"Eviction overhead: {total_overhead * 100:.2f}% per token")
return total_overhead
End-to-end pipeline for efficient long-context generation.
def generate_with_lookaheadkv(
model,
input_ids,
max_new_tokens=100,
max_cache_size=32000,
eviction_ratio=0.5,
importance_predictor=None
):
"""
Generate with efficient KV cache management.
"""
cache_manager = KVCacheWithImportanceEviction(model, max_cache_size)
generated_tokens = []
current_input = input_ids
for step in range(max_new_tokens):
# Check if cache exceeds limit
if current_input.shape[1] > max_cache_size:
# Evict low-importance tokens
if importance_predictor:
current_input = cache_manager.forward_with_eviction(
current_input,
importance_predictor,
eviction_ratio=eviction_ratio
)
# Generate next token
output = model(current_input)
next_token = torch.argmax(output[:, -1, :], dim=-1, keepdim=True)
generated_tokens.append(next_token)
current_input = torch.cat([current_input, next_token], dim=1)
# Decode generated tokens
generated_ids = torch.cat(generated_tokens, dim=1)
generated_text = model.tokenizer.decode(generated_ids[0])
return generated_text
When to Use:
When NOT to Use:
Hyperparameter Tuning:
Common Pitfalls: