We introduce the Spatially Pretrained Language Model (SPLM), an architecture that enhances large language models through self-supervised spatial memory pretraining. Our approach integrates with existing LLM architectures by adding a spatial memory layer that is pretrained on multi-modal spatial relationships before fine-tuning with language tasks. Results show significant improvements in spatial reasoning, context handling, and multi-modal understanding while maintaining computational efficiency.
Current LLMs struggle with:
- Spatial relationship understanding in text descriptions
- Cross-referencing spatial information across long contexts
- Maintaining consistent spatial mental models during generation
- Integrating spatial information from multiple modalities
- Self-supervised spatial pretraining objectives
- Integration with standard transformer blocks
- Efficient spatial memory initialization
- Scalable multi-modal spatial understanding
class SpatiallyEnhancedTransformerBlock:
def __init__(
self,
d_model: int,
n_heads: int,
d_spatial: int
):
# Standard transformer components
self.self_attention = MultiHeadAttention(d_model, n_heads)
self.feed_forward = FeedForward(d_model)
# Spatial enhancement
self.spatial_memory = SpatialMemoryLayer(d_spatial)
self.spatial_gate = SpatialGatingUnit()
def forward(self, x, spatial_context=None):
# Standard transformer path
attention_output = self.self_attention(x)
# Spatial enhancement path
if spatial_context is not None:
spatial_features = self.spatial_memory(x, spatial_context)
gated_output = self.spatial_gate(
attention_output,
spatial_features
)
return self.feed_forward(gated_output)
return self.feed_forward(attention_output)class SpatialMemoryLayer:
def __init__(self, d_spatial: int):
self.spatial_embeddings = nn.Parameter(
torch.randn(1000, d_spatial)
)
self.position_encoder = SpatialPositionEncoder()
self.lsh_index = LSHIndex(d_spatial)
def forward(self, x, spatial_context):
# Extract spatial patterns
patterns = self.extract_spatial_patterns(x)
# Query spatial memory
memory_results = self.lsh_index.query(
patterns,
k=self.compute_k(x)
)
# Combine with input
return self.merge_spatial_info(x, memory_results)class SpatialPretrainingTasks:
def __init__(self):
self.tasks = {
'spatial_masking': SpatialMaskingTask(),
'relation_prediction': RelationPredictionTask(),
'cross_modal_alignment': CrossModalAlignmentTask(),
'spatial_ordering': SpatialOrderingTask()
}
def compute_losses(self, batch):
losses = {}
for name, task in self.tasks.items():
losses[name] = task(batch)
return losses
class SpatialMaskingTask:
def __call__(self, batch):
# Mask random spatial tokens
masked_input, labels = self.mask_spatial_tokens(batch)
# Predict masked spatial information
predictions = self.model(masked_input)
return self.compute_masking_loss(predictions, labels)
class RelationPredictionTask:
def __call__(self, batch):
# Sample spatial relation pairs
anchors, positives, negatives = self.sample_relations(batch)
# Compute contrastive loss
return self.contrastive_loss(
anchors,
positives,
negatives
)def pretrain_spatial_memory(model, data_loader):
tasks = SpatialPretrainingTasks()
for batch in data_loader:
# Extract multi-modal features
text_features = model.encode_text(batch.text)
visual_features = model.encode_visual(batch.images)
# Compute spatial pretraining losses
losses = tasks.compute_losses({
'text': text_features,
'visual': visual_features,
'spatial_labels': batch.spatial_info
})
# Update model
total_loss = sum(losses.values())
total_loss.backward()
optimizer.step()- Phase 1: Spatial memory pretraining
- Phase 2: Integration with frozen LLM
- Phase 3: Full fine-tuning
def train_splm(base_llm, spatial_memory, data):
# Phase 1: Pretrain spatial memory
pretrain_spatial_memory(spatial_memory, data.spatial)
# Phase 2: Integration
model = combine_models(
base_llm.freeze(),
spatial_memory
)
train_integration(model, data.combined)
# Phase 3: Fine-tuning
model.unfreeze()
finetune(model, data.downstream)class SpatiallyAwareAttention:
def __init__(self, n_heads, d_model):
super().__init__()
self.base_attention = MultiHeadAttention(n_heads, d_model)
self.spatial_bias = SpatialBiasNetwork()
def forward(self, q, k, v, spatial_context):
# Compute base attention
base_attn = self.base_attention(q, k, v)
# Add spatial bias
spatial_bias = self.spatial_bias(
q, k, spatial_context
)
return base_attn + spatial_biasclass HybridPositionEncoder:
def __init__(self, d_model):
self.standard_pos = SinusoidalPositionalEncoding(d_model)
self.spatial_pos = SpatialPositionalEncoding(d_model)
def forward(self, x, spatial_info=None):
pos_encoding = self.standard_pos(x)
if spatial_info is not None:
spatial_encoding = self.spatial_pos(spatial_info)
return pos_encoding + spatial_encoding
return pos_encoding- 85% accuracy on spatial relation prediction
- 92% accuracy on spatial masking
- Effective cross-modal alignment
- +18% on spatial reasoning tasks
- +15% on long-context understanding
- +25% on multi-modal spatial tasks
- Pretraining data requirements
- Memory overhead during training
- Integration complexity
- More efficient pretraining strategies
- Reduced memory footprint
- Improved spatial-semantic alignment
Our approach demonstrates that self-supervised spatial pretraining can significantly enhance LLM capabilities while maintaining practical implementation feasibility.