Framework enabling LLMs to actively manage internal working memory and context through tools for fragmentation, summarization, and semantic search. Mitigates proactive interference and improves reasoning at extended context lengths.
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Framework enabling LLMs to actively manage internal working memory and context through tools for fragmentation, summarization, and semantic search. Mitigates proactive interference and improves reasoning at extended context lengths.
Sculptor: Active Context Management for LLMs
Core Concept
Sculptor empowers large language models to self-manage their attention and working memory through active context control. Rather than passively processing entire contexts, models gain explicit tools to focus on relevant information, hide distractions, and reconstruct hidden content as needed. This mimics human cognitive strategies of selective attention and information filtering.
Architecture Overview
Context Fragmentation Tools: Break extended contexts into manageable chunks that can be independently accessed and processed
Summary/Hide/Restore Operations: Allow models to compress irrelevant information while preserving critical context for later retrieval
Semantic Search Capabilities: Enable precise extraction of relevant passages from fragmented context without linear scanning
Proactive Interference Mitigation: Reduce performance degradation from irrelevant early-context information through explicit hiding mechanisms
Adaptive Focus: Models learn to prioritize reasoning on pertinent information rather than all available context
Implementation Steps
Step 1: Instrument Context with Fragmentation Markers
Augment input contexts with structural markers that enable efficient partitioning and selective access.
deffragment_context(text, chunk_size=500, overlap=50):
"""
Fragment a long context into overlapping chunks with unique identifiers.
Args:
text: Input context to fragment
chunk_size: Target characters per fragment
overlap: Characters to overlap between consecutive fragments
Returns:
List of (fragment_id, content) tuples with metadata
"""
fragments = []
start = 0while start < len(text):
end = min(start + chunk_size, len(text))
fragment_id = f"FRAG_{len(fragments):04d}"
fragments.append({
"id": fragment_id,
"content": text[start:end],
"start_idx": start,
: end
})
start = end - overlap
fragments
():
annotated =
frag fragments:
annotated +=
annotated + + context
"end_idx"
return
def
annotate_context_with_fragments
context, fragments
"""
Create a context reference system with fragment identifiers.
Returns:
Annotated context with access hints
"""
This enables the model to reference specific fragments rather than re-scanning the entire context.
Step 2: Implement Summary and Hide/Restore Tools
Create tools that allow models to compress and selectively suppress context elements.
classContextManager:
"""
Manages context state including summary generation and hiding.
"""def__init__(self):
self.hidden_content = {}
self.summaries = {}
defhide_fragment(self, fragment_id, reason="low relevance"):
"""
Hide a fragment from active context.
Args:
fragment_id: ID of fragment to hide
reason: Why this fragment is being hidden
Returns:
Confirmation of hide operation
"""self.hidden_content[fragment_id] = {
"hide_reason": reason,
"hidden_at_step": self.current_step
}
returnf"Fragment {fragment_id} hidden ({reason})"defsummarize_fragment(self, fragment_id, summary_text):
"""
Replace fragment with a concise summary.
Args:
fragment_id: Fragment to summarize
summary_text: Compressed representation of content
Returns:
Summary registration
"""self.summaries[fragment_id] = {
"summary": summary_text,
"original_length": len(self.fragments[fragment_id]),
"compression_ratio": len(summary_text) / len(self.fragments[fragment_id])
}
returnf"Fragment {fragment_id} summarized"defrestore_fragment(self, fragment_id):
"""
Retrieve and restore a previously hidden fragment.
Args:
fragment_id: Fragment to restore
Returns:
Original fragment content
"""if fragment_id inself.hidden_content:
delself.hidden_content[fragment_id]
returnself.fragments[fragment_id]
defget_active_context(self):
"""
Generate context excluding hidden fragments.
Returns:
Current active context for reasoning
"""
active = []
for frag_id, content inself.fragments.items():
if frag_id notinself.hidden_content:
if frag_id inself.summaries:
active.append(self.summaries[frag_id]["summary"])
else:
active.append(content)
return"\n".join(active)
Step 3: Implement Semantic Search Tool
Enable precise retrieval of relevant passages without full-context scanning.
defsemantic_search_fragments(query, fragments, embedding_model):
"""
Search fragments by semantic similarity to a query.
Args:
query: Search query
fragments: List of fragment dictionaries
embedding_model: Pre-trained embedding model
Returns:
Ranked list of relevant fragments
"""
query_embedding = embedding_model.encode(query)
scores = []
for fragment in fragments:
fragment_embedding = embedding_model.encode(fragment["content"])
similarity = cosine_similarity(query_embedding, fragment_embedding)
scores.append((fragment["id"], similarity, fragment))
# Sort by relevance
scores.sort(key=lambda x: x[1], reverse=True)
return [{"id": s[0], "score": s[1], "content": s[2]["content"]}
for s in scores]
Step 4: Integrate into Model Inference
Embed fragment management into the inference loop.
definference_with_active_context(prompt, context, model, context_manager):
"""
Run inference with active context management.
Args:
prompt: User query
context: Full document or context
model: Language model
context_manager: ContextManager instance
Returns:
Model response
"""# Fragment the context
fragments = fragment_context(context)
context_manager.load_fragments(fragments)
# Initial reasoning with full context
response = model.generate(
prompt=prompt,
context=context_manager.get_active_context(),
tools=["hide_fragment", "summarize_fragment", "restore_fragment", "search"]
)
# Model can call tools during generation to manage contextwhile response.uses_tools():
for tool_call in response.tool_calls:
if tool_call.name == "hide_fragment":
context_manager.hide_fragment(tool_call.args["fragment_id"])
elif tool_call.name == "summarize_fragment":
context_manager.summarize_fragment(
tool_call.args["fragment_id"],
tool_call.args["summary"]
)
# Continue generation with updated context
response = model.continue_generation(
context=context_manager.get_active_context()
)
return response.text
Practical Guidance
When to Use Sculptor
Long-context reasoning: Documents >10K tokens where not all information is equally relevant
Noisy contexts: Sources containing irrelevant or distracting information mixed with critical details
Multi-step reasoning: Tasks where intermediate reasoning steps need to suppress and restore information
Heterogeneous documents: Contexts mixing multiple topics that require selective focus
When NOT to Use Sculptor
Short contexts: <2K tokens where fragmentation overhead exceeds benefit
Linear narrative tasks: Reading comprehension where all context is equally important in order
Real-time latency-critical: Tool calling adds overhead unsuitable for high-throughput applications
Adversarial settings: Information hiding could be exploited if prompts can override instructions
Hyperparameter Recommendations
Fragment size: 400-800 characters balances accessibility with structural clarity
Fragment overlap: 50-100 characters captures transition information between chunks
Embedding model: Use dense retrievers (e.g., BAAI/bge-base) for semantic search
Hide threshold: Configure to trigger when model predicts <0.3 relevance score
Key Insights
The framework demonstrates that explicit context management outperforms simple attention masking. By giving models agency over what to focus on, Sculptor exploits the model's internal understanding of task relevance. The proactive interference mitigation is particularly powerful: models actively suppress rather than merely ignore distracting content.
Reference
Sculptor: Empowering LLMs with Cognitive Agency via Active Context (arXiv:2508.04664)
Introduces Active Context Management tools for fragmentation, summarization, hiding, and semantic search. Demonstrates improved reasoning at extended context lengths through explicit control of working memory and attention.
