Layer 3: Context Compression

Layer 3: Semantic Context Differentials (SCD)

Crate: atp-context | Tests: 45

Layer 3 provides 28x context compression by extracting only the semantically relevant portions of context for each task.

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The Problem

When agents collaborate, they pass context between each other. A coding agent might send 50,000 tokens of context to a review agent. But the review agent only needs the relevant parts — perhaps 1,768 tokens. Sending everything wastes bandwidth, costs money, and can overwhelm receiving agents.

The Solution: Minimal Sufficient Context (MSC)

SCD extracts the Minimal Sufficient Context — the smallest subset of context that preserves task-relevant information.

Compression Pipeline

Input Context (50,000B)
       │
       ▼
┌──────────────┐
│ 1. Chunk     │  Split into fixed-size chunks (default 512B)
└──────┬───────┘
       ▼
┌──────────────┐
│ 2. Embed     │  Generate hash-based embeddings (64-dim)
└──────┬───────┘
       ▼
┌──────────────┐
│ 3. Score     │  Cosine similarity against task embedding
└──────┬───────┘
       ▼
┌──────────────┐
│ 4. Extract   │  Keep chunks where score > threshold (0.3)
└──────┬───────┘
       ▼
┌──────────────┐
│ 5. Package   │  Generate wire-format ContextDiff
└──────────────┘
       │
       ▼
Output Context (1,768B)  ← 28x smaller

Core Formula

cos(a, b) = (a · b) / (|a| × |b|)

MSC = {(chunk, score) : cosine(e_task, e_chunk) > threshold}

Where:

• e_task = embedding of the task type
• e_chunk = embedding of each context chunk
• threshold = relevance cutoff (default 0.3)

Configuration

pub struct MscConfig {
    pub relevance_threshold: f64,  // Default 0.3 — chunks below this are dropped
    pub max_chunks: usize,         // Default 10 — maximum chunks to retain
    pub chunk_size: usize,         // Default 512 — bytes per chunk
    pub dimensions: usize,         // Default 64 — embedding dimensions
}

Adaptive Context

When compression confidence is below 0.7, the system automatically:

1. Lowers the relevance threshold
2. Includes more chunks
3. Re-evaluates until confidence is acceptable

This ensures critical information is never dropped.

Usage

// Simple — just prints the result
atp_sdk::compress(b"your context data here...", "coding");
// Output: "28.3x compression (50000B → 1768B, 3 chunks, confidence=0.85)"

// Structured — returns CompressResult
let result = atp_sdk::shrink(b"your context data here...", "coding");
println!("Ratio: {}x", result.ratio);
println!("Chunks retained: {}", result.chunks);
println!("Confidence: {}", result.confidence);

Key Functions

// High-level compressor
pub struct ContextCompressor {
    pub fn new() -> Self
    pub fn with_config(config: MscConfig) -> Self
    pub fn compress_for_task(
        &self, data: &[u8], task_type: TaskType, context: &[u8]
    ) -> Result<ContextDiff, ContextError>
}

// Low-level similarity
pub fn cosine_similarity(a: &ContextEmbedding, b: &ContextEmbedding) -> Result<f64, ContextError>
pub fn batch_cosine_similarity(
    query: &ContextEmbedding, candidates: &[ContextEmbedding]
) -> Result<Vec<(usize, f64)>, ContextError>

Benchmark Impact

From the ablation study:

Scenario	Cost/Task	Ctx Compression
ATP (full)	$0.0393	28.0x
ATP w/o SCD	$0.0627	1.0x

Removing SCD increases cost by 59% — the single biggest cost driver in the stack.

Why This Matters

• Cost: Sending 28x less context means 28x lower token costs
• Speed: Less data to transfer and process
• Quality: Irrelevant context can actually hurt agent performance
• Scalability: Makes large multi-agent workflows economically viable

Next Steps

• Layer 4: Economic Routing — How routes are optimized
• Benchmarks — Full ablation analysis