Precision Mode: Confidence-Scored Prompt Compression

Why Precision Mode?

Standard compiler mode maximizes token savings. It's great for most use cases — chat history, RAG contexts, agent memory — because the query-aware scoring ensures answer-critical lines are preserved. But sometimes you need stronger guarantees:

• Legal/financial document review — every clause matters, even tangentially relevant ones
• Clinical decision support — dropping a single symptom mention could change the diagnosis
• Compliance audit trails — you must prove no answer-critical content was removed
• Agent orchestration — the orchestrator needs to trace decisions across compressed turns

Precision mode adds a verifier that explicitly scores each compression output. Instead of hoping the compression kept enough context, you get a measured confidence score.

Architecture

Precision Model (AMCP)

The precision model extends the base 16-dim policy to a 20-dim Adaptive Multi-Context Policy with:

• 4 additional features: line type score (code/JSON/narrative), block importance (semantic section boundaries), token entropy (information density), and cross-context similarity (redundancy detection)
• Gating vector (20-dim) that learns which features matter for different content types
• Shared encoder (64-dim hidden layer) feeding two heads
• Score head: BCE loss vs oracle importance labels (primary objective)
• Confidence head: Auxiliary loss that aligns confidence estimates with actual precision

The precision policy stays a small keep/drop network — fast on CPU, no GPU required.

Verifier Model

The verifier is a small MLP that scores the quality of a compression result:

Linear(16, 8) → ReLU → Linear(8, 1) → Sigmoid

It extracts 16 features from the compressed output:

The verifier was trained on 10,000 synthetic samples with known quality labels and achieves 100% accuracy on balanced holdout data.

How It Works

The precision compression algorithm follows a progressive budget search:

budget_ratios = [0.40, 0.35, 0.30, 0.25, 0.20]

for budget_ratio in budget_ratios:
    compressed = compressAdaptive(text, query, budget_ratio)
    features = extractVerifierFeatures(compressed, text)
    confidence = forwardVerifier(features)
    entityOk = checkEntityPreservation(compressed, query)

    if confidence >= 0.85 and entityOk:
        return compressed  # Accept this budget

return compressAdaptive(text, query, 0.40)  # Fallback to safest

Key behaviors:

• Starts conservative (40% token budget) — safe for all content
• Escalates aggressively (down to 20%) — maximizes savings when quality allows
• Verifier gates every step — never accepts a result below threshold
• Entity check — ensures query-relevant named entities survive
• Graceful fallback — returns compiler mode result if no budget meets threshold

Benchmarks

Usage

HTTP API

curl -X POST https://supercompress.dev/api/v1/compress \
  -H "X-API-Key: sc_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "context": "long context...",
    "query": "What matters?",
    "mode": "precision"
  }'

# Response includes:
# {
#   "compressed_text": "...",
#   "confidence": 0.92,
#   "confidence_ok": true,
#   "budget_ratio": 0.30,
#   "tokens_saved": 850,
#   "tokens_saved_pct": 65.0,
#   "compression_risk": "low"
# }

Python client

from supercompress.client import SuperCompress

sc = SuperCompress(api_key="sc_live_YOUR_KEY")
result = sc.compress(
    context="long context...",
    query="What matters?",
    mode="precision"
)
print(f"Confidence: {result.confidence}")
print(f"Budget used: {result.budget_ratio}")
print(f"Risk: {result.compression_risk}")

Browser (JS)

const model = await SuperCompressEngine.loadPrecisionModel("assets/data/model_precision.json");
const verifier = await SuperCompressEngine.loadVerifier("assets/data/verifier.json");
const result = SuperCompressEngine.compressPrecision(context, query, model, verifier);
console.log(`Confidence: ${result.confidence}, Budget: ${result.budget_ratio}`);

When to Use

What's Next

The verifier is currently a static MLP. Future work includes:

• Fine-tuning the verifier on real production data for even better calibration
• Adaptive thresholds that learn per-user quality requirements
• Multi-level verifiers (sentence, block, and document level)
• End-to-end training where the verifier loss backpropagates into the compressor

Precision Mode is available in SuperCompress v0.6+. Get your API key to try it on your own context. Or open the playground to test it in-browser.