How to Set Confidence Thresholds for Needle 2 Responses

Needle 2 returns a calibrated confidence score (0–1) for every response; you check the "confidence" field and gate actions behind a threshold you choose per product.

The confidence score in Needle 2 is produced by a dedicated confidence head defined in needle/model/architecture.py【/cactus-compute/needle/main/needle/model/architecture.py#L63-L76】. This score represents the minimum of two signals—a calibrated post-hoc head and the decode probability—which must agree for a response to be trusted.

Understanding Confidence Score Availability

Needle 2 handles confidence differently depending on which weights you load.

Base Weights: Confidence Enabled

When you instantiate Needle() with base weights, the confidence head remains active. Every response includes a "confidence" field populated by the confidence head:

from needle import Needle

agent = Needle()
response = agent.complete("Summarize the latest AI research trends.")

# response contains: {"type": "answer", "content": "...", "confidence": 0.94}

Fine-Tuned Weights: Confidence Disabled

When you load fine-tuned weights via Needle(weights=...), the confidence head is not updated by the fine-tuning process. According to needle/__init__.py【/cactus-compute/needle/main/needle/init.py#L58-L62】, Needle deliberately disables the score and sets "confidence": null to prevent misleading reliability signals. A warning is emitted the first time such an agent is constructed.

The documentation in doc/finetuning.md【/cactus-compute/needle/main/doc/finetuning.md#L76-L84】 clarifies that responses from fine-tuned agents must be treated as untrusted since the confidence head no longer reflects the model's actual calibration.

Implementing a Confidence Threshold Check

The standard workflow for setting confidence thresholds follows four steps as documented in doc/apis.md【/cactus-compute/needle/main/doc/apis.md#L130-L134】:

1. Generate a response with agent.complete() or agent.run().
2. Inspect the "confidence" field.
3. Compare against your chosen threshold.
4. Act only if confidence meets or exceeds the threshold; otherwise re-ask, route to a higher-capacity model, or escalate.

Basic Threshold Example

from needle import Needle

agent = Needle()  # base weights, confidence enabled

response = agent.complete("Summarize the latest AI research trends.")
if response.get("confidence", 0) >= 0.8:
    print("✅ High-confidence answer:", response["content"])
else:
    print("⚠️ Low confidence – ask again or route elsewhere.")

Retry Loop with Threshold

For interactive applications, implement a retry loop that re-queries until confidence exceeds your threshold or reaches a maximum step limit:

from needle import Needle

agent = Needle()
max_steps = 5
threshold = 0.85

for _ in range(max_steps):
    resp = agent.run("Help the user fix a broken npm install.")
    conf = resp.get("confidence")
    
    if conf is None:
        # Fine-tuned weights detected

        print("⚠️ Confidence unavailable – treat as fallback.")
        break
        
    if conf >= threshold:
        print("✅ Accepted response:", resp["content"])
        break
    else:
        print(f"🔎 Confidence {conf:.2f} < {threshold} – retrying.")

Handling Fine-Tuned Models Gracefully

Always check for null confidence when your codebase supports both base and fine-tuned models:

from needle import Needle

agent = Needle(weights="my_finetuned.cact")

resp = agent.complete("Explain quantum entanglement.")

if resp.get("confidence") is None:
    print("ℹ️ Confidence disabled – using fallback verification.")
    # Implement alternative validation: human review, secondary model, etc.

else:
    if resp["confidence"] >= 0.8:
        deliver_response(resp["content"])
    else:
        escalate_to_human(resp)

Choosing Your Confidence Threshold

The documentation in doc/apis.md【/cactus-compute/needle/main/doc/apis.md#L151-L156】 explains that the confidence score is the minimum of the calibrated head output and the decode probability. Both signals must agree—this conservatism means:

• A threshold of 0.8 typically captures high-precision responses suitable for direct user-facing answers.
• A threshold of 0.9 or higher adds safety for sensitive applications (medical, financial, legal).
• Lower thresholds (0.6–0.7) may be acceptable with additional post-hoc verification.

Set your threshold per product based on error tolerance and available fallback options【/cactus-compute/needle/main/doc/apis.md#L130-L134】.

Key Source Files

File	Purpose
needle/model/architecture.py – ConfidenceHead class【/cactus-compute/needle/main/needle/model/architecture.py#L63-L76】	Implements the calibrated confidence scoring mechanism.
needle/__init__.py – response construction【/cactus-compute/needle/main/needle/init.py#L58-L62】	Injects or nulls the "confidence" field based on weight type.
doc/apis.md – confidence gating contract【/cactus-compute/needle/main/doc/apis.md#L151-L156】	Documents the minimum-of-two-signals logic and threshold recommendations.
doc/finetuning.md – fine-tuning effects【/cactus-compute/needle/main/doc/finetuning.md#L76-L84】	Explains why confidence is disabled for LoRA-fine-tuned weights.

Summary

• Needle 2 confidence scores are always available with base weights, always null with fine-tuned weights.
• The score in response["confidence"] is a float ∈ [0, 1] or null—check explicitly before comparison.
• Apply a product-specific confidence threshold (commonly 0.8–0.9) to gate high-stakes actions.
• For fine-tuned agents, implement fallback verification since the confidence head cannot attest to response reliability.

Frequently Asked Questions

What happens if I use a fine-tuned model without checking for null confidence?

Your code will likely throw a TypeError when comparing None to a numeric threshold, or worse, silently accept untrusted responses. Always check resp.get("confidence") is None and branch to fallback logic.

Why does fine-tuning disable the confidence head?

The confidence head is not updated during LoRA fine-tuning (doc/finetuning.md【/cactus-compute/needle/main/doc/finetuning.md#L76-L84】). Keeping it active would produce miscalibrated scores that falsely suggest reliability, so Needle explicitly sets "confidence": null to signal uncertainty.

Can I recalibrate the confidence head after fine-tuning?

Not through the current Needle 2 API. Recalibration would require retraining the confidence head on fine-tuned model outputs—a feature not exposed in the open-source release. For now, treat fine-tuned responses as requiring external validation.

What's the recommended threshold for production use?

Start with 0.8 for general applications, 0.9+ for high-stakes domains. The documentation emphasizes choosing per-product: err toward higher thresholds when errors are costly, lower ones when you have robust retry or human-in-the-loop fallbacks.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}