# How to Configure Confidence Thresholds for Needle Tool Gating

> Configure confidence thresholds for Needle tool gating by checking the confidence field returned by Needle.complete. Programmatically gate tool execution based on your application's risk tolerance.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: how-to-guide
- Published: 2026-08-15

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**Set a confidence threshold in the Needle library by checking the `"confidence"` field returned by `Needle.complete()` and programmatically gating tool execution based on your application's risk tolerance.**

The **confidence threshold** is a critical control mechanism when deploying language models with tool use. In the `cactus-compute/needle` repository, confidence scores are generated by a dedicated neural head that evaluates how certain the model is about its generated output. This article explains how to access these scores, configure effective thresholds, and implement gating logic that prevents low-confidence tool calls.

## Understanding the ConfidenceHead Architecture

The Needle library implements confidence scoring through a specialized neural component defined in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py). Understanding this architecture helps you interpret the raw scores correctly.

### Where Confidence Scores Originate

The **ConfidenceHead** (lines 63–76) is a compact probe network that produces a scalar logit for each token sequence:

```python

# Conceptual structure from needle/model/architecture.py

class ConfidenceHead:
    def __init__(self, dtype):
        # Probe pool followed by dense projection

        self.probe = ProbePool(...)
        self.projection = Dense(...)
    
    def __call__(self, hidden_states):
        # Returns single logit per sequence

        return self.projection(self.probe(hidden_states))

```

This head is instantiated in the main model class (lines 88–89) and exposed through `forward_confidence` (lines 72–77), making the score accessible to callers.

### How Scores Flow Through the API

The confidence value travels through several integration points:

1. **Model wiring** – `self.confidence_head` is registered during `Needle` initialization
2. **Export metadata** – assigned identifier `2` in `HEAD_CODES` ([`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py), lines 48–50) for model serialization
3. **Runtime API** – returned in the response dictionary from `Needle.complete()`

The library deliberately applies **no activation function** to the raw logit. You receive a `float32` value that may be positive or negative, which you can optionally transform (e.g., via `sigmoid`) for probabilistic interpretation.

## Retrieving Confidence Scores at Runtime

Before configuring thresholds, you must ensure confidence values are actually available in your responses.

### Base Models vs. Fine-Tuned Checkpoints

A critical implementation detail in [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) (lines 58–60) affects confidence availability:

| Model Type | Confidence Available | Reason |
|------------|---------------------|--------|
| Base model (`.cact`) | ✅ Yes | ConfidenceHead is active and trained |
| Fine-tuned checkpoint | ❌ `None` | Head weights not updated during fine-tuning |

The library emits a runtime warning when loading a fine-tuned checkpoint, setting the confidence field to `None` to prevent misleading scores. To restore confidence on a tuned model, **re-export the checkpoint** using the same library version with the confidence head included.

### Accessing the Confidence Field

Call `Needle.complete()` or `Needle.run()` and extract the value:

```python
import needle

agent = needle.Needle(weights="path/to/base.cact", tools=[my_tool])

response = agent.complete("Query here", max_new_tokens=256)
confidence = response.get("confidence")  # float or None

if confidence is None:
    # Handle fine-tuned checkpoint case

    raise RuntimeError("Confidence unavailable for this model version")

```

## Implementing Confidence Threshold Gating

With confidence scores in hand, implement threshold-based gating to control tool execution risk.

### Basic Threshold Pattern

Define a threshold constant and compare against retrieved scores:

```python
import needle
import math

# Load base model to ensure confidence availability

agent = needle.Needle(
    weights="path/to/base.cact",
    tools=[search_tool, calculator_tool]
)

CONFIDENCE_THRESHOLD = 0.7  # Adjust based on application criticality

def execute_with_confidence_gate(prompt, max_new_tokens=256):
    """
    Generate response and gate tool execution on confidence threshold.
    Returns output or raises if confidence insufficient.
    """
    response = agent.complete(prompt, max_new_tokens=max_new_tokens)
    
    conf = response.get("confidence")
    if conf is None:
        raise RuntimeError(
            "Confidence unavailable – fine-tuned checkpoint loaded. "
            "Re-export model with confidence head or use base weights."
        )
    
    # Optional: convert logit to probability

    conf_prob = 1 / (1 + math.exp(-conf))  # sigmoid transform

    
    if conf_prob < CONFIDENCE_THRESHOLD:
        # Gate triggered: handle low-confidence case

        return handle_low_confidence(prompt, response, conf_prob)
    
    # Confidence acceptable: proceed with tool execution

    return response["output"]

```

### Dynamic Threshold Strategies

Static thresholds may not suit all operational contexts. Consider these **adaptive approaches**:

- **Operation-tier thresholds** – Stricter gates (0.85+) for destructive operations (database writes, payments), lenient gates (0.5+) for read-only queries
- **User-adjustable sensitivity** – Expose threshold as configuration parameter per deployment
- **Consecutive failure backoff** – Lower threshold slightly after N gated rejections to prevent infinite loops

### Retry and Fallback Patterns

When confidence falls below threshold, you have several response options:

```python
def handle_low_confidence(prompt, original_response, confidence_score):
    """Multi-strategy handler for sub-threshold confidence."""
    
    # Strategy 1: Retry with modified prompt

    enhanced_prompt = f"{prompt}\n\nPlease be more specific and certain."
    retry_response = agent.complete(enhanced_prompt, max_new_tokens=256)
    
    # Strategy 2: Synthetic fallback response

    if confidence_score < 0.3:
        return "I cannot answer with sufficient confidence. Please clarify your request."
    
    # Strategy 3: Human escalation queue

    queue_for_review(prompt, original_response, confidence_score)
    
    return retry_response["output"]

```

## Calibrating Your Confidence Threshold

Threshold selection balances **recall** (not missing valid tool calls) against **precision** (avoiding incorrect executions).

### Empirical Calibration Workflow

1. **Collect validation set** – 100–500 representative queries with ground-truth correctness labels
2. **Score distribution analysis** – Plot confidence scores for correct vs. incorrect predictions
3. **Threshold selection** – Choose cutoff at intersection of distributions or target false-positive rate
4. **Production monitoring** – Track gate trigger rate and human review outcomes

### Interpreting Raw Logits vs. Probabilities

The ConfidenceHead outputs unnormalized logits. For threshold design:

| Approach | Implementation | When Useful |
|----------|---------------|-------------|
| Raw logit threshold | `if conf > 0.5:` | When score distribution is roughly symmetric |
| Sigmoid probability | `sigmoid(conf) > 0.7:` | For intuitive "70% confident" semantics |
| Percentile-based | Dynamic from validation set | When absolute scale varies across model versions |

## Summary

- **ConfidenceHead** in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) generates per-sequence confidence logits that are cast to `float32` and returned via `Needle.complete()`
- **Fine-tuned checkpoints disable confidence** (set to `None`) unless re-exported with the confidence head preserved
- **Threshold gating** is implemented in user code by comparing `response["confidence"]` against application-defined limits
- **Raw logits** require transformation (sigmoid) for probability interpretation, or direct thresholding if empirically calibrated
- **Key files**: [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) (API), [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) (head definition), [`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py) (serialization metadata)

## Frequently Asked Questions

### Why is confidence `None` when I load my fine-tuned model?

The Needle library intentionally disables confidence reporting for fine-tuned checkpoints because the ConfidenceHead weights are not updated during fine-tuning. According to [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) lines 58–60, loading such a checkpoint triggers a warning and sets confidence to `None`. To restore confidence, re-export your fine-tuned model using the same Needle version with the confidence head included in the export.

### Should I apply sigmoid to the confidence score before thresholding?

The choice depends on your calibration approach. The raw confidence value is an unnormalized logit. Applying `sigmoid(conf)` yields a 0–1 probability that may be more interpretable, but a directly calibrated threshold on raw logits performs equally well if validated empirically. The library applies no activation, giving you full control.

### Can I use different thresholds for different tools?

Yes. Since threshold gating occurs in your application code after receiving `response["confidence"]`, you can implement dynamic logic that selects thresholds based on which tool the model selected, the operation's risk level, or user-specific preferences. The Needle API returns confidence independently of tool routing decisions.

### What confidence threshold value should I start with?

Begin with **0.7** (after sigmoid transformation) for moderate-risk applications, **0.85** for high-stakes operations, and **0.5** for exploratory or read-only use cases. Validate against your specific task distribution, as optimal thresholds vary significantly based on prompt complexity and tool criticality.