# How to Debug When Needle Returns `confidence=None`: A Complete Guide

> Debug Needle confidence=None with this guide. Understand why finetuned weights exclude the confidence head and how to resolve it. Learn the intentional design behind this behavior.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: how-to-guide
- Published: 2026-08-20

---

**When Needle returns `confidence=None`, it is intentional design behavior—not an error—that occurs whenever you load custom finetuned weights, because the confidence head is excluded from the finetuning process.**

The Needle agent from the [cactus-compute/needle](https://github.com/cactus-compute/needle) repository deliberately nullifies confidence scores to prevent uncalibrated outputs. Understanding this mechanism helps you diagnose whether your code is working correctly or if you've encountered an edge case worth investigating.

## Why Needle Returns `confidence=None`

The confidence score originates from a dedicated **confidence head** in the model architecture. However, this head receives no gradient updates during finetuning. To prevent misleading calibration, the Needle engine forces `confidence=None` whenever custom weights are loaded.

### The Weight-Loading Logic

In [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py), the `Needle.__init__` constructor emits a warning and later overrides the confidence field:

```python

# needle/__init__.py

if weights:
    warnings.warn(
        "finetuning does not update the confidence head, so scores are "
        "uncalibrated for tuned weights; this agent reports confidence as None",
        stacklevel=2)
…
if self._weights:
    response["confidence"] = None

```

(source: [needle/__init__.py line 60-62](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py#L60-L62))

This check runs after every completion, ensuring no uncalibrated confidence leaks through.

### The Confidence Head Architecture

The head itself lives in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) and projects pooled hidden representations to a single logit:

```python

# needle/model/architecture.py

class ConfidenceHead(nn.Module):
    …
    @nn.compact
    def __call__(self, cells, keep=None):
        pooled = probe_pool(...)
        logit = nn.Dense(1, …)(pooled)
        return logit[..., 0].astype(jnp.float32)

```

(source: [needle/model/architecture.py line 63-75](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py#L63-L75))

The finetuning script explicitly acknowledges this limitation:

```python

# needle/model/finetune.py

print(f"  {'note':<9} confidence reports None with tuned weights; the head is not tuned")

```

(source: [needle/model/finetune.py line 401](https://github.com/cactus-compute/needle/blob/main/needle/model/finetune.py#L401))

## Step-by-Step Debugging Checklist

Follow these steps to verify that `confidence=None` is expected behavior in your setup.

### 1. Verify You Are Loading Custom Weights

Check your `Needle` constructor. If you pass a `weights=` argument, confidence will be `None` by design:

```python

# This WILL return confidence=None

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

# This WILL return a calibrated float

agent = needle.Needle()

```

### 2. Confirm the Warning Appears

The constructor emits a `UserWarning` when custom weights load. If suppressed, re-enable warnings:

```python
import warnings
warnings.simplefilter('always')

agent = needle.Needle(weights="tuned.cact")  # Should now show warning

```

### 3. Inspect the Response Directly

After any completion call, check the response dictionary:

```python
response = agent.complete("Explain quantum computing")
print(response.keys())           # Verify "confidence" exists as a key

print(response["confidence"])    # None or float

```

### 4. Determine If You Actually Need Confidence

- **Use base model**: Omit `weights=` to get calibrated confidence scores
- **Accept `None`**: Many production use cases function fine without confidence
- **Compute proxy confidence**: Access raw logits manually (advanced, see below)

### 5. Verify the Confidence Head Exists

Open [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) and confirm instantiation:

```python

# Should appear in your model class

self.confidence_head = ConfidenceHead(cfg.jax_dtype)

```

If this line is missing or commented, you have a build/branch issue—not standard behavior.

### 6. Check for Weight Mixing Bugs

The `_bind` method guards against loading different weights in the same process. If you see a runtime error rather than `None`, a previous agent with weights may still be active. Create a fresh Python process to isolate.

## Common Pitfalls and Misconceptions

| Pitfall | Reality |
|---------|---------|
| Silencing all warnings | You miss the explicit explanation for `None` confidence |
| Reusing a `Needle` instance with different weights | The library prevents this; must create new instance or process |
| Assuming `None` indicates failure | Completions are valid; only the metric is withheld |
| Expecting confidence from finetuned models | The head is explicitly excluded from training |

## Workarounds to Obtain Confidence with Custom Weights

If your application requires confidence scores despite using finetuned weights, you have two options.

### Option A: Patch `Needle.complete`

Override the method to skip the `None` assignment. This exposes raw, uncalibrated logits:

```python
import needle
import json

def _patched_complete(self, text, max_new_tokens=256):
    self._bind()
    rc = needle._lib().needle_complete(
        text.encode(), 
        int(max_new_tokens),
        self._buffer, 
        len(self._buffer)
    )
    if rc < 0:
        raise RuntimeError("needle_complete failed")
    response = json.loads(self._buffer.value.decode())
    # Bypass: do not set confidence to None

    return response

needle.Needle.complete = _patched_complete

```

**Warning**: These scores are uncalibrated and may not correlate with actual correctness.

### Option B: Manually Invoke the Confidence Head

For advanced use, access the JAX/Flax model directly:

```python
from needle.model.architecture import SimpleAttentionNetwork

# Assuming you have model instance `net` and tokenized input

tokens = ...  # jnp.array of token IDs

confidence_logit = net.forward_confidence(tokens)

```

This requires the same JAX runtime as the C++ engine and understanding of the internal API.

## Testing Expected Behavior

Run this verification script to confirm your setup matches design expectations:

```python
import needle

# Test 1: Base model returns float confidence

print("=== Base Model ===")
agent_base = needle.Needle()
resp_base = agent_base.complete("Write a haiku.")
print(f"confidence type: {type(resp_base['confidence'])}")
print(f"confidence value: {resp_base['confidence']:.4f}")

# Test 2: Finetuned weights return None

print("\n=== Finetuned Model ===")
agent_ft = needle.Needle(weights="path/to/weights.cact")
resp_ft = agent_ft.complete("Write a haiku.")
print(f"confidence type: {type(resp_ft['confidence'])}")
print(f"confidence value: {resp_ft['confidence']}")

```

Expected output:

```

=== Base Model ===
confidence type: <class 'float'>
confidence value: 0.8473

=== Finetuned Model ===
confidence type: <class 'NoneType'>
confidence value: None

```

## Key Source Files Reference

| File | Purpose |
|------|---------|
| [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) | Core agent class; forces `confidence=None` when weights loaded |
| [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) | `ConfidenceHead` definition and `forward_confidence` method |
| [`needle/model/finetune.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/finetune.py) | Training script with explanatory note about confidence head |
| [`tests/test_weights.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_weights.py) | Test coverage for envelope handling and response structure |

## Summary

- **`confidence=None` is intentional**, not an error, when using custom weights in Needle
- The **confidence head is not finetuned**, so uncalibrated scores are suppressed to prevent misuse
- Check for the **constructor warning** to confirm expected behavior
- Use the **base model** (no `weights=`) if you need calibrated confidence scores
- Advanced users can **patch the library** or access raw logits, but these lack calibration
- The relevant code spans [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py), [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py), and [`needle/model/finetune.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/finetune.py)

## Frequently Asked Questions

### Why does Needle disable confidence instead of showing uncalibrated scores?

Needle prioritizes reliability over convenience. Uncalibrated confidence scores can mislead users into trusting incorrect outputs. By forcing `confidence=None`, the library makes the uncertainty explicit rather than hiding it behind potentially deceptive numbers.

### Can I finetune the confidence head myself?

The current training pipeline in [`needle/model/finetune.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/finetune.py) excludes the confidence head from optimization. Modifying this would require changes to the loss function, data pipeline, and calibration validation. The repository does not currently support this workflow.

### How do I know if my `None` confidence is due to weights or a bug?

Three indicators confirm intentional behavior: (1) you passed `weights=` to the constructor, (2) a warning appeared at startup, and (3) completions succeed without errors. If any of these are missing—especially if the warning is absent despite loading weights—investigate your warning filter settings or check whether you are using a modified fork.