# How Needle 2 Generates Confidence Scores and How to Use Them in Your Applications

> Discover how Needle 2 generates confidence scores using a ConfidenceHead on hidden states. Learn to integrate this score into your applications for response correctness estimates.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: how-to-guide
- Published: 2026-09-06

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**Needle 2 produces a confidence score by running a dedicated `ConfidenceHead` on the model's hidden states, returning a scalar logit that represents the model's internal estimate of response correctness.**

The cactus-compute/needle repository implements a sophisticated confidence estimation system in its second-generation model. Understanding how this score is generated—and its limitations with fine-tuned weights—enables developers to build more robust applications that can gate actions or rank candidate responses based on model certainty.

## How the Needle 2 Confidence Score Is Generated

The confidence score generation follows a multi-step pipeline that operates on the Transformer's internal representations.

### Extracting Hidden Cells

After the Transformer processes input tokens, the `hidden_cells()` method extracts per-layer hidden representations. These *cells* capture the model's intermediate computations across all layers.

### The ConfidenceHead Architecture

In [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) (lines 63–76), the `ConfidenceHead` class implements the core scoring logic:

1. **Probe pooling**: The `probe_pool` mechanism (lines 29–41) aggregates information across the hidden cells using a learned pooling operation shared with the contrastive head.

2. **Linear projection**: A single-output dense layer `nn.Dense(1, ...)` projects the pooled representation to a scalar logit.

The `forward_confidence()` method (lines 572–577) orchestrates this by feeding extracted cells to the `ConfidenceHead`:

```python
from needle.model.architecture import SimpleAttentionNetwork, TransformerConfig
import jax.numpy as jnp

cfg = TransformerConfig(d_model=768, num_layers=27)   # Needle‑2 preset

model = SimpleAttentionNetwork(cfg)

# Dummy token sequence (already tokenised)

tokens = jnp.array([[42, 17, 8, 0, 0]])               # pad token = 0

conf_logit = model.forward_confidence(tokens)
print("Raw confidence logit:", float(conf_logit))

```

Higher logit values indicate greater model confidence that the generated response is correct.

### Critical Limitation with Fine-Tuned Models

When fine-tuned weights are loaded, the confidence head is **not re-trained**. According to [`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) (lines 61–63), the library overwrites the confidence field with `None` and issues a warning that the score is uncalibrated. The raw `forward_confidence` method remains callable on the underlying model, but its output will not reflect fine-tuned behavior.

## How to Use the Needle 2 Confidence Score

Developers can access confidence scores at two levels of abstraction.

### Direct Model Access

For maximum control, call `forward_confidence()` directly on a `SimpleAttentionNetwork` instance:

```python
from needle.model.architecture import SimpleAttentionNetwork, TransformerConfig
import jax.numpy as jnp

cfg = TransformerConfig()                     # use default or custom config

model = SimpleAttentionNetwork(cfg)           # instantiate the network

tokens = jnp.array([[1, 2, 3, 4]])            # example token IDs

conf = model.forward_confidence(tokens)      # ← scalar confidence logit

print("Confidence logit:", conf.item())

```

This approach returns the raw logit before any transformation, useful for custom calibration or logging.

### High-Level Needle API

The simpler approach uses the public `Needle` class:

```python
from needle import Needle

needle = Needle()                     # no fine‑tuned weights → base Needle 2

response = needle.complete("What is the capital of France?")

# response is a dict, e.g. {"type": "call", "output": "...", "confidence": 0.87}

print("Confidence:", response["confidence"])

```

The API returns confidence as a normalized value within the response dictionary.

### Practical Applications: Confidence Gating

The primary use case is **threshold-based action gating**:

```python
from needle import Needle

needle = Needle()
MIN_CONF = 0.5

resp = needle.complete("Explain quantum tunnelling.")
if resp["confidence"] is not None and resp["confidence"] < MIN_CONF:
    print("Confidence too low – retry or fallback.")
else:
    print("Answer:", resp["output"])

```

Additional applications include:
- **Response ranking**: Select the highest-confidence candidate from multiple generations
- **Human escalation**: Route low-confidence queries to human review
- **Uncertainty quantification**: Track confidence distributions across request types

## Summary

- **Confidence generation**: `ConfidenceHead` pools hidden cells via `probe_pool` and applies a dense layer to produce a scalar logit (`architecture.py#L63‑L76`).
- **API access**: `forward_confidence()` for direct model access; `Needle.complete()` returns normalized scores in the response dict.
- **Fine-tuned limitation**: Confidence is set to `None` with fine-tuned weights because the head is not re-trained (`__init__.py#L61‑L63`).
- **Best practice**: Always check for `None` before using confidence scores, and implement fallback logic for low-confidence responses.

## Frequently Asked Questions

### What does the Needle 2 confidence score actually measure?

The score measures the model's internal estimate of response correctness based on patterns learned during pre-training. It is **not** a calibrated probability—higher values indicate greater confidence but do not map directly to accuracy percentages.

### Why is confidence None when using fine-tuned Needle 2 models?

The `ConfidenceHead` weights are frozen during fine-tuning. Since the head was trained on base model behavior, its outputs become unreliable after weight updates. The library explicitly sets `response["confidence"] = None` to prevent misuse of uncalibrated scores.

### Can I recalibrate the confidence head after fine-tuning?

The source code does not provide built-in recalibration. You can still call `forward_confidence()` on the underlying `SimpleAttentionNetwork` to obtain raw logits, but you would need to implement your own calibration procedure (e.g., temperature scaling or Platt scaling) on a held-out validation set.

### How should I choose a confidence threshold for gating?

Threshold selection depends on your application's cost of error versus cost of fallback. Start with 0.5–0.6 for general use, then tune based on observed precision-recovery tradeoffs on your specific task. Log confidence distributions to identify natural decision boundaries in your data.