How to Adjust the Confidence Threshold for Accepting Calls in Needle 2

The confidence threshold in Needle 2 is controlled by the min_confidence parameter passed to run_tests(), which filters out any model-generated call whose confidence score falls below the specified value.

Needle 2 uses a confidence-based gating mechanism to decide whether to accept or reject calls generated by the underlying language model. According to the cactus-compute/needle source code, this gate is implemented in the test harness and can be adjusted programmatically or via CLI flag.

Where the Confidence Threshold Is Enforced

The core logic resides in needle/environments/_harness.py. The run_tests() function accepts a min_confidence parameter and applies it to every model response:

# From needle/environments/_harness.py

def run_tests(module, min_confidence=0.0, verbose=True):
    """
    Run tests for a given environment module.
    
    Parameters:
        module: The environment module containing test cases
        min_confidence: Minimum confidence threshold (0.0-1.0) for accepting calls
        verbose: Whether to print detailed output
    """
    for test_case in module.get_test_cases():
        response = model.generate(test_case.prompt)
        
        # Confidence gate: reject calls below threshold

        confidence = response.get("confidence", 0.0)
        if confidence < min_confidence:
            # Call is rejected/treated as failure

            handle_rejection(test_case, response, confidence)
            continue
            
        # Call accepted: proceed with execution

        execute_call(response)

The harness defaults to min_confidence=0.0, meaning all calls are accepted unless you explicitly raise the threshold.

How Confidence Scores Are Generated

The confidence value originates from the model architecture itself. In needle/model/architecture.py, the ConfidenceHead module produces scalar confidence scores during inference:

# From needle/model/architecture.py

class ConfidenceHead(nn.Module):
    """
    Predicts confidence score for model-generated outputs.
    """
    def forward(self, hidden_states):
        # Projects to single confidence value

        return torch.sigmoid(self.projection(hidden_states[:, -1, :]))
    
class NeedleModel(nn.Module):
    # ...

    
    def forward_confidence(self, input_ids, attention_mask=None):
        """Generate confidence score for a given input."""
        hidden = self.encoder(input_ids, attention_mask=attention_mask)
        return self.confidence_head(hidden.last_hidden_state)

When the model generates a response, forward_confidence() attaches the score to the response dictionary as response["confidence"]. If the model lacks a trained confidence head, this field will be None; the harness treats None as 0.0, causing any positive threshold to reject those responses automatically.

Method 1: Programmatic Adjustment via Environment Module

For environment-specific testing, import the environment's run_tests function and specify your threshold:

from needle.environments.smart_home import run_tests

# Require 60% confidence for smart home calls

results = run_tests(min_confidence=0.6, verbose=True)

# Results will show which calls passed/failed the confidence gate

print(f"Passed: {results.passed}, Failed: {results.failed}")

This approach targets a single environment. The environment module's run_tests is typically a thin wrapper that calls the harness with environment-specific test cases.

Method 2: Global Adjustment Across All Environments

To apply a consistent threshold across multiple environments, use the generic entry point:

from needle.environments import run_tests as run_all_tests

# Apply 45% confidence floor to every environment

all_results = run_all_tests(min_confidence=0.45)

for env_name, result in all_results.items():
    print(f"{env_name}: {result.pass_rate:.1%} pass rate")

The generic run_tests iterates through registered environments and invokes each with the shared min_confidence value.

Method 3: Command-Line Interface

The CLI in needle/cli.py exposes --min-confidence as a direct flag:

# Run with 70% confidence threshold

needle run --min-confidence 0.7

# Combine with other flags

needle run --min-confidence 0.5 --verbose --output results.json

# Per-environment override

needle run smart_home --min-confidence 0.8

The CLI parser forwards the flag value directly to the underlying run_tests() call, making it equivalent to the programmatic approaches.

Choosing an Appropriate Threshold

The optimal min_confidence value depends on your risk tolerance and model calibration:

Threshold	Use Case	Trade-off
0.0	Development/debugging	Maximum recall, accepts all calls
0.3-0.5	Balanced production	Moderate precision/recall balance
0.7-0.8	High-stakes applications	High precision, may reject valid calls
0.9+	Critical safety systems	Near-zero false positives, significant false negatives

Monitor your model's calibration curve to avoid setting thresholds in poorly-calibrated regions. The confidence head's output is passed through a sigmoid, so values naturally range 0-1.

Summary

• The confidence threshold is set via min_confidence in run_tests() from needle/environments/_harness.py
• Scores originate from ConfidenceHead.forward() in needle/model/architecture.py
• Adjust via environment module, generic entry point, or --min-confidence CLI flag
• None confidence values default to 0.0, so untrained models fail any positive threshold
• Threshold selection should balance precision requirements against call acceptance rates

Frequently Asked Questions

What happens if I set min_confidence above 1.0 or below 0.0?

Values outside [0.0, 1.0] are technically accepted by the function signature but behave predictably: thresholds above 1.0 reject all calls (since sigmoid outputs max at ~1.0), while negative thresholds accept all calls. The CLI validates range in needle/cli.py, raising argparse.ArgumentTypeError for out-of-bounds values.

Can I disable the confidence gate entirely?

Pass min_confidence=0.0 (the default) or any negative value. This accepts all calls regardless of confidence score, effectively disabling the feature without code modification.

Where can I calibrate or retrain the confidence head?

The ConfidenceHead is defined in needle/model/architecture.py. Fine-tuning requires labeled confidence data; the training loop in needle/training/trainer.py supports auxiliary confidence loss via the --confidence-weight hyperparameter. Retrain with your domain-specific data to improve calibration before adjusting thresholds.

Does the confidence threshold affect test reporting or just execution?

The threshold affects both. In needle/environments/_harness.py, failing the confidence check marks the test case as failed (affecting pass rates) and skips actual call execution. The test output includes both the confidence value and explicit "confidence_rejected" status for transparency.

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