nerfprobe-core 0.2.0

Shared probe and scorer implementations for LLM degradation detection

NerfProbe Core

Shared probe and scorer implementations for scientifically-grounded LLM degradation detection.

Installation

pip install nerfprobe-core

Overview

nerfprobe-core provides the detection logic used by:

• nerfprobe - CLI tool for developers
• NerfStatus - Monitoring service

Probes

14 probes across 3 tiers, each grounded in peer-reviewed research:

Core Tier

Probe	Detection	Research
MathProbe	Arithmetic reasoning degradation	2504.04823
StyleProbe	Vocabulary collapse (TTR)	2403.06408
TimingProbe	Latency fingerprinting	2502.20589
CodeProbe	Syntax collapse	2512.08213

Advanced Tier

Probe	Detection	Research
FingerprintProbe	Framework detection	2407.15847
ContextProbe	KV cache compression	2512.12008
RoutingProbe	Model routing detection	2406.18665
RepetitionProbe	Phrase looping	2403.06408
ConstraintProbe	Instruction adherence	2409.11055
LogicProbe	Reasoning drift	2504.04823
ChainOfThoughtProbe	CoT integrity	2504.04823

Optional Tier

Probe	Detection	Research
CalibrationProbe	Confidence calibration	2511.07585
ZeroPrintProbe	Mode collapse	2407.01235
MultilingualProbe	Cross-language asymmetry	EMNLP.935

Scorers

10 scoring implementations:

• MathScorer - Expected answer matching
• TTRScorer - Type-Token Ratio calculation
• CodeScorer - Python syntax validation
• RepetitionScorer - N-gram repetition detection
• ConstraintScorer - Word count and forbidden word checks
• LogicScorer - Answer + reasoning validation
• ChainOfThoughtScorer - Step counting & circular detection
• CalibrationScorer - Confidence extraction
• EntropyScorer - Shannon entropy calculation
• MultilingualScorer - Cross-language consistency

Model Registry

Ships with 10 SOTA models (Dec 2025) with probe-relevant fields:

• context_window - For ContextProbe
• knowledge_cutoff - For TemporalProbe

from nerfprobe_core import get_model_info, RESEARCH_PROMPT

# Known model
info = get_model_info("gpt-5.2")
print(f"Context: {info.context_window:,}")

# Unknown model - get research prompt
prompt = RESEARCH_PROMPT.format(model_name="new-model", provider="provider")

Usage

from nerfprobe_core import ModelTarget
from nerfprobe_core.probes import MathProbe
from nerfprobe_core.probes.config import MathProbeConfig

# Configure probe
config = MathProbeConfig(
    prompt="What is 15 * 12 + 8 * 9?",
    expected_answer="252",
)

# Run probe
target = ModelTarget(provider_id="openai", model_name="gpt-5.2")
probe = MathProbe(config)
result = await probe.run(target, gateway)

print(result.summary())  # math_probe: PASS (1.00) in 234ms

Dependencies

• pydantic>=2.0.0
• pyyaml>=6.0.0

License

Apache-2.0