EvalQReason: Step-Level Reasoning Evaluation for Large Language Models via Probability Distribution Analysis

Core Overview

EvalQReason is a three-stage framework for step-level reasoning evaluation of Large Language Models (LLMs) using probability distribution analysis. It eliminates manual annotation and achieves up to F1=0.98 in correctness prediction for math and medical tasks.

Basic Information

• Author: Shaima Ahmad Freja (University of Stavanger)
• Source: GitHub
• Release Time: 2026 June
• Link: https://github.com/Shaima4127/EvalQReason
• Contact: shaima.a.freja@uis.no

Traditional result-based LLM evaluation has critical flaws:

• Correct answers may come from wrong reasoning paths.
• Correct reasoning can lead to wrong answers due to calculation errors.
• Hallucinations/logical jumps are invisible in result-only assessment.

Manual step-level evaluation is costly and unscalable, so an automated, interpretable method is urgent.

Three-Stage Architecture

1. Reasoning Generation & Logit Extraction: Generate step-by-step chains and extract token logits (saved as .pkl; no closed-source API models like GPT-4).
2. Reasoning Dynamics Quantification: Compute CSD/SFC using KL/JS divergence, Hellinger distance, cosine similarity, entropy difference.
3. Pattern Analysis & Prediction: Visualize trajectories, use classic ML (XGBoost) and sequence models (GRU) for correctness prediction.

Key Metrics

• CSD: Local consistency between adjacent steps (low=smooth, high=drift).
• SFC: Global alignment between steps and final answers.

Datasets

Dataset	Domain	Scale	Difficulty
AIME	Math	240	3 levels
Math-500	Math	500	5 levels
MedQA	Medical	1273	2 levels

Models Tested

• Qwen2.5-7B-Instruct
• MathStral-7B
• Qwen-Medicine-7B
• Qwen3-4B
• Qwen3-8B

Cross-domain/scale design ensures generalizability.

Best Performance

Algorithm	Model Type	Classifier	Dataset	LLM	F1	ROC-AUC
CSD	Classic ML	XGBoost	AIME	Qwen3-4B	0.91	0.90
CSD	Sequence	GRU	Math-500	Qwen3-8B	0.98	0.90
SFC	Sequence	NN	Math-500	Qwen3-8B	0.98	0.96

Findings

1. CSD outperforms SFC in most cases.
2. Sequence models (GRU) beat classic ML.
3. Stable across 4B-8B model scales.
4. Math tasks have clearer patterns than medical tasks.

Hardware

Stage	Requirements	Notes
1	GPU (A100)	Logit extraction
2	CPU (≥64GB)	Large .pkl files
3	CPU	ML training

Code Plan

After paper acceptance: open-source prompt scripts, reasoning generators, divergence tools, ML notebooks, example CSV files.

EvalQReason enables:

1. Interpretable Diagnosis: Identify reasoning drift via CSD/SFC trajectories.
2. Boundary Detection: Find model weaknesses across difficulty levels.
3. Domain Strategies: Adapt evaluation for math/medical tasks.
4. Data Quality: Filter incoherent reasoning samples.

EvalQReason offers a novel step-level evaluation approach—no manual annotation, using model probability distributions. Its F1=0.98 in math tasks shows great potential for improving LLM reliability, making it valuable for researchers and developers.