Step-Level Reasoning Verification
Individual step correctness validation, error classification, and first error step isolation for LLM reasoning alignment.
Quality & Precision Benchmarks
STEP VERIFICATION PRECISION
98.4%
FIRST ERROR RECALL
99.1%
ANNOTATOR CONSENSUS
Fleiss Îș = 0.891
SLA TURNAROUND
< 24 Hours Express
Dataset Taxonomy & Output Structure
step_index (0 to N)
step_text (Natural Language / Latex / Code)
step_label (+1: Correct, -1: Incorrect, 0: Neutral)
first_error_step_flag (Boolean Latch)
error_type (Arithmetic / Logic / Premise / Syntax)
Specific Type Tasks & Applications
- • LLM Hallucination Reduction in Mathematical Proofs
- • Code Generation Step Bug Isolation
- • Autonomous Task Planner Decision Audit
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Evaluating only final answers allowing incorrect intermediate logic to pass | PASS: Explicit step-level verification detecting first false premise or calculation error |
| FAIL: Unlabeled error steps forcing LLM reward models to guess hallucination points | PASS: Precise first error step isolation with error taxonomy classification |
Files & CoT Reasoning Example (Python)
import json
# Load Blue Projects Process Supervision Data Type: Step-Level Reasoning Verification
with open("step-level-reasoning-verification_cot_sample.json", "r") as f:
data = json.load(f)
print("Loaded Keys:", list(data.keys()))
Why Blue Projects for Step-Level Reasoning Verification?
Request a free matched 500-problem PRM sample batch formatted to your exact reasoning model requirements.
Request Free Sample Batch â