| --- |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - benchmark |
| - coding |
| - math |
| - science |
| - reasoning |
| - logic |
| - evaluation |
| pretty_name: CHIMERA Bench v3.0 Mega |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # CHIMERA Bench v3.0 Mega |
|
|
| **Comprehensive Hybrid Intelligence Metric for Excellence in Reasoning & Analysis** |
|
|
| **8503 articulated multi-step problems** across 4 domains (larger than GSM8K). |
|
|
| | Domain | Problems | Focus | |
| |--------|----------|-------| |
| | **MATH** | 3803 | Multi-step word problems: shopping, speed/distance, geometry, combinatorics, algebra, number theory, calculus | |
| | **CODE** | 1500 | Code tracing, bug finding, algorithm design, complexity analysis, OOP, recursion | |
| | **SCIENCE** | 1500 | Physics (projectile, energy, circuits), chemistry (stoichiometry, pH, gas laws), biology (genetics, ecology) | |
| | **THINK** | 1700 | Constraint satisfaction, knights & knaves, scheduling, deduction, pattern recognition, estimation | |
| | **Total** | **8503** | | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("vectionlabs/chimera-bench-v1", split="test") |
| print(f"{len(ds)} problems loaded") |
| for p in ds.select(range(5)): |
| print(f"[{p['domain']}] {p['title']}: {p['prompt'][:80]}...") |
| ``` |
|
|
| ## Evaluation Types |
|
|
| | Type | Method | |
| |------|--------| |
| | `numeric` | Value within tolerance | |
| | `exact` | Normalized string match | |
| | `keyword` | Required keywords present | |
|
|
| ## Difficulty: 1-5 stars (10-100 points) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{chimera-bench-v3-2025, |
| title={CHIMERA Bench v3: Comprehensive Hybrid Intelligence Metric for Excellence in Reasoning and Analysis}, |
| year={2025}, |
| url={https://huggingface.co/datasets/vectionlabs/chimera-bench-v1} |
| } |
| ``` |
|
|
