| # 🔬 ChemArgus |
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|
| ### Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark |
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| **ChemArgus** is a competition-level, multimodal, diagnostic benchmark for expert |
| chemistry reasoning. Problems are drawn from chemistry competition material, |
| textbooks, and research papers — and scored by fine-grained rubrics with partial |
| credit, instead of answer endpoints. |
|
|
| > Chemistry evaluation has moved past multiple-choice probes toward open-ended |
| > expert problems, yet current frontier benchmarks still score answer endpoints |
| > alone. Such scoring conflates memorized facts with stepwise derivation, treats |
| > visual and textual inputs as interchangeable, and confounds long-horizon |
| > planning with stepwise execution. ChemArgus replaces the answer endpoint with |
| > three measurement properties: |
| > |
| > - **Fine-grained scoring** — every subquestion is graded point by point with |
| > partial credit, so a score lands at the level of the step. |
| > - **Multimodal capability** — every problem with a real image is paired with a |
| > controlled text description, pricing visual understanding against text. |
| > - **Diagnostic attribution** — paired metrics share one scoring unit and differ |
| > only in context construction, isolating integration, propagation, or local |
| > reasoning as the cause of a score gap. |
|
|
| --- |
|
|
| ## 📊 Dataset Partitions |
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| | | Share | Problems | Subquestions | Marks | Images | Rubrics | |
| | --- | ---: | ---: | ---: | ---: | ---: | --- | |
| | 🧪 **dev** · Public Dev Set | 25% | 48 | 209 | 676 | 350 | ✅ included | |
| | 🏁 **test** · Held-out Test Set | 55% | 105 | 391 | 1271 | 173 | ❌ stripped | |
| | 🔒 **private** · Private Set | 20% | — | — | — | — | 🔐 local only | |
| | **Total (public)** | — | **153** | **600** | **1947** | **523** | | |
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|
| > 🖼 **Balanced image split** — problems are assigned deterministically |
| > (largest-image-first) so that the total number of model-visible images is |
| > roughly equal across the three partitions (≈164 images per partition on |
| > average), while the 25/55/20 problem ratio is kept exact. The private |
| > partition is never published, so its figures are omitted here. |
|
|
| ## 📁 Repository Layout |
|
|
| ``` |
| dev/ |
| dataset.jsonl one JSON object per dev problem (with rubrics) |
| instance_ids.json the 48 dev problem ids |
| tasks/<problem_id>/ |
| problem.json full problem: prompt + rubric + image metadata |
| images/*.png |
| test/ |
| dataset.jsonl one JSON object per test problem (rubrics stripped) |
| instance_ids.json the 105 test problem ids |
| tasks/<problem_id>/ |
| problem.json problem prompt; rubric fields absent |
| images/*.png |
| ``` |
|
|
| ## 🧬 Data Format |
|
|
| Each problem is a JSON object: |
|
|
| ```json |
| { |
| "problem_id": "Exam-01-T10", |
| "header": "Light", |
| "header_images": [ |
| {"file": "Exam-01-T10-header-1.png", "description": "..."} |
| ], |
| "subquestions": [ |
| { |
| "sub_id": "Exam-01-T10-1", |
| "tags": ["2.2", "2.4"], |
| "text_open": "10-1 Light can excite certain chemical bonds. ...", |
| "text_images": [ |
| {"file": "Exam-01-T10-1-1-prompt.png", "description": "..."} |
| ], |
| "full_mark": 8, |
| "rubric": ["[C]: ... (4 points)", "D: ... (4 points)"], |
| "rubric_images": [ |
| {"file": "Exam-01-T10-1-1-rubric.png", "description": "..."} |
| ] |
| } |
| ] |
| } |
| ``` |
|
|
| | Field | Meaning | |
| | --- | --- | |
| | `problem_id` | `Exam-<nn>-T<task>` — `nn` is the 01–23 exam index | |
| | `sub_id` | `{problem_id}-{n}` (sequential within the problem) | |
| | `header` | Shared background of the whole problem | |
| | `text_open` | Subquestion text | |
| | `full_mark` | Integer maximum score of the subquestion | |
| | `rubric` | Scoring points with partial-credit annotations — **absent in `test/`** | |
| | `*_images[].file` | Image file name (see naming scheme below) | |
| | `*_images[].description` | Text description of the image content | |
|
|
| Image files follow a unified naming scheme: |
| `{problem_id}-{sub_index}-{n}-prompt|rubric.png` (subquestion images) and |
| `{problem_id}-header-{n}.png` (problem-level images). |
|
|
| > ⚠️ **Image visibility** — `header_images` and `text_images` are |
| > **model-visible**; `rubric_images` are **private scoring images**, given only |
| > to the judge, never to the answering model. They do not appear in `test/` at all. |
| |
| ## 🔀 Evaluation Protocols |
| |
| Three protocols share one scoring unit and differ only in context construction: |
| |
| 1. **`full_problem`** — the model receives the complete problem in one call and |
| answers everything in one response. |
| 2. **`sequential_carry`** — subquestions are answered in order; each answer is |
| carried into the next step, with no access to gold answers. |
| 3. **`oracle_scaffolded`** — each subquestion is answered after being given the |
| correct result of the previous one; isolates local reasoning from error |
| propagation. |
| |
| ## ⚖️ Scoring |
| |
| The core metric is the **Normalized Rubric Score**: rubric-grounded points |
| awarded divided by the problem's total marks. Every subquestion is graded by an |
| LLM judge against the private rubric (partial credit per scoring point), with |
| structure/SMILES equivalence judged by chemical meaning. |
| |
| ## 🏅 Results & Leaderboard |
| |
| *Snapshot: September 2026 · Normalized Rubric Score (%). Runs are sorted by |
| `full_problem` within each input mode.* |
|
|
| 🥇 **`full_problem` leaders** — Multimodal: Claude Opus 5 53.8% · |
| Text: Claude Opus 5 61.5% · Text + reader: GLM-5.2 52.0% |
| |
| | Model | Input | full_problem | sequential_carry | oracle_scaffolded | |
| | --- | --- | ---: | ---: | ---: | |
| | Claude Opus 5 | multimodal | 53.8 | 59.4 | 64.9 | |
| | Gemini 3.8 Flash | multimodal | 48.5 | 58.5 | 60.3 | |
| | Grok 4.6 | multimodal | 33.8 | 41.8 | 45.5 | |
| | Qwen3.8-Max | multimodal | 29.3 | 31.2 | 34.0 | |
| | Kimi K3 | multimodal | 27.5 | 30.6 | 31.8 | |
| | MiniMax M3 | multimodal | 27.3 | 27.5 | 27.9 | |
| | GPT-5.6-Sol | multimodal | 23.2 | 28.8 | 30.4 | |
| | Claude Opus 5 | text | 61.5 | 61.7 | 61.7 | |
| | Gemini 3.8 Flash | text | 46.5 | 59.0 | 59.7 | |
| | Qwen3.8-Max | text | 36.0 | 39.0 | 41.9 | |
| | Kimi K3 | text | 34.5 | 36.0 | 37.5 | |
| | Grok 4.6 | text | 33.4 | 42.0 | 46.0 | |
| | GPT-5.6-Sol | text | 29.8 | 35.6 | 36.5 | |
| | MiniMax M3 | text | 26.2 | 28.7 | 29.1 | |
| | DeepSeek V4 Pro | text | 25.0 | 25.0 | 27.6 | |
| | GLM-5.2 | text | 23.5 | 24.9 | 25.1 | |
| | GLM-5.2 | text + reader | 52.0 | 52.2 | 52.4 | |
| | Claude Opus 5 | text + reader | 46.2 | 53.9 | 54.7 | |
| | DeepSeek V4 Pro | text + reader | 36.7 | 36.8 | 37.0 | |
| | Gemini 3.8 Flash | text + reader | 33.8 | 45.1 | 49.7 | |
| | Kimi K3 | text + reader | 32.3 | 32.3 | 32.4 | |
| | GPT-5.6-Sol | text + reader | 31.1 | 33.6 | 34.0 | |
| | Grok 4.6 | text + reader | 28.6 | 29.0 | 31.1 | |
| | Qwen3.8-Max | text + reader | 27.9 | 29.2 | 31.5 | |
| | MiniMax M3 | text + reader | 27.2 | 27.3 | 27.3 | |
| |
| Live leaderboard: <https://www.cosmosmind.ai/leaderboard/chemargus> |
| |
| ## 📖 Citation |
| |
| ```bibtex |
| @misc{chemargus2026, |
| title = {ChemArgus: Automated Scientific Diagnostics for Lab-Grade |
| Multimodal Chemistry Reasoning: A Frontier Benchmark}, |
| author = {Hanyu Guo and Mo Cui and Zihan Tan and Leixin Sun and Yichun Wang and |
| Yinan Shen and Guojun Zhu and Jia Jiang and Yichen Fan and Zui Chen and |
| Wenzhe Gao and Zhihao Yu and Qijun Wang and Yetian Zhu and Jiande Chen and |
| Chuhang Pei and Yao Jiang and Erhao Chen and Pinhan Wang and Xiaorui Pan and |
| Qianyi Zhu and Yang Liu and Mang Ye and Guancheng Wan}, |
| year = {2026}, |
| eprint = {2610.20012}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG}, |
| url = {https://arxiv.org/abs/2610.20012} |
| } |
| ``` |
| |
