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694545c 0400752 694545c 0400752 694545c fd69a65 0400752 694545c 0400752 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | # IRSpectra-Bench leaderboard
Blind structure elucidation from **molecular formula + IR + ¹H + ¹³C** peak lists exactly as reported in open-access papers. Constitution scoring uses RDKit InChIKey connectivity (first 14 characters) unless noted.
**Paper:** [IRexp and IRSpectra-Bench](https://github.com/IlkhamFY/spectro-agent) (manuscript in preparation, 2026).
---
## Main benchmark (n = 194)
| Rank | Model / method | Top-1 ↑ | Recall (top-3) ↑ | Gen. recall | Verif. prec. \| recall | Notes |
|---:|---|--:|--:|--:|--:|---|
| 1 | Claude Fable 5 | **46%** | 54% | — | — | 24-compound subset only |
| 2 | Claude Opus + generate-wide + forward-verify | **30%** | — | 42% | 72% | 60-compound arm |
| 3 | Claude Opus + forward-verify | 30% | 33.5% | 34% | **89%** | Full benchmark (headline) |
| 4 | Claude Opus (solver self-rank) | 28.4% | 33.5% | 34% | 85% | Full benchmark |
| 5 | Grok 4.6 | — | — | 53% | 62% | 60-compound arm |
| 6 | Gemini 3.7 Flash | — | — | 50% | 73% | 60-compound arm |
| 7 | GPT-5.6 Sol | — | — | 42% | 68% | 60-compound arm |
| 8 | Claude Sonnet | 21% | 25% | — | — | 24-compound subset |
| 9 | Claude Haiku | 0% | 4% | — | — | 24-compound subset |
Bootstrap 95% CIs for the headline row: top-1 **28.4% [22–35]**, recall **33.5% [27–40]**. Corpus-reweighted top-1 (17.5% simple / 82.5% complex): **15.2% [11–20]**.
**Key finding:** verification precision exceeds generation recall for every vendor tested — the binding constraint is *candidate proposal*, not spectral ranking.
### By difficulty (Claude Opus, n = 194)
| Stratum | n | Top-1 | Recall |
|---|---:|--:|--:|
| All | 194 | 28.4% | 33.5% |
| Simple | 98 | 48.0% | 54.1% |
| Complex | 96 | 8.3% | 12.5% |
---
## Evaluate your model
### 1. Download the benchmark (questions only — no answers in the solver prompt)
```bash
git clone https://github.com/IlkhamFY/spectro-agent.git
cd spectro-agent
pip install -r requirements.txt
```
Questions (blind inputs):
- `data/benchmark_main/questions2.jsonl` (140; use `clean_qids.json` for validated subset)
- `data/benchmark_v3/questions2.jsonl` (40)
- `data/benchmark_v2_ctrl/questions2.jsonl` (20)
Each row: `qid`, `formula`, `ir_bands_cm-1`, `h_nmr`, `c_nmr`. **No structure hints.**
### 2. Run your elucidator
Return up to **three ranked SMILES** per `qid`. Protocol:
- Inputs: formula + IR + ¹H + ¹³C only (as printed in the source paper).
- No web search, no structure hints, no answer-key access.
- Document model version, prompt, and tool access in your submission.
### 3. Score locally
Write predictions as JSONL:
```json
{"qid": "R01", "candidates": ["SMILES_rank1", "SMILES_rank2", "SMILES_rank3"]}
```
```bash
python scripts/score_submission.py --predictions my_run.jsonl --name "YourModel-1.0"
# optional strict stereochemistry scoring:
python scripts/score_submission.py --predictions my_run.jsonl --stereo
```
Reproduce the official headline numbers:
```bash
python scripts/score_main.py
python scripts/forward_verify_all.py
```
### 4. Submit to the leaderboard
Open a GitHub issue or PR on [IlkhamFY/spectro-agent](https://github.com/IlkhamFY/spectro-agent) with:
1. `--name` label for the table
2. `score_submission.py` output (copy-paste)
3. Predictions file (`my_run.jsonl`) or link to reproducible run
4. Model ID, date, and brief protocol note (tools, candidate budget, reasoning tier)
5. Confirmation: blind protocol, no answer-key access
We will verify scoring with `scripts/score_submission.py` before adding a row.
---
## Subsets & extensions
| Benchmark | n | Purpose |
|---|---:|---|
| **IRSpectra-Bench** (main + v3 + v2_ctrl) | 194 | Headline leaderboard |
| IRSpectra-Bench (main clean only) | 134 | Spectrally validated main round |
| IRSpectra-Bench-Electrolyte | 46 | Battery-electrolyte functional classes |
| Cross-vendor arm | 60 | Same compounds, multiple vendors (`docs/CROSS_VENDOR.md`) |
| Model comparison subset | 24 | Claude Haiku → Fable ladder |
---
## Related resources
- **IRexp dataset (training):** https://huggingface.co/datasets/ilkhamfy/IRexp — use `data/train_no_bench.jsonl.gz` to avoid benchmark leakage (`data/irexp_release/README_HF.md`)
- **Cross-vendor protocol:** `docs/CROSS_VENDOR.md`
- **Forward-verification:** `docs/FORWARD_VERIFY.md`
- **Full reproduction:** `README.md` in repository root
---
## Citation
If you use IRSpectra-Bench or report numbers on it, please cite:
```bibtex
@article{yabbarov2026irspectra,
title = {{IRexp} and {IRSpectra-Bench}: redistributable experimental {IR} band lists,
a blind peak-list benchmark, and a recall-bound diagnosis of {LLM} elucidation},
author = {Yabbarov, Ilkham and Sondhi, Rudra and Vargas-Hern{\'a}ndez, Rodrigo A.},
year = {2026},
note = {Manuscript in preparation; target J. Chem. Inf. Model.}
}
```
*Last updated: 2026-08-25 (JCIM editorial strengthen). External submissions listed after verification.*
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