Sage Sci Perception links scientific encoders to a language model; ECG, sleep and RNA results published
Sage Sci Perception (书生·元征) connects scientific encoders to a language model through trainable projectors. Published results include 97.35% accuracy on BEACON ncRNA RNA family classification and 49.75% Acc@1 on QM9S joint-spectrum structure identification.
Sage Sci Perception (书生·元征) connects scientific encoders to a language model through trainable projectors, according to its Hugging Face page under the internlm organization. The projectors map scientific inputs into the language model's input space, so the model can produce natural-language analysis and structured scientific answers.
Supported inputs
The page lists these modalities:
- Molecular graphs
- Spectra
- Crystal structures
- Protein and nucleic-acid sequences
- Scientific signals
Projectors are organized by language backbone, modality, and task. Each is used with its corresponding scientific encoder and configuration.
Published results
The page reports selected results from task-specific checkpoints, evaluated on generated answers:
| Benchmark | Task | Metric | Score |
|---|---|---|---|
| QM9S | Joint-spectrum structure identification | Acc@1 (%) | 49.75 |
| JARVIS-DFT | Formation-energy prediction | MAD/MAE | 15.03 |
| MPBind, assembly-1 reconstruction | PPI interface sites | Mean per-protein F1 (%) | 61.12 |
| SciTS PHU01 | ECG classification | Macro-F1 (%) | 71.53 |
| SciTS NEU06 | Sleep staging | Macro-F1 (%) | 72.01 |
| BEACON ncRNA | RNA family classification | Accuracy (%) | 97.35 |
These scores are published by the model's authors on the model page.
Availability
The model page shows that no Inference Provider currently deploys it. The page links to a GitHub repository, a framework description and a results section.
What this means
Sage Sci Perception covers molecules, crystals, biological sequences and physiological signals in one projector-based design. The language model receives scientific data through trainable projectors paired with domain encoders. The reported results come from task-specific checkpoints, so each score reflects a projector trained for that task, not a single general checkpoint. Because the metrics span classification, identification and regression tasks, they are not directly comparable to each other.
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