Scientific data is a mix of text, tables, formulas, code, images, and uncertain measurements. Nuances are easily lost.
🟢 How scientific LLMs evolve through richer data and closed loops with autonomous agents?
Analyzed 270 datasets and 190 benchmarks and gave 94-page review.
- a unified taxonomy of scientific data
- a multilayer model of scientific knowledge: from raw observations to theory
This framework helps build pretraining and fine-tuning so that models retain scientific rules and can connect different formats and scales.
The review classifies models by fields: physics, chemistry, biology, materials, earth sciences, astronomy, plus universal scientific assistants.
In quality assessment, there is a shift: from one-shot quizzes to process-oriented checks that evaluate reasoning chains, tool use, and intermediate results.
The authors promote a closed loop: agents plan experiments, run simulators or labs, verify results, and update collective knowledge.
Scientific LLMs are moving toward a data-driven, process-verified, agent-loop approach linked to real evidence.
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🤖 Data Science, ML & Big Data with @DataXplore