This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Symbolic regression that respects physics by baking in known stoichiometry. The idea is sound and the application domain (flux-based systems in chemistry and biology) is real. But it's a narrow tooling contribution. Matters if you're doing systems identification in biology or chemistry; irrelevant if you're not.
The framing is interesting: equations with high numerical fit can be misleading outside observed regimes, so track the derivation not just the result. The method uses LLM agents to perform principled search over scientific investigations. If you're building systems that need to discover equations from data, or if you're automating scientific discovery, this is worth a close read. The technique could transfer to other discovery problems.