pith:OEHZB7YY
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
A neural network first extracts candidate features to shrink the search space for symbolic regression, recovering more complex equations than direct search.
arxiv:2605.12704 v1 · 2026-05-12 · cs.SC · cs.AI · cs.LG
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Claims
Across five standard benchmarks, FePySR outperforms state-of-the-art methods by achieving higher equation recovery rates. On a set of 75 highly complex synthesized equations, FePySR recovers 36 equations, while producing substantially smaller mean squared errors on the remaining unrecovered cases, with reduced computation time compared to PySR. Applied to ordinary differential equations governing biological systems, FePySR successfully identifies governing equations in 24 out of 100 tests where PySR recovers none.
That observational data can be reliably constrained by the heterogeneous neural network to a set of valid candidate expressions without systematically excluding critical nonlinear modules or introducing many invalid ones that still expand the search space.
FePySR uses a neural network to pre-extract valid features before PySR search, recovering more equations than baselines on benchmarks and identifying governing ODEs in 24 of 100 biological cases where PySR finds none.
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Receipt and verification
| First computed | 2026-05-18T03:09:49.642788Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
710f90ff18418ae05eab8853f1ce908220d967344cd89f2fe0d174887fb028c3
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/OEHZB7YYIGFOAXVLRBJ7DTUQQI \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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