pith:AYJA3I6G
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Reinforcement learning post-training with biological rewards improves virtual cell generators to respect physical and biological rules.
arxiv:2603.21743 v4 · 2026-03-23 · cs.LG · q-bio.QM
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\pithnumber{AYJA3I6G4TF6ERM6K7OIHCZUX7}
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Record completeness
Claims
CellFluxRL consistently improves over CellFlux across all rewards, with further performance boosts from test-time scaling, advancing beyond visually realistic generations towards biologically meaningful ones.
The seven reward functions accurately capture biologically meaningful constraints without introducing unintended biases or allowing the model to game the rewards while still violating real cellular physics.
CellFluxRL post-trains the CellFlux generative model with reinforcement learning driven by biologically meaningful reward functions, yielding virtual cell images that better satisfy physical and biological constraints than the base model.
Receipt and verification
| First computed | 2026-05-22T01:03:18.898926Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
06120da3c6e4cbe2459e57dc838b34bfdc31b25819a3f25c5d243b3f206600c3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/AYJA3I6G4TF6ERM6K7OIHCZUX7 \
| 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())"
# expect: 06120da3c6e4cbe2459e57dc838b34bfdc31b25819a3f25c5d243b3f206600c3
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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