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pith:AYJA3I6G

pith:2026:AYJA3I6G4TF6ERM6K7OIHCZUX7
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CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Dongxia Wu, Elaine Sui, Emily B. Fox, Emma Lundberg, Serena Yeung-Levy, Shiye Su, Yuhui Zhang

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

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

arxiv: 2603.21743 · arxiv_version: 2603.21743v4 · doi: 10.48550/arxiv.2603.21743 · pith_short_12: AYJA3I6G4TF6 · pith_short_16: AYJA3I6G4TF6ERM6 · pith_short_8: AYJA3I6G
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
{
  "metadata": {
    "abstract_canon_sha256": "028703b9a3764f2b820b079fef07310045ac9e7bb4a525fc7b317459e67392db",
    "cross_cats_sorted": [
      "q-bio.QM"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-23T09:33:18Z",
    "title_canon_sha256": "b3eac08c29a709d337e3e547cb42778e2acb8df80ccda2fe773dfaf5dbb9eb4f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.21743",
    "kind": "arxiv",
    "version": 4
  }
}