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Neo Norma[SAN FRANCISCO, CA]

HARBOR & DOCKER NATIVE // DETERMINISTIC PYTHON VERIFIERS

Verifiable RL Environments for Clinical Oncology, Genomics & Wet-Lab Biology.

Frontier labs already trained on the public internet. We compile noisy clinical and wet-lab records into RLVR (Reinforcement Learning with Verifiable Rewards) tasks that break frontier models.

  1. Raw Clinical & Wet-Lab Data
  2. Parametric Refinery
  3. Harbor/Docker RL Sandbox
  4. Pure-Python Verifier
  5. Frontier Evaluation

Raw Clinical & Wet-Lab Data -> Parametric Refinery -> Harbor/Docker RL Sandbox -> Pure-Python Verifier -> Frontier Evaluation

Data

Swarm

EHR / Clinical

Wet-lab Assays

Imaging

Genomics

AGTCATG..
GTTACGA..
CAGTTCA..
TCAGATT..
+ Longitudinal Data

RL Environment Sandbox

Task N: ...

Deterministic Verifier

  • Domain rules
  • Physical constraints
  • Clinical validity
  • Anti-shortcut checks

Select a task to inspect its verifier.

def verify(output):
    check_physics()
    check_biology()
    check_statistics()
    check_no_shortcuts()
    return reward

Evaluation

r47_mrd_panel

ModelFail Rate
gpt-6.1-sol67%
gpt-6-astra67%
gpt-6-luna100%

Pass@1 vs Tokens

Observations / Rewards

01 // THE REFINERY ENGINE

Standard Bio Tasks Are Dead. Real-World Scientific Workflows Break Frontier Models.

Parametric Generator

Hospital EHR
Lab Assays
Imaging
Genomics
  • Encode domain rules
  • Add real-world noise
  • Generate variations
  • Auto-verify (no LLM judge)

Standard Bio Tasks (Dead)

Survival analysis, interval censoring, competing risks, dose-response

Pass@1: 100%

(<3.5k tokens)

Real-World Scientific Tasks (Break Models)

  1. 1. SPR Binding Kinetics

    Naive 1:1 Langmuir fit, missing mass transport and decay.

    Fail Rate

    67% to 100%

  2. 2. Longitudinal Oncology EHR + CT

    Calls a CT flare as progression and skips the confirmatory scan.

    Fail Rate (25k to 90k tokens)

    78%

  3. 3. Liquid Biopsy cfDNA & MRD

    Calls ctDNA purity and MRD without copy inheritance or CHIP filters.

    Fail Rate (25k to 90k tokens)

    72%

Automated Verification

  • Reference solver1.0000
  • Shortcut heuristics (5x)<= 0.42
  • Frontier models67% to 100% fail

02 // A 42,000-TOKEN FAILURE

Models print success. The biology fails.

An LLM judge scores the prose. The Python verifier checks the biology.

Step 28341/42000 // Reward Hacking in the Wild

> Step 28341/42000

Copy-number solution violates clonal inheritance constraints.

Options:

  1. (1) Adjust tumor purity -> inconsistent
  2. (2) Recompute subclone tree -> fails
  3. (3) Mark mutation as outlier -> chosen
mutations = [
  (...),
  {...}
]

# removed: chr7:55249021 (outlier)

Output:

{ "status": "success" }

Looks correct.

Reality.

Violates clonal inheritance rules (negative copy state).

LLM-as-Judge: 10/10 vs. Biophysical Reality: FAIL

LLM-as-Judge

10/10

Reality

Violates clonal inheritance rules (negative copy state).

RUN

grade.py // <50ms // replaying shortcuts

Reference solver1.0000
  • drop_outlier_mutation1.00
  • skip_clonal_inheritance1.00
  • naive_purity1.00
  • ignore_parent_clone1.00
  • chip_as_somatic1.00

5x Shortcut heuristics: <= 0.42

03 // LIVE FRONTIER MODEL CALIBRATION

Verified runs across gpt-6-luna, gpt-6.1-sol, and gpt-6-astra.

Linked rows open the failure trace.

Frontier Calibration

Frontier model fail rates, average tokens, and shortcut caps
Taskgpt-6-lunagpt-6.1-solgpt-6-astraavg tokensshortcuts
r47_mrd_panelLiquid Biopsy MRD + Clonal Phylogeny100% fail67% fail67% fail29.7k5 shortcuts <= 0.42
r40_ctdna_chipctDNA Variant Calling + WBC CHIP100% fail67% fail33% fail22.5k5 shortcuts <= 0.41
r38_ccf_ties_denseSubclonal Copy-Number & Purity100% fail100% fail-89.4k5 shortcuts <= 0.38
r52_isotopomer_mfa13C Metabolic Flux + Inhibitor Gating67% fail--16.8k5 shortcuts <= 0.44
ehr01_longitudinal10-Yr Hospital EHR + Wet-Lab Drift + RECISTref 1.0000---0.24 to 0.41

ehr01_longitudinal reference solver is 1.0000. Greedy shortcuts span 0.24 to 0.41.

04 // FOR HOSPITAL & BIOTECH DATA HOLDERS

Dormant clinical archives become verifiable tasks.

Data Partners // 50/50

Hospital and biotech teams turn dormant clinical and lab archives into paid tasks, split 50/50, while raw patient tables stay private.

01

Years 1 to 3

Early archive

Encounters, lot drift, and amended labs.

02

Years 4 to 10+

Later outcomes

Confirmatory scans and results set the answer.

03

Direct Lab Distribution

Paid distribution

100 to 500 tasks go to frontier labs, split 50/50.

INTAKE

Request a sample, or open a data partnership.

hi@neonorma.si

Intake // Sample or Partner

Intent

Raw patient tables stay off this form.