raw image
Summary — new pipeline (LLM + frozen SAM) vs trained NN
| Dataset | LLM pipeline (training-free) | LLM old→new | NN N=10 | N=25 | N=50 | N=100 | Crossover |
|---|---|---|---|---|---|---|---|
| ISIC 2018 | draw → numpy genome (pigment cue); SAM gated OFF | 0.882→0.892 (+0.01) | 0.749 | 0.830 | 0.828 | 0.858 | never (≤100) |
| Kvasir-SEG | draw → frozen SAM decoder (no GT-free cue) | 0.580→0.783 (+0.20) | 0.628 | 0.777 | 0.808 | 0.845 | N≈50 |
| Chest X-ray | CLAHE → draw 2 lungs → SAM per-component → darkness-grow | 0.788→0.899 (+0.11) | 0.925 | 0.943 | 0.950 | 0.956 | N≈10 |
| Spleen | soft-tissue window → relocate → SAM decode | 0.420→0.563 (+0.14) | 0.537 | 0.686 | 0.838 | 0.885 | N≈25 |
All pipelines are training-free (frozen LLM draws + frozen SAM + numpy, ≤8 genome params fit on a few labels — no target-task NN trained). The router picks each domain's path GT-free: cue-rich → numpy genome (ISIC); no boundary cue → frozen SAM decoder (polyp/lung/spleen); SAM never touches a cue-rich genome boundary. Red cells = the training-free LLM matches/beats the trained net at that budget. NN = U-Net/resnet34, ImageNet-pretrained.
ISIC 2018 (dermoscopy)
Dermoscopy
| N | LLM | NN |
|---|---|---|
| 10 | 0.892 | 0.749 |
| 25 | 0.892 | 0.830 |
| 50 | 0.892 | 0.828 |
| 100 | 0.892 | 0.858 |
Cue-rich pigment boundary → the numpy genome recovers it (0.892), beating the trained net at every N≤100. SAM is gated OFF here (it would regress an already-good boundary: 0.887→0.817). The standout positive case.
GTLLMNN @N
✓ Good cases
isic · ISIC_0036159
ground truth
LLM
0.99
NN N=10
0.91
NN N=25
0.91
NN N=50
0.95
NN N=100
0.94
isic · ISIC_0023694
raw image
ground truth
LLM
0.95
NN N=10
0.77
NN N=25
0.92
NN N=50
0.93
NN N=100
0.95
isic · ISIC_0023836
raw image
ground truth
LLM
0.97
NN N=10
0.86
NN N=25
0.96
NN N=50
0.96
NN N=100
0.97
✗ Bad cases
isic · ISIC_0023306
raw image
ground truth
LLM
0.17
NN N=10
0.54
NN N=25
0.30
NN N=50
0.15
NN N=100
0.04
isic · ISIC_0022313
raw image
ground truth
LLM
0.06
NN N=10
0.36
NN N=25
0.35
NN N=50
0.71
NN N=100
0.52
isic · ISIC_0022007
raw image
ground truth
LLM
0.06
NN N=10
0.04
NN N=25
0.13
NN N=50
0.10
NN N=100
0.15
Kvasir-SEG (polyp / endoscopy)
Polyp / endoscopy
| N | LLM | NN |
|---|---|---|
| 10 | 0.783 | 0.628 |
| 25 | 0.783 | 0.777 |
| 50 | 0.783 | 0.808 |
| 100 | 0.783 | 0.845 |
No GT-free boundary cue (polyp ≈ mucosa) → a FROZEN SAM decoder, box-prompted by the LLM draw, recovers the boundary the polygon can't: 0.58 → 0.783, now BEATING the NN @N=25 (0.750), with zero target-task labels.
GTLLMNN @N
✓ Good cases
kvasir · cju7fq7mm2pw508176uk5ugtx
raw image
ground truth
LLM
0.90
NN N=10
0.79
NN N=25
0.82
NN N=50
0.94
NN N=100
0.92
kvasir · cju5o1vu9gz8a0818eyy92bns
raw image
ground truth
LLM
0.87
NN N=10
0.72
NN N=25
0.95
NN N=50
0.93
NN N=100
0.96
kvasir · cju7amjna1ly40871ugiokehb
raw image
ground truth
LLM
0.85
NN N=10
0.69
NN N=25
0.91
NN N=50
0.96
NN N=100
0.96
✗ Bad cases
kvasir · cju3xl264ingx0850rcf0rshj
raw image
ground truth
LLM
0.12
NN N=10
0.72
NN N=25
0.70
NN N=50
0.96
NN N=100
0.95
kvasir · cju5u8gz4kj5b07552e2wpkwp
raw image
ground truth
LLM
0.10
NN N=10
0.13
NN N=25
0.56
NN N=50
0.01
NN N=100
0.42
kvasir · cju884985nlmx0817vzpax3y4
raw image
ground truth
LLM
0.06
NN N=10
0.26
NN N=25
0.55
NN N=50
0.88
NN N=100
0.85
🧪 Skill: gated polyp ROI contrast-enhance new
Bad Kvasir cases mislocate — the draw lands on the wrong region. A GT-free contrast enhancer (specular-inpaint + CLAHE-L + saturation) makes the polyp salient enough to find. It rescues mislocations but distorts already-good boundaries, so it is gated on K-draw self-consistency (applied only to low-confidence draws). Skill
polyp_roi_enhance (b45cd77).| case group | LLM raw | LLM enhanced | Δ |
|---|---|---|---|
| bad | 0.094 | 0.434 | +0.340 |
| good | 0.874 | 0.729 | -0.145 |
| gated (raw on good, enhance on bad) | 0.484 | 0.654 | +0.170 |
GTLLM rawLLM on enhanced view
✗ Bad cases — rescued by enhancement
kvasir · cju3xl264ingx0850rcf0rshj
raw
enhanced view
GT
LLM raw
0.12
LLM enhanced
0.63
kvasir · cju5u8gz4kj5b07552e2wpkwp
raw
enhanced view
GT
LLM raw
0.10
LLM enhanced
0.61
kvasir · cju884985nlmx0817vzpax3y4
raw
enhanced view
GT
LLM raw
0.06
LLM enhanced
0.06
✓ Good cases — enhancement not needed (gated off)
kvasir · cju7fq7mm2pw508176uk5ugtx
raw
enhanced view
GT
LLM raw
0.90
LLM enhanced
0.65
kvasir · cju5o1vu9gz8a0818eyy92bns
raw
enhanced view
GT
LLM raw
0.87
LLM enhanced
0.81
kvasir · cju7amjna1ly40871ugiokehb
raw
enhanced view
GT
LLM raw
0.85
LLM enhanced
0.73
Chest X-ray (lung fields)
Chest X-ray
| N | LLM | NN |
|---|---|---|
| 10 | 0.899 | 0.925 |
| 25 | 0.899 | 0.943 |
| 50 | 0.899 | 0.950 |
| 100 | 0.899 | 0.956 |
Two lung fields → SAM PER-COMPONENT (one box per lung) recovers the precise boundary, then a darkness-grow recovers under-segmentation: 0.79 → 0.899, near the trained NN (0.925), GT-free. The per-component prompt was essential (single-box hurts: 0.72).
GTLLMNN @N
✓ Good cases
lungseg · MCUCXR_0150_1
raw image
ground truth
LLM
0.86
NN N=10
0.89
NN N=25
0.91
NN N=50
0.91
NN N=100
0.93
lungseg · CHNCXR_0330_1
raw image
ground truth
LLM
0.85
NN N=10
0.96
NN N=25
0.97
NN N=50
0.98
NN N=100
0.98
lungseg · CHNCXR_0421_1
raw image
ground truth
LLM
0.85
NN N=10
0.94
NN N=25
0.95
NN N=50
0.96
NN N=100
0.95
✗ Bad cases
lungseg · CHNCXR_0229_0
raw image
ground truth
LLM
0.71
NN N=10
0.91
NN N=25
0.94
NN N=50
0.94
NN N=100
0.94
lungseg · MCUCXR_0055_0
raw image
ground truth
LLM
0.71
NN N=10
0.96
NN N=25
0.97
NN N=50
0.97
NN N=100
0.98
lungseg · CHNCXR_0027_0
raw image
ground truth
LLM
0.70
NN N=10
0.94
NN N=25
0.95
NN N=50
0.95
NN N=100
0.96
🧪 Skill: X-ray CLAHE + non-convex lung trace new
Bad lung draws came out as rounded ovals — the polygon rasterizer already supports non-convex (PIL even-odd fill), so this was a drawing issue, not a skill limit. Fix = the literature-standard CXR CLAHE enhancer (
xray_clahe) + a guide to trace the true shape (concave mediastinal border + costophrenic angle). Net-positive and non-destructive on good cases (lungs are large/consistent) → applied ungated. Part of the new modality→preprocess registry (8b535f1).| case group | LLM raw | LLM enhanced | Δ |
|---|---|---|---|
| bad | 0.707 | 0.762 | +0.055 |
| good | 0.855 | 0.846 | -0.009 |
| all (ungated) | 0.781 | 0.804 | +0.023 |
GTLLM rawLLM on enhanced view
✗ Bad cases — round ovals fixed
lungseg · CHNCXR_0229_0
raw
enhanced view
GT
LLM raw
0.71
LLM enhanced
0.78
lungseg · MCUCXR_0055_0
raw
enhanced view
GT
LLM raw
0.71
LLM enhanced
0.74
lungseg · CHNCXR_0027_0
raw
enhanced view
GT
LLM raw
0.70
LLM enhanced
0.77
✓ Good cases — unaffected
lungseg · MCUCXR_0150_1
raw
enhanced view
GT
LLM raw
0.86
LLM enhanced
0.85
lungseg · CHNCXR_0330_1
raw
enhanced view
GT
LLM raw
0.85
LLM enhanced
0.83
lungseg · CHNCXR_0421_1
raw
enhanced view
GT
LLM raw
0.85
LLM enhanced
0.86
Spleen (abdominal CT)
Abdominal CT
| N | LLM | NN |
|---|---|---|
| 10 | 0.563 | 0.537 |
| 25 | 0.563 | 0.686 |
| 50 | 0.563 | 0.838 |
| 100 | 0.563 | 0.885 |
Small invisible-boundary organ → a soft-tissue window relocates the mislocated draw, then SAM decodes: 0.22 → 0.42 → 0.563 (+SAM). Still below the NN (0.70@25) — the residual bottleneck is LOCALIZATION (SAM is prompted by a weak 0.42 draw), not the boundary.
GTLLMNN @N
✓ Good cases
spleen · spleen_8_z024
raw image
ground truth
LLM
0.76
NN N=10
0.68
NN N=25
0.49
NN N=50
0.91
NN N=100
0.96
spleen · spleen_33_z057
raw image
ground truth
LLM
0.75
NN N=10
0.72
NN N=25
0.70
NN N=50
0.81
NN N=100
0.96
spleen · spleen_6_z098
raw image
ground truth
LLM
0.68
NN N=10
0.71
NN N=25
0.90
NN N=50
0.86
NN N=100
0.96
✗ Bad cases
spleen · spleen_21_z058
raw image
ground truth
LLM
0.07
NN N=10
0.55
NN N=25
0.94
NN N=50
0.85
NN N=100
0.87
spleen · spleen_21_z041
raw image
ground truth
LLM
0.01
NN N=10
0.36
NN N=25
0.52
NN N=50
0.65
NN N=100
0.59
spleen · spleen_40_z068
raw image
ground truth
LLM
0.00
NN N=10
0.70
NN N=25
0.75
NN N=50
0.95
NN N=100
0.96