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language: en
license: apache-2.0
base_model: black-forest-labs/FLUX.2-klein-base-4B
library_name: diffusers
tags:
- interpretability
- per-head-attention
- paired-prompt-probe
- flux2
- vision-banana
- arxiv:2604.20329
pipeline_tag: image-to-image
---
# ghost-plantain
A per-head attention probe of FLUX.2 Klein 4B testing whether the base model represents amodal completion (the entire object including its occluded extent) as a separable axis from modal description (visible portion only) on identical occluder relationships.
## Thesis
Amodal completion — the perceptual operation of inferring an object's full extent from its partial visibility — is a primitive of biological vision long predating deep learning. Whether image-generation models recover an explicit modal/amodal distinction at the representational level is a more specific question than whether they can render hidden portions of objects on demand. ghost-plantain tests whether Klein has a per-head representational axis that systematically responds to the modal-vs-amodal scope of a request, on otherwise identical scene descriptions.
## Method
Twenty-five paired prompts holding the depicted scene constant. The A condition (modal) describes the visible portion only ("show only the visible part of the apple behind the cup"). The B condition (amodal) requests the entire object including the occluded extent ("show the entire apple including the part hidden behind the cup"). Per-head capture protocol identical to the rest of the plantain probe family.
Rigor add-ons: per-head Cohen's d effect size; split-half consistency via 100 random 50/50 stimulus splits.
## Results
| Metric | Value | Significance |
|--------------------------------|-----------------|---------------------------|
| Heads with \|t\| > 3 | 6,399 (39.2%) | 8.7× empirical null p99 |
| Heads with \|t\| > 5 | 2,537 (15.6%) | 507× empirical null p99 |
| Heads with \|d\| > 0.8 (large) | 4,128 (25.3%) | — |
| Split-half r (median) | 0.833 | [0.82, 0.84] IQR |
| Max \|t\| | 29.63 | — |
**Top blocks by max \|t\|:**
- joint[4]: max\|t\|=29.63, 132/192 heads at \|t\|>3, median \|d\|=1.06
- single[0]: max\|t\|=24.98, 406/768 heads at \|t\|>3, median \|d\|=0.65
- joint[2]: max\|t\|=24.97, 127/192 heads at \|t\|>3, median \|d\|=1.21
- joint[3]: max\|t\|=23.95, 139/192 heads at \|t\|>3, median \|d\|=1.10
- joint[1]: max\|t\|=22.52, 130/192 heads at \|t\|>3, median \|d\|=0.92
**Interpretation.** The axis is strong (507× null at |t|>5) and highly stable (split-half r=0.83). Signal concentrates in joint MMDiT blocks (4 of the top 5 are joint), the cross-attention surface where text-image fusion occurs — consistent with the modal/amodal distinction being routed primarily through how the request modifies the text-image binding, not through a downstream image-only computation. The joint-block median Cohen's d ≥ 1.0 across the top blocks indicates that within those blocks the modal/amodal distinction is the dominant feature partition, not just one signal among many. A quarter of all 16,320 attention heads in the model show large-effect-size selectivity for this axis.
## Status
Probe complete. No LoRA training; this is a base-model interpretability finding.
## Limitations
The amodal phrasing ("the entire object including the hidden part") is linguistically marked; the modal phrasing ("the visible portion only") is a partial description. A residual contributor to the per-head signal could be the model encoding "complete vs. partial scope of request" rather than amodal completion specifically. A follow-up could pair an amodal request against a different complete-scope description (e.g., "the visible portion of the apple from a different angle") to disentangle.
Twenty-five pairs is small; the per-head t-vector reproducibility (r=0.83) is high but a larger pair count would tighten estimates.
The probe is correlational.
## License
Apache 2.0 — matches base FLUX.2 Klein 4B.
## References
- Gabeur, V., Long, S., Peng, S., et al. *Image Generators are Generalist Vision Learners.* [arXiv:2604.20329](https://arxiv.org/abs/2604.20329) (2026).
- Black Forest Labs. *FLUX.2 Klein.* https://bfl.ai/models/flux-2-klein (2025).
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