# covtoken — Research Overview

Label-free rare-structure geometry in frozen self-supervised vision transformers, for medical
imaging. One principle, three papers, four gated programs — all compute run as Hugging Face Jobs,
all experiments reproducible, labels eval-only throughout.

## The principle (one sentence)
**Rare/critical structure lives in the mid-layer *concentration subspace* of self-distilled
foundation models; depth (view-invariance/globalization) erodes it; rank/spanning objectives are
anti-aligned with it; and it is readable, certifiable, and bounded label-free.**

## The three papers
1. **Method** — *Where Lesions Live: Label-Free Mid-Layer Lesion Subspaces for Token-Economical
   Medical Imaging* (`paper/working_draft.md`). The label-free lesion subspace + membership pruning
   + conformal retention certificate + lesion-routed depth, across CT (MedDINOv3) and ultrasound
   (DINOv2). Includes the coverage-floor negative result.
2. **Theory / negative-results** — *Rank-Based Representation Objectives Fail for Rare-Signal
   Retention: A Mechanism and a Predictive Law* (`paper/paper2_rank_objectives_draft.md`). The
   exact closed-form law (gap=(m−r)/m, r\*=m), the `A(rank,SNR)` alignment surface, the
   selection-vs-scaling sharpening. Transferable beyond medicine.
3. **Mechanism / SPIE** — *Where Lesions Live: Mid-Layer Localization in Frozen ViTs, and Why*
   (`paper/paper3_midlayer_draft.md`). Mid-layer localization + label-free tail-gap selector +
   the invariance mechanism + the cross-objective causal-by-comparison result.

## The four programs (gated, HF-Jobs reproducible)
- **v1 (method)** — `gate_reports/` Gates 0–6. Subspace validity, cross-modality localization,
  membership > saliency pruning, coverage-floor NEGATIVE, conformal cert (0.978 ≥ 0.90), routed
  depth (1.6× FLOPs @ 98% sensitivity).
- **v2 (S1–S5)** — `research_v2/`. S1 the closed-form law (✅), S2 mid-layer + label-free selector
  (✅), S4 detection viable (◑), S3 precondition-prediction hard (✗), S5 conformal validity transfers (✅).
- **v3 (F1–F4)** — `research_v3/`. F2a alignment surface (✅ synthetic), F1a selection≠scaling (◑),
  F2b law needs rank-relative-to-background (◑), F3a invariance mechanism (◑), **F3 cross-objective
  decisive** (✅ — erosion across DINOv2/supervised/MedDINOv3; MAE not separable).
- **v4 (G1–G3)** — `research_v4/`. **G1a training-free concentration steering = NEGATIVE** (✅
  honest): globalization is an *entangling* transformation, not a removable nuisance — reinforces
  S2 and sharpens F3. G2 (cross-domain universality) and G3 (deployable tool + benchmark) specced,
  not built.

## Headline numbers (multi-seed where applicable)
- Mid-layer lesion AUROC peak **0.866 ± 0.010** (block 3), final-layer 0.565; label-free tail-gap
  selector regret **0.006**.
- Cross-objective depth-erosion ρ(invariance, AUROC): DINOv2 −0.93, supervised −0.73, MedDINOv3 −0.94.
- The law is **exact** (gap=(m−r)/m, r\*=m, std 0.0 over 40 seeds).
- Coverage-floor ablation: 0.22 (floor) vs 0.82 (membership) small-lesion retention.

## What's honest about this body of work
The clean wins (the law, the mid-layer mechanism + selector, the cross-modality method, the
conformal certificate) are real and reproducible. The deep extensions (steering, concentration-
preserving pretraining via reconstruction) returned **negatives that sharpened the science** rather
than producing new methods — reported as first-class results. The remaining novel lever is G2
(cross-domain universality), a deliberate new project, not a quick eval.

## Concurrent work & positioning
**FINO** (Gardès et al., *Who Needs Labels? Adapting Vision Foundation Models With the Metadata You
Already Have*, arXiv:2606.05107, Meta FAIR, June 2026; code on the DINOv3 FINO branch) adapts vision
FMs to scientific domains label-free via **metadata-guided SSL training**. It is **orthogonal**:
FINO *trains/adapts* a backbone with metadata; covtoken *probes a frozen* backbone with pure token
geometry — no training, no metadata. It addresses none of covtoken's contributions (depth/layer
localization, the rank-vs-concentration law, the conformal certificate, rare-structure focus).
Three implications:
- **Differentiate** in related work: frozen geometric probe + law + certificate, not adaptation.
- **F1b lever — TESTED at adapter scale (G1c), not de-risked.** No FINO checkpoint is released, so we
  trained a label-free metadata-guided adapter (the trained counterpart to G1a). Result: SSL-only
  adaptation *erodes* localizability (globalization, reproduced by a trained objective), and metadata
  guidance *offsets* it directionally (+0.014 mean vs SSL) — but the gap is not CI-significant and it
  does **not recover** the depth-eroded concentration subspace toward the mid-layer (0.866). So at
  adapter scale with anatomical metadata, FINO's lever moves the needle in the right direction but
  does not solve the erosion. F1b would need full-backbone capacity AND lesion-relevant metadata.
- **Complementary experiment, if a FINO checkpoint ships**: run the probe on a *full* FINO-adapted
  backbone (more capacity than our adapter) — the open question G1c could only lower-bound.

## Reproduce
All jobs in `jobs/` (PEP-723 uv scripts) run via `hf jobs uv run --flavor <t4-medium|t4-small|cpu>
--secrets HF_TOKEN -v hf://buckets/Chucks90/eryon-datasets:/mnt <script>`. Specs in
`research_specs/`. Figures: `paper/figures/` via `paper/make_figures.py`.