position vs FINO (Meta FAIR, arXiv:2606.05107): orthogonal frozen-probe vs metadata adaptation; de-risks F1b; complementary experiment noted

RESEARCH_OVERVIEW.md

@@ -50,6 +50,23 @@ preserving pretraining via reconstruction) returned **negatives that sharpened t
 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.
+- **De-risks F1b**: G1a showed the eroded subspace cannot be recovered *training-free* (globalization
+  is entangling). FINO is an existence proof that *with* a training signal one can "preserve
+  informative, suppress spurious" factors — so the practical concentration-preserving lever is
+  **metadata-guided adaptation**, not steering.
+- **Complementary experiment** (new future direction): run the covtoken probe on a FINO-adapted
+  backbone — does metadata-guided adaptation preserve the mid-layer concentration subspace at depth
+  and raise rare-signal separability? Bridges the two lines and rides their release.
+
 ## 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

paper/working_draft.md

@@ -205,6 +205,18 @@ mechanistic negative result on rank-based coverage objectives. Note (iv) gives c
 from a companion representation-coverage probe study: if such a probe reads final-layer features,
 Finding 1 says it reads the wrong layer — the two results reinforce rather than overlap.
 
+**Relation to label-free FM adaptation (FINO; Gardès et al., 2026).** Concurrent work adapts vision
+foundation models to scientific domains *without task labels* by guiding a self-supervised objective
+with **metadata**, training the backbone. Our work is orthogonal and complementary: we keep the
+backbone **frozen**, use **no metadata or labels**, and contribute a geometric *analysis* (where the
+signal lives), a token *economy* (membership pruning, routed depth), a retention *certificate*, and
+a *law* on objective choice — none of which an adaptation method addresses. The two compose: our
+probe can be run on a FINO-adapted backbone to test whether metadata-guided adaptation preserves the
+mid-layer concentration subspace at depth and improves rare-signal separability (a question our
+training-free steering result, Sec. 6 / companion study, shows cannot be solved without a training
+signal). Their result is also indirect support for our mechanism: counteracting depth-globalization
+of informative local factors is plausibly part of why metadata guidance helps.
+
 ## 8. Limitations
 
 - The method helps only where feature density localizes the lesion (liver = characterized