{
 "artifacts": null,
 "category": "architecture",
 "description": "Replace a fragile learned similarity score for continuous object coordinates with a projection residual against a learned signal subspace. Candidate coordinates are represented by normalized Fourier or positional feature vectors, and the score is the fraction of feature energy outside the estimated subspace. The score remains useful even when the estimated subspace is adversarially rotated, because the perturbation is controlled directly by a sine-theta distance rather than by assumptions about random noise.",
 "formulas_latex": [
  "$$\\widetilde q(\\bm z)=1-\\|P_{\\widetilde{\\mathcal U}}\\varphi_{\\bm z}\\|_2^2,$$",
  "$$\\|P_{\\widetilde{\\mathcal U}}-P_{\\mathcal U}\\|_2\\leq\\varepsilon_{\\rm sub},\\qquad |\\widetilde q(\\bm z)-q(\\bm z)|\\leq\\varepsilon_{\\rm sub},$$",
  "$$\\varphi_{\\bm z}(\\bm\\omega)=e^{\\mathrm i\\kappa\\bm\\omega\\cdot\\bm z},\\qquad u_m^\\infty(\\bm\\omega)=\\sum_{j=1}^{s}\\alpha_j^{(m)}e^{\\mathrm i\\kappa\\bm\\omega\\cdot\\bm x_j}.$$",
  "$$\\kappa\\delta_X\\gtrsim s^{2/3},$$"
 ],
 "id": 3137,
 "implementation": "Integrate this as a continuous-coordinate localization head on top of a neural encoder. Let the encoder map an input into a matrix \\(Y\\in\\mathbb C^{M\\times B}\\), where \\(M\\) is the number of sampled spatial or angular channels and \\(B\\) is the number of examples or views. Compute the rank-\\(s\\) left singular vectors \\(U_s\\) of \\(Y\\), optionally stopping gradients through the SVD during the first experiment, and define \\(P=U_sU_s^*\\). Use a coordinate feature map with entries \\(\\varphi_z[m]=\\exp(i\\kappa\\omega_m^\\top z)/\\sqrt M\\), or replace it by a learned normalized Fourier-feature map. For every candidate coordinate, compute \\(q(z)=1-\\|U_s^*\\varphi_z\\|_2^2\\). Train the encoder with a ranking loss forcing ground-truth coordinates to have lower \\(q\\) than negatives, and add augmentation that randomly rotates the estimated subspace. In pseudocode: `Y=encoder(x); U=top_s_svd(Y); phi=exp(1j*kappa*Omega@z)/sqrt(M); q=1-squared_norm(U.conj().T@phi); loss=hinge(q(z_pos)+margin-q(z_neg))`. Estimate subspace uncertainty from two independently augmented batches using \\(\\|UU^*-VV^*\\|_2\\), approximated by power iteration; the paper supplies the projector-residual construction and perturbation interpretation, while the margin and rank are hyperparameters. Start with a small synthetic 2D or 3D point-source dataset, a four-layer MLP encoder, and 64 Fourier channels. Compare against cosine similarity and an attention coordinate head under feature corruption and adversarial subspace rotation. Success means lower localization error and graceful degradation proportional to the measured projector distance at equal encoder size.",
 "math_summary": "The paper defines the spherical MUSIC objective \\(\\widetilde q(\\bm z)=1-\\|P_{\\widetilde{\\mathcal U}}\\varphi_{\\bm z}\\|_2^2\\), where \\(\\varphi_{\\bm z}\\) is the normalized steering or feature vector for coordinate \\(\\bm z\\), \\(\\mathcal U\\) is the ideal \\(s\\)-dimensional signal subspace, \\(\\widetilde{\\mathcal U}\\) is its estimated version, and \\(P_{\\widetilde{\\mathcal U}}\\) is the orthogonal projector onto the estimate. The perturbation size is measured by the sine-theta distance, operationalized as \\(\\|P_{\\widetilde{\\mathcal U}}-P_{\\mathcal U}\\|_2\\leq\\varepsilon_{\\rm sub}\\). Since \\(\\|\\varphi_{\\bm z}\\|_2=1\\), the score perturbation obeys \\(|\\widetilde q(\\bm z)-q(\\bm z)|\\leq\\varepsilon_{\\rm sub}\\), where \\(q(\\bm z)=1-\\|P_{\\mathcal U}\\varphi_{\\bm z}\\|_2^2\\). The paper proves that, under separation and frame-conditioning hypotheses, the perturbed objective has one strongly convex well near each true point and a uniform value gap outside those wells.",
 "math_tags": [
  "linear-algebra",
  "spectral-theory",
  "geometry",
  "harmonic-analysis"
 ],
 "ml_areas": [
  "embedding",
  "graph-nn",
  "training"
 ],
 "paper": {
  "arxiv_id": "2609.03264",
  "arxiv_url": "https://arxiv.org/abs/2609.03264",
  "summary_what_math_gives_to_ml": "The paper develops a robust subspace-matching objective whose minima remain localized under deterministic adversarial perturbations of the estimated signal subspace. Its transferable asset is not spherical MUSIC itself, but the combination of projection-residual scoring, explicit sine-theta robustness, strongly convex local basins, and a fixed step size determined by the spatial bandwidth. This suggests neural modules that localize continuous latent objects from a learned low-rank feature subspace, while providing an adversarially stable score and a cheap coarse-to-fine refinement procedure. The guarantees are most relevant when a network produces Fourier-like or positional embeddings and the downstream task is continuous localization, slot extraction, or source separation.",
  "title": "Certified Spherical MUSIC for 3D Localization under Adversarial Subspace Perturbations",
  "year": "2026"
 },
 "ratings": {
  "difficulty": 5,
  "novelty": 7,
  "usefulness": 7
 },
 "solves": [
  "stability",
  "accuracy",
  "sample-efficiency"
 ],
 "title": "Adversarial Subspace Residual Localizer",
 "url": "https://synthcore.org/idea/3137/adversarial-subspace-residual-localizer",
 "verification": {
  "peer_reviewed": false,
  "status": "unverified",
  "status_label": "Unverified",
  "verdict_source": "deterministic test code (paired-seed permutation statistics)",
  "verification_axes": {
   "benchmark_mechanism": {
    "confirmed": null,
    "tested": false
   },
   "practical_benchmark": {
    "beats_baseline": null,
    "tested": false
   },
   "toy_mechanism_gate": {
    "confirmed": null,
    "tested": false
   }
  }
 }
}