diff --git a/gaussian_observations_localization_v2.txt b/gaussian_observations_localization_v2.txt
index 32f22fb..20d5957 100644
--- a/gaussian_observations_localization_v2.txt
+++ b/gaussian_observations_localization_v2.txt
@@ -2,20 +2,20 @@ A signal s is a point of the unit sphere S^{d-1} in R^d, fixed before any sampli
 
 The sample horizon T = T(d) is deterministic. Angular accuracy is measured by arccos<s, s-hat> <= epsilon with 0 < epsilon <= 1/10. The main result is that no learner of this kind, with M(d) = o(d^2) bits, succeeds at accuracy epsilon with probability at least 2/3 uniformly over signals using fewer than c d log(1/epsilon) samples; the constant c is absolute and the eventual dimension threshold may depend on the memory sequence. This closes an exact-observation analogue of the memory-sample tradeoffs known for noisy sparse regression and for parity: prior art bounds sample complexity by memory for specific regression and finite-learning problems, whereas the exact Gaussian model removes noise and leaves the geometric cost of localization as the only obstruction.
 
-./steinhardt-duchi-2015.pdf
-./steinhardt-valiant-wager-2016.pdf
-./raz-2016.pdf
-./raz-2017.pdf
-./dagan-kur-shamir-2019.pdf
-./ssv-2019.pdf
+./pdf/steinhardt-duchi-2015.pdf
+./pdf/steinhardt-valiant-wager-2016.pdf
+./pdf/raz-2016.pdf
+./pdf/raz-2017.pdf
+./pdf/dagan-kur-shamir-2019.pdf
+./pdf/ssv-2019.pdf
 
 The proof has one organizing idea. Concentration of the posterior is funded either by a geometric quantity whose increase is billed against information, or by a side-projection certificate the learner never sees. Both funds come from the same exact-label projection identity for independent Gaussian rows. The remaining work is bookkeeping: a chain rule that carefully separates what the learner retains from what the analysis retains, and a decoding argument to turn the information bound into a sample bound.
 
-./austin-2020.pdf
+./pdf/austin-2020.pdf
 
 All logarithms are natural unless an explicit base 2 is written. For probability measures P << Q on a standard Borel space, D(P||Q) = integral of log(dP/dQ) with respect to P; mutual information I(U; W) = D(P_{U,W} || P_U (x) P_W); conditional versions average the corresponding relative entropies against the conditioning variable. For a discrete variable W, H(W) = -sum_w P(W = w) log P(W = w), with 0 log 0 = 0. The chain rule and data processing on standard Borel spaces in the conditional-relative-entropy formulation of Austin are used without further comment; the cited Lemma 3.6 and Sections 3.3-3.4 of that reference contain the exact statements required. Shannon's founding account of entropy and channel capacity is the historical source of these quantities. Watanabe's early treatment of multivariate total correlation is the direct antecedent of the multi-information bookkeeping used later, with the standard-Borel formulation in Austin.
 
-./shannon-1948.pdf
+./pdf/shannon-1948.pdf
 
 The proof proceeds in three movements. First the exact equal-label finite-measure identity is recorded: it is the only external ingredient, and every projection and volume estimate to follow rests on it. Second a dyadic-cell refinement charges the cost of concentration. Third, three localization mechanisms (maximum-score refinement, predecessor-span averaging, and finite-entropy regularization) each give a chain-rule drift. The three combine into the sample lower bound. In all cases, the auxiliary variables introduced during the analysis are not visible to the learner.
 The one external geometric fact used below is the finite measure carried by tuples of signal points that share a common exact label. Fix integers k >= 1 and m >= 1, write t = m + 1, and require k + m <= d - 2. Two base laws are permitted: uniform probability on S^{d-1}, or standard Gaussian probability on R^d; write P for either. For an ordered tuple u = (u_1, ..., u_t) in (R^d)^t set D_u = [u_2 - u_1, ..., u_t - u_1], the d-by-m matrix assembled from these difference columns, let F_u = col(D_u) be their span, and set J(u) = sqrt(det(D_u^T D_u)). Write Gamma_{k,d} for the law of a k-by-d matrix of independent standard Gaussians, and Gamma_{k,d}^{F_u} for the conditional law of such a matrix whose rows are independent standard Gaussians in F_u^perp = col(D_u)^perp; this law is defined whenever rank D_u = m, which holds except on a null set for both base laws.