fix: improve CVP paper entry and add missing citations

- Fix factual errors: Kannan runs in n^O(n) (not n^n), Micciancio-Voulgaris
  is deterministic O*(4^n) Voronoi cell (not randomized sieve O*(2^n))
- Add Aggarwal-Dadush-Stephens-Davidowitz 2015 citation for the O*(2^n) bound
- Add BibTeX entries: vanemde1981, kannan1987, micciancio2010, aggarwal2015
- Add lattice visualization figure with basis vectors, target, and closest point
- Add evaluation walkthrough showing distance computation
- Fix Makefile: mkdir -p tests/julia before rust-export
- Regenerate problem_schemas.json and reduction_graph.json

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: GiggleLiu

Makefile

@@ -153,6 +153,7 @@ cli:
 GRAPHS := diamond bull house petersen
 MODES := unweighted weighted triangular
 rust-export:
+	@mkdir -p tests/julia
 	@for graph in $(GRAPHS); do \
 		for mode in $(MODES); do \
 			echo "Exporting $$graph ($$mode)..."; \

docs/paper/reductions.typ

@@ -641,9 +641,41 @@ Integer Linear Programming is a universal modeling framework: virtually every NP
 #problem-def("ClosestVectorProblem")[
   Given a lattice basis $bold(B) in RR^(m times n)$ (columns $bold(b)_1, dots, bold(b)_n in RR^m$ spanning lattice $cal(L)(bold(B)) = {bold(B) bold(x) : bold(x) in ZZ^n}$) and target $bold(t) in RR^m$, find $bold(x) in ZZ^n$ minimizing $norm(bold(B) bold(x) - bold(t))_2$.
 ][
-  The Closest Vector Problem is a fundamental lattice problem, proven NP-hard by van Emde Boas @vanemde1981. CVP plays a central role in lattice-based cryptography and the geometry of numbers. Kannan's algorithm @kannan1987 solves CVP in $O^*(n^n)$ time using the Hermite normal form, later improved to $O^*(2^n)$ via the randomized sieve of Micciancio and Voulgaris @micciancio2010. CVP is closely related to the Shortest Vector Problem (SVP) and integer programming: Lenstra's algorithm for fixed-dimensional ILP @lenstra1983 proceeds via CVP in the dual lattice.
-
-  *Example.* Consider the 2D lattice with basis $bold(b)_1 = (2, 0)^top$, $bold(b)_2 = (1, 2)^top$ and target $bold(t) = (2.8, 1.5)^top$. The lattice points near $bold(t)$ include $bold(B)(1, 0)^top = (2, 0)^top$, $bold(B)(1, 1)^top = (3, 2)^top$, and $bold(B)(0, 1)^top = (1, 2)^top$. The closest is $bold(B)(1, 1)^top = (3, 2)^top$ with distance $norm((0.2, 0.5))_2 approx 0.539$.
+  The Closest Vector Problem is a fundamental lattice problem, proven NP-hard by van Emde Boas @vanemde1981. CVP appears in lattice-based cryptography, coding theory, and integer programming @lenstra1983. Kannan's enumeration algorithm @kannan1987 solves CVP in $n^(O(n))$ time; Micciancio and Voulgaris @micciancio2010 improved this to deterministic $O^*(4^n)$ using Voronoi cell computations, and Aggarwal, Dadush, and Stephens-Davidowitz @aggarwal2015 achieved randomized $O^*(2^n)$.
+
+  *Example.* Consider the 2D lattice with basis $bold(b)_1 = (2, 0)^top$, $bold(b)_2 = (1, 2)^top$ and target $bold(t) = (2.8, 1.5)^top$. The lattice points near $bold(t)$ include $bold(B)(1, 0)^top = (2, 0)^top$, $bold(B)(0, 1)^top = (1, 2)^top$, and $bold(B)(1, 1)^top = (3, 2)^top$. The closest is $bold(B)(1, 1)^top = (3, 2)^top$ with distance $norm(bold(B)(1,1)^top - bold(t))_2 = norm((0.2, 0.5))_2 = sqrt(0.04 + 0.25) approx 0.539$.
+
+  #figure(
+    canvas(length: 0.8cm, {
+      // Lattice points: B*(x1,x2) = x1*(2,0) + x2*(1,2)
+      for x1 in range(0, 3) {
+        for x2 in range(0, 3) {
+          let px = x1 * 2 + x2 * 1
+          let py = x2 * 2
+          let is-closest = (x1 == 1 and x2 == 1)
+          draw.circle(
+            (px, py),
+            radius: if is-closest { 0.15 } else { 0.08 },
+            fill: if is-closest { graph-colors.at(0) } else { luma(180) },
+            stroke: if is-closest { 0.8pt + graph-colors.at(0) } else { 0.4pt + luma(120) },
+          )
+        }
+      }
+      // Target vector
+      draw.circle((2.8, 1.5), radius: 0.1, fill: graph-colors.at(1), stroke: none)
+      draw.content((2.8, 1.05), text(7pt)[$bold(t)$])
+      // Dashed line from target to closest point
+      draw.line((2.8, 1.5), (3, 2), stroke: stroke(paint: graph-colors.at(0), thickness: 0.8pt, dash: "dashed"))
+      // Basis vectors as arrows from origin
+      draw.line((0, 0), (2, 0), mark: (end: ">"), stroke: 0.8pt + luma(100))
+      draw.content((1.0, -0.35), text(7pt)[$bold(b)_1$])
+      draw.line((0, 0), (1, 2), mark: (end: ">"), stroke: 0.8pt + luma(100))
+      draw.content((0.2, 1.1), text(7pt)[$bold(b)_2$])
+      // Label closest point
+      draw.content((3.45, 2.3), text(7pt)[$bold(B)(1,1)^top$])
+    }),
+    caption: [2D lattice with basis $bold(b)_1 = (2, 0)^top$, $bold(b)_2 = (1, 2)^top$. Target $bold(t) = (2.8, 1.5)^top$ (red) and closest lattice point $bold(B)(1,1)^top = (3, 2)^top$ (blue). Distance $norm(bold(B)(1,1)^top - bold(t))_2 approx 0.539$.],
+  ) <fig:cvp-example>
 ]
 
 == Satisfiability Problems

docs/paper/references.bib

@@ -307,6 +307,43 @@ @article{epping2004
   doi     = {10.1016/S0166-218X(03)00442-6}
 }
 
+@techreport{vanemde1981,
+  author      = {Peter van Emde Boas},
+  title       = {Another NP-complete Problem and the Complexity of Computing Short Vectors in a Lattice},
+  institution = {Mathematisch Instituut, Universiteit van Amsterdam},
+  number      = {81-04},
+  year        = {1981}
+}
+
+@article{kannan1987,
+  author  = {Ravi Kannan},
+  title   = {Minkowski's Convex Body Theorem and Integer Programming},
+  journal = {Mathematics of Operations Research},
+  volume  = {12},
+  number  = {3},
+  pages   = {415--440},
+  year    = {1987},
+  doi     = {10.1287/moor.12.3.415}
+}
+
+@inproceedings{micciancio2010,
+  author    = {Daniele Micciancio and Panagiotis Voulgaris},
+  title     = {A Deterministic Single Exponential Time Algorithm for Most Lattice Problems Based on {V}oronoi Cell Computations},
+  booktitle = {Proceedings of the 42nd ACM Symposium on Theory of Computing (STOC)},
+  pages     = {351--358},
+  year      = {2010},
+  doi       = {10.1145/1806689.1806739}
+}
+
+@inproceedings{aggarwal2015,
+  author    = {Divesh Aggarwal and Daniel Dadush and Noah Stephens-Davidowitz},
+  title     = {Solving the Closest Vector Problem in $2^n$ Time -- The Discrete {G}aussian Strikes Again!},
+  booktitle = {Proceedings of the 56th IEEE Symposium on Foundations of Computer Science (FOCS)},
+  pages     = {563--580},
+  year      = {2015},
+  doi       = {10.1109/FOCS.2015.41}
+}
+
 @article{shannon1956,
   author  = {Claude E. Shannon},
   title   = {The zero error capacity of a noisy channel},

docs/src/reductions/problem_schemas.json

@@ -83,6 +83,27 @@
       }
     ]
   },
+  {
+    "name": "ClosestVectorProblem",
+    "description": "Find the closest lattice point to a target vector",
+    "fields": [
+      {
+        "name": "basis",
+        "type_name": "Vec<Vec<T>>",
+        "description": "Basis matrix B as column vectors"
+      },
+      {
+        "name": "target",
+        "type_name": "Vec<f64>",
+        "description": "Target vector t"
+      },
+      {
+        "name": "bounds",
+        "type_name": "Vec<VarBounds>",
+        "description": "Integer bounds per variable"
+      }
+    ]
+  },
   {
     "name": "Factoring",
     "description": "Factor a composite integer into two factors",