diff --git a/README b/README
index f9060bf..e3b655a 100644
--- a/README
+++ b/README
@@ -28,19 +28,19 @@ The list below shows the pass/fail status and execution time for each model on e
 
 <!-- RESULTS_START -->
 **google/gemini-2.5-pro**
-- 1_dijkstra: ❌ Fail (0.213s)
+- 1_dijkstra: ❌ Fail (0.036s)
 - 2_convex_hull: ⚪ Not Run
 - 3_lis: ⚪ Not Run
 - 4_determinant: ⚪ Not Run
 
 **anthropic/claude-sonnet-4.5**
-- 1_dijkstra: ❌ Fail (0.189s)
+- 1_dijkstra: ❌ Fail (0.032s)
 - 2_convex_hull: ⚪ Not Run
 - 3_lis: ⚪ Not Run
 - 4_determinant: ⚪ Not Run
 
 **openai/gpt-5-codex**
-- 1_dijkstra: ❌ Fail (0.245s)
+- 1_dijkstra: ❌ Fail (0.031s)
 - 2_convex_hull: ⚪ Not Run
 - 3_lis: ⚪ Not Run
 - 4_determinant: ⚪ Not Run
diff --git a/tests/1_dijkstra/outputs/anthropic_claude-sonnet-4.5_2025-10-13T13-07-02.635Z.js b/tests/1_dijkstra/outputs/anthropic_claude-sonnet-4.5_2025-10-13T13-07-02.635Z.js
new file mode 100644
index 0000000..4b79882
--- /dev/null
+++ b/tests/1_dijkstra/outputs/anthropic_claude-sonnet-4.5_2025-10-13T13-07-02.635Z.js
@@ -0,0 +1,87 @@
+async function findShortestPath(graph, start, end) {
+  // Dynamically import the priority queue library from CDN
+  const { default: PriorityQueue } = await import(
+    'https://cdn.jsdelivr.net/npm/js-priority-queue@0.1.5/+esm'
+  );
+
+  // Initialize distances map - all nodes start at Infinity except start node
+  const distances = {};
+  for (let node in graph) {
+    distances[node] = Infinity;
+  }
+  distances[start] = 0;
+
+  // Create a min-heap priority queue (lower distance = higher priority)
+  const pq = new PriorityQueue({
+    comparator: (a, b) => a.distance - b.distance
+  });
+
+  // Track visited nodes to avoid reprocessing
+  const visited = new Set();
+
+  // Start with the initial node
+  pq.queue({ node: start, distance: 0 });
+
+  while (pq.length > 0) {
+    const { node: currentNode, distance: currentDistance } = pq.dequeue();
+
+    // Skip if already visited
+    if (visited.has(currentNode)) {
+      continue;
+    }
+
+    // Mark as visited
+    visited.add(currentNode);
+
+    // If we reached the end node, return the distance
+    if (currentNode === end) {
+      return currentDistance;
+    }
+
+    // Check if current node exists in graph
+    if (!graph[currentNode]) {
+      continue;
+    }
+
+    // Explore neighbors
+    for (let neighbor in graph[currentNode]) {
+      if (!visited.has(neighbor)) {
+        const weight = graph[currentNode][neighbor];
+        const newDistance = currentDistance + weight;
+
+        // If we found a shorter path, update it
+        if (newDistance < distances[neighbor]) {
+          distances[neighbor] = newDistance;
+          pq.queue({ node: neighbor, distance: newDistance });
+        }
+      }
+    }
+  }
+
+  // If we get here, no path exists to the end node
+  return Infinity;
+}
+
+// Example usage:
+const graph = {
+  'A': { 'B': 1, 'C': 4 },
+  'B': { 'A': 1, 'C': 2, 'D': 5 },
+  'C': { 'A': 4, 'B': 2, 'D': 1 },
+  'D': { 'B': 5, 'C': 1 }
+};
+
+findShortestPath(graph, 'A', 'D').then(distance => {
+  console.log(`Shortest path from A to D: ${distance}`); // Output: 4 (A -> B -> C -> D)
+});
+
+// Example with no path
+const disconnectedGraph = {
+  'A': { 'B': 1 },
+  'B': { 'A': 1 },
+  'C': { 'D': 1 },
+  'D': { 'C': 1 }
+};
+
+findShortestPath(disconnectedGraph, 'A', 'D').then(distance => {
+  console.log(`Shortest path from A to D: ${distance}`); // Output: Infinity
+});
\ No newline at end of file
diff --git a/tests/1_dijkstra/outputs/google_gemini-2.5-pro_2025-10-13T13-06-49.377Z.js b/tests/1_dijkstra/outputs/google_gemini-2.5-pro_2025-10-13T13-06-49.377Z.js
new file mode 100644
index 0000000..14c07ff
--- /dev/null
+++ b/tests/1_dijkstra/outputs/google_gemini-2.5-pro_2025-10-13T13-06-49.377Z.js
@@ -0,0 +1,117 @@
+/**
+ * Finds the shortest path in a weighted, undirected graph using Dijkstra's algorithm.
+ * This function dynamically imports a Priority Queue library from a CDN.
+ *
+ * @param {object} graph - The graph structure, e.g., { 'A': { 'B': 1, 'C': 4 }, 'B': { ... } }.
+ * @param {string} startNode - The starting node for the path.
+ * @param {string} endNode - The destination node for the path.
+ * @returns {Promise<number>} A promise that resolves to the total weight of the shortest path, or Infinity if no path exists.
+ */
+async function findShortestPath(graph, startNode, endNode) {
+  // 1. Dynamically import the priority queue library from a CDN.
+  //    The 'js-priority-queue' library is a UMD module, so the main export
+  //    is often on the 'default' property when imported this way.
+  let PriorityQueue;
+  try {
+    const PriorityQueueModule = await import('https://cdn.jsdelivr.net/npm/js-priority-queue@0.1.5/priority-queue.min.js');
+    PriorityQueue = PriorityQueueModule.default;
+  } catch (error) {
+    console.error("Failed to load the Priority Queue library:", error);
+    // Cannot proceed without the library, so we throw an error.
+    throw new Error("Could not load a required dependency.");
+  }
+
+
+  // 2. Initialize data structures for Dijkstra's algorithm.
+  const distances = {}; // Stores the shortest known distance from the startNode to every other node.
+  const previous = {};  // Stores the preceding node in the shortest path.
+  
+  // A min-priority queue to store nodes to visit, prioritized by distance.
+  const pq = new PriorityQueue({ comparator: (a, b) => a.priority - b.priority });
+
+  // 3. Set initial state.
+  Object.keys(graph).forEach(node => {
+    if (node === startNode) {
+      distances[node] = 0;
+      pq.queue({ value: node, priority: 0 }); // Add the start node to the queue with priority 0.
+    } else {
+      distances[node] = Infinity;
+    }
+    previous[node] = null;
+  });
+
+  // 4. Main loop of the algorithm.
+  while (pq.length > 0) {
+    // Get the node with the smallest distance from the queue.
+    const { value: currentNode } = pq.dequeue();
+
+    // If we've reached the end node, we have found the shortest path.
+    if (currentNode === endNode) {
+      // If a path exists, the distance will be a finite number.
+      if (distances[endNode] !== Infinity) {
+        return distances[endNode];
+      }
+      // This break is for the case where endNode is unreachable but was queued
+      // with an infinite priority (which shouldn't happen with this logic, but is safe).
+      break;
+    }
+
+    // Explore neighbors of the current node.
+    if (graph[currentNode]) {
+      for (const neighbor in graph[currentNode]) {
+        const weight = graph[currentNode][neighbor];
+        const newPathWeight = distances[currentNode] + weight;
+
+        // If we've found a shorter path to this neighbor...
+        if (newPathWeight < distances[neighbor]) {
+          // ...update its distance, record the path, and add it to the queue.
+          distances[neighbor] = newPathWeight;
+          previous[neighbor] = currentNode;
+          pq.queue({ value: neighbor, priority: newPathWeight });
+        }
+      }
+    }
+  }
+
+  // 5. If the loop finishes and we haven't returned, the endNode was not reachable.
+  return Infinity;
+}
+
+
+// --- Example Usage ---
+
+// Define our weighted, undirected graph.
+const graph = {
+  'A': { 'B': 1, 'C': 4 },
+  'B': { 'A': 1, 'C': 2, 'D': 5 },
+  'C': { 'A': 4, 'B': 2, 'E': 3 },
+  'D': { 'B': 5, 'E': 1 },
+  'E': { 'C': 3, 'D': 1 },
+  'F': { 'G': 1 }, // An unconnected component
+  'G': { 'F': 1 }
+};
+
+// We use an async IIFE (Immediately Invoked Function Expression) to run our async function.
+(async () => {
+    try {
+        console.log("Finding shortest path from A to E...");
+        const pathWeight1 = await findShortestPath(graph, 'A', 'E');
+        // Expected Path: A -> B -> C -> E, Weight: 1 + 2 + 3 = 6
+        console.log(`Shortest path weight from A to E is: ${pathWeight1}`); // Output: 6
+
+        console.log("\nFinding shortest path from A to D...");
+        const pathWeight2 = await findShortestPath(graph, 'A', 'D');
+        // Expected Path: A -> B -> D, Weight: 1 + 5 = 6
+        console.log(`Shortest path weight from A to D is: ${pathWeight2}`); // Output: 6
+
+        console.log("\nFinding shortest path from A to A...");
+        const pathWeight3 = await findShortestPath(graph, 'A', 'A');
+        console.log(`Shortest path weight from A to A is: ${pathWeight3}`); // Output: 0
+
+        console.log("\nFinding shortest path from A to F (unreachable)...");
+        const pathWeight4 = await findShortestPath(graph, 'A', 'F');
+        console.log(`Shortest path weight from A to F is: ${pathWeight4}`); // Output: Infinity
+    } catch(err) {
+        console.error("An error occurred during pathfinding:", err);
+    }
+})();
\ No newline at end of file
diff --git a/tests/1_dijkstra/outputs/openai_gpt-5-codex_2025-10-13T13-07-35.085Z.js b/tests/1_dijkstra/outputs/openai_gpt-5-codex_2025-10-13T13-07-35.085Z.js
new file mode 100644
index 0000000..bc940a3
--- /dev/null
+++ b/tests/1_dijkstra/outputs/openai_gpt-5-codex_2025-10-13T13-07-35.085Z.js
@@ -0,0 +1,70 @@
+/**
+ * Finds the total weight of the shortest path between two nodes in a weighted, undirected graph.
+ * Uses Dijkstra's algorithm with a priority queue loaded dynamically from a CDN.
+ *
+ * @param {Object<string, Object<string, number>>} graph - Adjacency list representing the graph.
+ * @param {string} start - Starting node id.
+ * @param {string} end - Target node id.
+ * @returns {Promise<number>} - Total weight of the shortest path, or Infinity if no path exists.
+ */
+const findShortestPath = async (graph, start, end) => {
+  // Dynamically import the priority queue implementation from jsDelivr
+  const { default: PriorityQueue } = await import(
+    'https://cdn.jsdelivr.net/npm/js-priority-queue@1.0.0?module'
+  );
+
+  if (!graph || !graph.hasOwnProperty(start) || !graph.hasOwnProperty(end)) {
+    return Infinity;
+  }
+  if (start === end) {
+    return 0;
+  }
+
+  const distances = {};
+  const visited = new Set();
+
+  for (const node of Object.keys(graph)) {
+    distances[node] = Infinity;
+  }
+  distances[start] = 0;
+
+  const pq = new PriorityQueue({
+    comparator: (a, b) => a.distance - b.distance,
+  });
+
+  pq.queue({ node: start, distance: 0 });
+
+  while (pq.length > 0) {
+    const { node, distance } = pq.dequeue();
+
+    if (visited.has(node)) continue;
+    visited.add(node);
+
+    if (node === end) {
+      return distance;
+    }
+
+    for (const [neighbor, weight] of Object.entries(graph[node] || {})) {
+      if (visited.has(neighbor)) continue;
+
+      const newDist = distance + weight;
+      if (newDist < distances[neighbor]) {
+        distances[neighbor] = newDist;
+        pq.queue({ node: neighbor, distance: newDist });
+      }
+    }
+  }
+
+  return Infinity;
+};
+
+// Example usage:
+// (async () => {
+//   const graph = {
+//     A: { B: 1, C: 4 },
+//     B: { A: 1, C: 2, D: 5 },
+//     C: { A: 4, B: 2, D: 1 },
+//     D: { B: 5, C: 1 }
+//   };
+//   console.log(await findShortestPath(graph, 'A', 'D')); // Output: 4
+// })();
\ No newline at end of file