LeetCode #2425 — MEDIUM

Bitwise XOR of All Pairings

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

You are given two 0-indexed arrays, nums1 and nums2, consisting of non-negative integers. Let there be another array, nums3, which contains the bitwise XOR of all pairings of integers between nums1 and nums2 (every integer in nums1 is paired with every integer in nums2 exactly once).

Return the bitwise XOR of all integers in nums3.

Example 1:

Input: nums1 = [2,1,3], nums2 = [10,2,5,0]
Output: 13
Explanation:
A possible nums3 array is [8,0,7,2,11,3,4,1,9,1,6,3].
The bitwise XOR of all these numbers is 13, so we return 13.

Example 2:

Input: nums1 = [1,2], nums2 = [3,4]
Output: 0
Explanation:
All possible pairs of bitwise XORs are nums1[0] ^ nums2[0], nums1[0] ^ nums2[1], nums1[1] ^ nums2[0],
and nums1[1] ^ nums2[1].
Thus, one possible nums3 array is [2,5,1,6].
2 ^ 5 ^ 1 ^ 6 = 0, so we return 0.

Constraints:

1 <= nums1.length, nums2.length <= 10⁵
0 <= nums1[i], nums2[j] <= 10⁹

Step 01

Brute Force Baseline

Problem summary: You are given two 0-indexed arrays, nums1 and nums2, consisting of non-negative integers. Let there be another array, nums3, which contains the bitwise XOR of all pairings of integers between nums1 and nums2 (every integer in nums1 is paired with every integer in nums2 exactly once). Return the bitwise XOR of all integers in nums3.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Bit Manipulation

Example 1

[2,1,3]
[10,2,5,0]

Example 2

[1,2]
[3,4]

Step 02

Core Insight

What unlocks the optimal approach

Think how the count of each individual integer affects the final answer.
If the length of nums1 is m and the length of nums2 is n, then each number in nums1 is repeated n times and each number in nums2 is repeated m times.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
class Solution {
    public int xorAllNums(int[] nums1, int[] nums2) {
        int ans = 0;
        if (nums2.length % 2 == 1) {
            for (int v : nums1) {
                ans ^= v;
            }
        }
        if (nums1.length % 2 == 1) {
            for (int v : nums2) {
                ans ^= v;
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
func xorAllNums(nums1 []int, nums2 []int) int {
	ans := 0
	if len(nums2)%2 == 1 {
		for _, v := range nums1 {
			ans ^= v
		}
	}
	if len(nums1)%2 == 1 {
		for _, v := range nums2 {
			ans ^= v
		}
	}
	return ans
}

# Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
class Solution:
    def xorAllNums(self, nums1: List[int], nums2: List[int]) -> int:
        ans = 0
        if len(nums2) & 1:
            for v in nums1:
                ans ^= v
        if len(nums1) & 1:
            for v in nums2:
                ans ^= v
        return ans

// Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
// Rust example auto-generated from java reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (java):
// // Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
// class Solution {
//     public int xorAllNums(int[] nums1, int[] nums2) {
//         int ans = 0;
//         if (nums2.length % 2 == 1) {
//             for (int v : nums1) {
//                 ans ^= v;
//             }
//         }
//         if (nums1.length % 2 == 1) {
//             for (int v : nums2) {
//                 ans ^= v;
//             }
//         }
//         return ans;
//     }
// }

// Accepted solution for LeetCode #2425: Bitwise XOR of All Pairings
function xorAllNums(nums1: number[], nums2: number[]): number {
    let ans = 0;
    if (nums2.length % 2 != 0) {
        ans ^= nums1.reduce((a, c) => a ^ c, 0);
    }
    if (nums1.length % 2 != 0) {
        ans ^= nums2.reduce((a, c) => a ^ c, 0);
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

SORT + SCAN

O(n log n) time

O(n) space

Sort the array in O(n log n), then scan for the missing or unique element by comparing adjacent pairs. Sorting requires O(n) auxiliary space (or O(1) with in-place sort but O(n log n) time remains). The sort step dominates.

BIT MANIPULATION

O(n) time

O(1) space

Bitwise operations (AND, OR, XOR, shifts) are O(1) per operation on fixed-width integers. A single pass through the input with bit operations gives O(n) time. The key insight: XOR of a number with itself is 0, which eliminates duplicates without extra space.

Shortcut: Bit operations are O(1). XOR cancels duplicates. Single pass → O(n) time, O(1) space.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.