Solutions that work on small test cases may fail due to poor time complexity or missing edge case handling on larger inputs.

If your solution works fine on small test cases but fails on larger ones, it's usually an indication of time complexity issues or unhandled edge cases. Many problems have hidden constraints that only show up when the input size grows significantly, revealing inefficiencies in your algorithm. For instance, a brute-force O(n^2) solution may work for small inputs but will time out for larger datasets. You need to optimize by finding more efficient algorithms, such as switching from O(n^2) to O(n log n). Another reason for failure could be unhandled edge cases like empty inputs, very large or small values, or specific patterns in the input. Thorough testing with a variety of edge cases and rethinking your algorithm’s scalability will help you pass larger test cases.