If we have a magic comparator that compares two intervals enough fast, then this is a simple sorting problem with time complexity O(MlgM * ComparatorTime). Can you find such a comparator?

As the weights are exponentially increasing, we can see the maximum matters in most cases, and this is indeed true if two interval are disjoint, because the sum is bounded to [2^Max, 2^Max + 1 - 1]. However, intervals are not disjoint in this case!

We can extend above solution to support query operations for two overlapping intervals. If one interval contains others, then the answer is obvious. So we can assume that two interval A, B are not disjoint, and one does not contain another.

Now, we can make both intervals to be disjoint, by removing their intersections. Now, and are both nonempty and disjoint intervals, and their comparison results are obviously same as comparison of A and B, So we can simply compare their maximum.

With well known RMQ structures such as tournament tree, we can use only O(lgN) operations for interval maximum, which results in O(MlgMlgN) algorithm that is fast enough to solve this problem. With "sparse table", you can use only O(1) operations, which makes the solution even faster — O((N + M)lgN).

One natural strategy is to adapt some existing MST algorithms, and exploit the nature of problem to reduce it's time complexity. There are a lot of well known MST algorithms, such as Jarniks-Prim, or Kruskal. However, both have a serious problem — the problem makes it really hard to retrieve single edge weights. What is the workaround?

While less famous than Jarnik and Kruskal's, there is also an algorithm called Borůvka's Algorithm — the first known algorithm for Minimum Spanning Tree. In Boruvka's algorithm, all we have to do is finding a smallest outgoing edge for each component. As some kind of batch processing is allowed now, it sounds that sweep line can help. Can you see how?

In Borůvka's algorithm, we find a smallest edge leading to other components for each component, and add it to spanning tree. We will instead find a smallest outgoing edge for each node (with different component labels), which we can easily merge for a component.

We will find the smallest outgoing node from 1 to n, in order. As rectangles are events adding constant values in the interval, we can use sweep line approach — the cost of edge outgoing from node i can be maintained in a simple array with O(N) operations per query.

Now, we have to find a node with minimum edge weight, with different component origin, which can be also done in O(N) operations per nodes. Note that Borůvka's algorithm needs careful attention for edges with same costs — If there are ties, picking the one with smallest node number is a good tie-breaking rule. This leads to an O((Q + N)NlgN) time complexity, and O(N) space. It's still too slow, but it's the first algorithm with feasible space complexity.

The bottleneck is in finding minimums, and adding constant value in an interval — which hints toward a segment tree approach. If we were to find a minimum edge weight, without different component restriction, then the problem is a classical lazy propagation — we will omit the details.

To support finding minimum value with different component origin, we can slightly modify the segment tree. For each node, we record two minimums — first one gives minimum value, and second one gives minimum value among the node that have different component than first. This modification still allows lazy propagation and query exactly same as before, so we can do the whole query in O(lgN) time — the complexity is O((Q + N)lg²N). The asymptotics is not huge, but due to the high constant factor of segment tree, the solution doesn't run that fast — that's why we have a huge time limit.