**Solution 1:**

Use a priority queue to simulate the process. Let us refer to an instance of multiple farmers finishing at the same time as an **event**. For each event, store the set of farmers that finished. For now, assign the next few cows to those farmers in an arbitrary order. From this, we can determine the time of Bessie's interview.

Once we simulate the process with $N$ cows, the next farmer that finishes will be Bessie's interviewer: call him farmer $x$. Now, consider what happens if we iterate through the **events** in reverse chronological order.

If farmer $x$ is in one of these events, then any farmer who finished at the same time as him could be Bessie's interviewer. In fact, as we move backward through time, whenever the set of Bessie's possible interviewers and the set of farmers in an **event** intersect, any of the farmers in that **event** could end up being Bessie's interviewer.

Thus, the algorithm is as follows:

- Simulate the process forward through time, storing **events** as they occur. Define $E_i$ to be the set of farmers in the $i$-th event.
- After all $N$ cows have been processed, create a set $S$ of Bessie's possible interviewers. Initially, $S = \{x\}$.
- Process the events backward through time, and if $S \cap E_i \neq \varnothing$, then update $S = S \cup E_i$.

This solution works in $O(N \log K)$.

Avnith's C++ code:

#include <bits/stdc++.h> using namespace std; using ll = long long; using pl = pair<ll, int>; #define f first #define s second int main() { int n, k; cin >> n >> k; vector<ll> t(n); for (int i = 0; i < n; i++) cin >> t[i]; priority_queue<pl, vector<pl>, greater<pl>> pq; for (int i = 0; i < k; i++) pq.push({t[i], i}); int cur_cow = k; pl last; vector<vector<int>> events; while (true) { vector<pl> ev; do { ev.push_back(pq.top()); pq.pop(); } while (!pq.empty() && pq.top().f == ev[0].f); if (ev.size() > 1) { vector<int> farmers; for (pl e : ev) farmers.push_back(e.s); events.push_back(farmers); } if (cur_cow + ev.size() > n) { last = ev[0]; break; } for (pl e : ev) { pq.push({e.f + t[cur_cow], e.s}); cur_cow++; } } cout << last.f << "\n"; vector<bool> can_interview(k, false); can_interview[last.s] = true; for (int i = (int)events.size()-1; i >= 0; i--) { bool intersect = false; for (int j : events[i]) { if (can_interview[j]) { intersect = true; break; } } if (intersect) { for (int j : events[i]) { can_interview[j] = true; } } } vector<int> ans; for (int i = 0; i < k; i++) { cout << can_interview[i] << " "; } cout << "\n"; }

**Solution 2:**

This is similar to solution 1, but we do not explicitly keep track of events. Instead, we construct a directed graph with a vertex for each time and a directed edge from each cow's interview start time to that cow's interview end time. Then the $i$th farmer can interview Bessie if and only if there is a path from $t_i$ to Bessie's start time in this graph. We can check for which times such a path exists by running a breadth-first search (or depth-first search) from Bessie's start time.

Ben's Python code:

import heapq N, K = map(int, input().split()) T = list(map(int, input().split())) times = [0] * K edges_into = dict() for t in T: start = heapq.heappop(times) end = start + t edges_into.setdefault(end, []).append(start) heapq.heappush(times, end) q = [heapq.heappop(times)] vis = set() for x in q: if x in vis: continue vis.add(x) if x not in edges_into: continue for prv in edges_into[x]: q.append(prv) print(q[0]) print("".join(["1" if t in vis else "0" for t in T[:K]]))

**Extension:** Suppose every cow chooses one of the available farmers
uniformly at random. Then compute for each farmer the probability that Bessie
will be interviewed by him, modulo $10^9+7$.