We consider a special block-structured integer program with $n$ blocks. Every block has a prescribed number of vectors that need to be selected while minimizing the objective $c^{\intercal}x + g(Ax)$, where $c$ is a rational cost vector, $A$ is an integral matrix with $d$ rows, and $g \colon \mathbb{Z}^d \to \mathbb{Q}\cup \{+\infty\}$ is an arbitrary function. We access $g$ via an oracle that minimizes $c^{\intercal}x + g(Ax)$ when all but $O(d)$ variables are fixed. Our algorithm runs in time $(ndΔ)^{O(d)}(Q + \text{poly}(|I|))$ where $Δ$ is the largest absolute value of an entry in $A$, $Q$ is the necessary time to evaluate the oracle for $g$ and $|I|$ is the size of the input. This model is a variation of the model studied by Dadush et al.~[IPCO 23]. Their model has only a single block but individual upper and lower bounds on the variables instead of prescribed block sums. For their model they present an algorithm with running time $t^d(dΔ)^{O(d^2)}Q$ where $t$ is the number of variables. We use our algorithm to solve mixed-integer linear programs with no upper bounds on the variables, a linear objective, and arbitrarily many constraints involving only fractional variables in time $(mΔ)^{O(m)}\text{poly}(|I|)$, where $m$ is the number of constraints involving integral variables and $Δ$ is the largest absolute value of coefficients of integral variables. This improves the dependence on $m$ in the exponent to asymptotically match the dependency in the integer setting. Further, we use our algorithm to generalize the parameterized algorithm for scheduling on uniformly related machines by Rohwedder~[ICALP 25] to obtain an algorithm for multiple knapsack with running time $w_{\max}^{O(d)}\text{poly}(|I|)$ where $w_{\max}$ is the maximum weight of an item and $d$ the number of different weights.

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