DRSR: Learning Set-Level Deletion Risk for Efficient Long-Horizon Agents

Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Existing compression strategies often score historical units independently, but the risk of deleting several units is generally not determined by their singleton scores: redundant evidence, accumulated small effects, and the information that remains after deletion all matter. We introduce Direct Relational Set-Risk Pruning (DRSR), which formulates agent-history compression as risk-constrained selection over deletion sets. Offline, DRSR constructs fixed-output counterfactual supervision by jointly deleting protocol-valid history Blocks and measuring the change in teacher-forced likelihood of the same recorded next output. A lightweight scorer then predicts set-level harm from online-visible relations between candidate history and the current pre-action state, together with deleted and retained set structure and pairwise interactions. At deployment, DRSR evaluates structurally valid deletion candidates with the lightweight scorer and removes the largest feasible set under recency, protocol, budget, and learned-risk constraints, abstaining when no set meets the risk gate. Across four domains of long horizon agent tasks, DRSR increases mean reward from 0.699 to 0.802 while reducing total model tokens by 20.82%. On the fixed Eval40 comparison, it obtains 0.794 reward at 1.211M tokens per task, using 35.85% fewer tokens than the uncompressed agent. Diagnostic analyses and ablations further show that decision-conditioned relations, retained-context information, pair interactions, and abstention each contribute to reliable pruning.