SEER: Boosting Agentic Reasoning via Entropy-Reducing Evidence Selection

Agentic reasoning has shown great potential for many real-world applications. Its success largely relies on whether limited context windows can contain the evidence needed for subsequent decisions of agents. However, properly selecting the useful evidence remains highly challenging due to the rapidly growing interaction histories. To tackle this problem, we propose **S**electing **E**vidence by **E**ntropy **R**eduction (**SEER**), a novel and theoretically-grounded context management framework to boost the agentic reasoning. We cast the context management rigorously in an Information Bottleneck (IB) formulation, which can well balance the *compactness* and *effectiveness* of contexts. Then, the IB formulation naturally gives rise to SEER, which measures the value of each evidence based on its marginal reduction in the agent's entropy over subsequent environment feedback. By prioritizing high-value evidence, SEER dynamically constructs compact contexts enriched with information that supports subsequent reasoning. Extensive experiments on challenging benchmarks show that SEER significantly outperforms all baselines across multiple model scales and families in terms of accuracy and task completion, improving over the strongest baseline by up to **9.4 points**.