多跳问答里选答案总是被多条轨迹搞晕？STEC用证据压缩把问题简化为候选级比较，四个基准上全面领先，值得读。
STEC提出证据压缩框架，解决开放域多跳问答中多条搜索轨迹带来的最终答案选择困难。框架通过答案级证据压缩将轨迹按答案分组并转换为候选特定证据表示，再通过证据引导答案验证比较这些表示并选出最终答案。在四个多跳问答基准（如HotpotQA、2WikiMultihopQA等）上，STEC总体表现优于现有方法。消融实验证实答案级证据压缩对选择效果有显著贡献。
STEC: Evidence Compression for Deep Search in Open-domain Multi-Hop QA
In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach to knowledge-intensive QA by combining retrieval with reasoning. Existing methods mainly improve open-domain multi-hop QA through reasoning paradigms, retrieval interaction, and search strategy optimization. However, using multiple search trajectories introduces a challenging final answer selection problem. Different trajectories may support different candidates, and the retrieved information can be heterogeneous, redundant, incomplete, or conflicting. Directly comparing raw trajectories exposes the verifier to noisy and unaligned content, while comparing answer strings ignores the evidence supporting each candidate, making reliable final selection difficult. To address this challenge, we propose STEC, an evidence compression framework for final answer selection in multi-hop QA. STEC selects the final answer from the existing candidate set through two mechanisms: (1) Answer-Level Evidence Compression, which groups trajectories by normalized answer identity and converts each answer group into a candidate-specific evidence representation; and (2) Evidence-Guided Answer Verification, which compares these representations and selects the final answer from the candidate set. The design shifts final selection from raw trajectory comparison to candidate-level evidence comparison. We evaluate STEC on four open-domain multi-hop QA benchmarks against representative baselines. Experimental results show that STEC performs best overall among the compared methods, and ablation results provide evidence that answer-level evidence compression contributes to final answer selection.