通过轨迹选择提升延迟消歧下的序列预测可靠性
EviTrack: Selection over Sampling for Delayed Disambiguation
- 在推理时维护多条候选轨迹，动态筛选最优路径
- 相同计算预算下，恢复速度比采样基线快得多
- 适合需要高可靠性的实时跟踪与决策场景
序列预测在延迟消歧场景中极具挑战性：早期观测模糊，多个潜在解释长期共存，需积累足够证据后才能确定。基于边缘推断的常规方法在此类场景中表现不佳，或过早压缩不确定性，或在信息到来后难以恢复。我们提出EviTrack，一种测试时推理框架，不依赖边缘状态，而是基于潜在轨迹进行推断。EviTrack维护一组竞争性轨迹假设，并结合证据与似然比进行选择，延迟最终决断直至数据充分支持。为此，我们构建了一个具有已知真实轨迹的可控合成基准，明确体现延迟消歧特性。在相同推理预算下，EviTrack显著优于采样基线，实现更快的后消歧恢复。结果表明，在延迟消歧场景中，适度的轨迹级选择比扩大采样覆盖更有效，凸显‘选择优于采样’是可靠序列推断的关键原则。
原文摘要 · Abstract (English)
Sequential prediction is challenging in regimes of delayed disambiguation, where early observations are ambiguous and multiple latent explanations remain plausible until sufficient evidence accumulates. Standard approaches based on marginal inference struggle in this setting, either collapsing uncertainty prematurely or failing to recover once informative evidence arrives. We introduce EviTrack, a test-time inference framework that operates over latent trajectories rather than marginal states. EviTrack maintains a set of competing trajectory hypotheses and applies evidence- and likelihood-ratio-based selection to delay commitment until supported by data, drawing inspiration from hypothesis management in multiple hypothesis tracking and track-before-detect. To evaluate this setting, we construct a controlled synthetic benchmark with known latent ground truth that explicitly exhibits delayed disambiguation. At matched inference budget, EviTrack substantially outperforms sampling-based baselines, achieving faster post-disambiguation recovery. These results show that, in delayed disambiguation regimes, moderate trajectory-level selection is more effective than increasing sampling coverage, highlighting selection over sampling as a key principle for reliable sequential inference.
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