EviTrack: Selection over Sampling for Delayed Disambiguation

TL;DR

EviTrack is a novel inference framework that manages multiple hypotheses over latent trajectories, improving accuracy in delayed disambiguation scenarios by delaying commitment until sufficient evidence is available.

Contribution

It introduces a trajectory-based selection method for sequential inference, outperforming sampling approaches in delayed disambiguation tasks.

Findings

EviTrack outperforms sampling baselines in synthetic benchmarks.

It achieves faster recovery after disambiguation.

Moderate trajectory-level selection is more effective than increased sampling.

Abstract

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.