State Tracking
Overview
State tracking asks whether a model can keep an accurate account of what has happened and use it to interpret later inputs or choose actions. Approaches include recurrent networks and state-space models that update hidden states, Transformers that infer state from context, and explicit memories, state machines, or structured records that agents update and retrieve. Evaluations range from synthetic counting and permutation tasks to dialogue benchmarks such as MultiWOZ, video entity and event tracking, and long-horizon agent tasks. Explicit state representations can improve reliability, but models still struggle with long sequences, changing or partially observed states, and generalizing learned updates; greater representational power does not ensure that training will discover robust tracking mechanisms.
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