The paper argues that error control is as crucial as expressive capacity for state tracking in recurrent models. It proves that affine recurrent networks, including State‑Space Models and Linear Attention, cannot correct errors along state‑separating subspaces once they preserve state representations, leading to finite‑horizon solutions governed by accumulated error. Empirical results on group state‑tracking tasks show that tracking collapses when the distinguishability ratio falls below a readability threshold, and this crossing predicts downstream accuracy failure.
arXiv:2606. 07254v1 Announce Type: new Abstract: State tracking exposes a sharp limitation of sequence models: the relevant signal is often not a summary of observed tokens, but an ordered latent state that evolves through non-commutative transformations.
arXiv:2603. 01959v2 Announce Type: replace Abstract: State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling tasks while remaining efficient and highly-parallelizable.
arXiv:2608. 03425v1 Announce Type: new Abstract: Transformer-based architectures have dominated sequence modeling, largely due to the expressive power of attention mechanisms.
arXiv:2604. 17121v3 Announce Type: replace Abstract: Transformers encode structure in sequences via an expanding contextual history.
arXiv:2602. 14814v3 Announce Type: replace Abstract: Over the last years, state-tracking tasks, particularly permutation composition, have become a testbed to understand the limits of sequence models architectures like Transformers and RNNs (linear and non-linear).
The paper investigates how small action errors evolve when using action chunking in behavioural cloning. By injecting errors at each state and observing their growth under open‑loop (no replanning) and closed‑loop (replanning) regimes, the authors classify states as contracting, expanding, or unresolved. Across twelve manipulation tasks, they find that stable states are rare, error amplification is common, and that short‑horizon fitting can overestimate long‑horizon propagation. Predictors trained on camera and proprioceptive data can recover open‑loop stability but only partially capture closed‑loop dynamics, indicating that standard imitation learning does not reliably produce policies that contract errors when perturbed.
The paper investigates how neural networks, particularly Transformers and recurrent models, learn to track group elements by predicting their running product. It finds that Transformers tend to recover quotient classes and that their accuracy can be predicted by the reciprocal of class size, while recurrent networks can capture both normal and non‑normal right‑coset partitions. The study links partial accuracy, learning stages, and internal state representations to the subgroup cosets the models learn to track.
arXiv:2609.22854v1 Announce Type: cross Abstract: Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually i...
ReWorld-Track introduces a recursive event world model for language‑guided multi‑camera tracking that explicitly carries association uncertainty into future predictions. By treating candidate matches and waiting as alternative target states, the model updates a persistent recurrent belief that preserves uncertainty across successive observations. This approach improves identity continuity and next‑camera accuracy, achieving HOTA scores of 65.19 on CityFlowV2 and 45.36 on MTMMC, and reducing median arrival‑time error from 0.78 s to 0.71 s.
arXiv:2610. 27628v1 Announce Type: new Abstract: Looped language models can run more iterations at test time, but additional iterations need not help them solve longer problems.
arXiv:2609.16054v1 Announce Type: cross Abstract: Transformer-based multi-target tracking (MTT) jointly learns data association and state estimation, but MT3/Track-MT3-style trackers repeatedly re-en...
arXiv:2608.30899v1 Announce Type: new Abstract: Most trajectory forecasting models are trained on clean annotated histories, and are often evaluated under the same idealized assumption, although prac...