arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long...
arXiv:2606.20092v3 Announce Type: replace Abstract: Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-...
arXiv:2609.05533v1 Announce Type: cross Abstract: Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earl...
arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between...
LookStep is a new end‑to‑end framework for Vision‑Language Navigation that integrates Language‑Centric Future State Modeling with an Event‑Driven Rolling Memory. It uses language labels to predict coarse navigation progress and future states for candidate actions, and autonomously decides which observations to store in a bounded memory with semantic roles. Empirical results show that LookStep outperforms existing methods on VLN‑CE tasks, achieving a 49.7% success rate on R2R‑CE Val‑Unseen while improving memory efficiency and reducing data requirements.
The paper introduces RePlanMem-VLA, a selective visual memory system for Vision‑Language‑Action models that uses a policy’s own plan revisions as an online memory‑write signal. By detecting unusually large disagreements between temporally aligned action predictions, the method identifies task‑relevant events and updates a bounded visual memory while suppressing redundant frames. Experiments on MIKASA‑Robo‑VLA tasks and real‑robot replay data show that RePlanMem‑VLA retains essential observations in every episode and achieves high ROC‑AUC scores for event detection.
arXiv:2609.23601v1 Announce Type: new Abstract: Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens,...
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...
VisCache introduces a two-stage, plug‑and‑play framework for pruning visual key‑value caches in Vision Large Language Models without retraining. The first stage filters out temporally redundant keyframes, while the second stage, PruneKV, applies a parabolic layer‑wise budget and asymmetric update to selectively prune keys and fuse values, preserving essential context. Experiments show up to 2.35× speedup and significant memory savings with only 19–28% of the original cache retained, outperforming existing baselines.
arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past...
arXiv:2510. 09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage.
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.