arXiv AI
Industrial ReMEmbR: Scaling Spatiotemporal Memory to Robot Fleets
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arXiv Computer Vision
4d ago
LVMem: Image-Augmented Memory for Long-Horizon Question Answering
arXiv:2610.27104v1 Announce Type: new Abstract: Reliable language-guided perception is essential for embodied agents, where questions about objects, events, and their locations must be answered from...
arXiv AI
Jul 21
Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering
arXiv:2510. 01483v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memory or explicit spatial representation, all sampled frames must be re-processed for every new query.
arXiv Computer Vision
Oct 2
SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation
arXiv:2610.00686v1 Announce Type: new Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene...
Hugging Face Trending Papers
Jul 22
Memory-Augmented Multimodal Large Language Models for Small Object Understanding in Streaming Aerial Videos
Language-guided aerial perception aims to understand user-specified tiny targets in complex unmanned aerial vehicle (UAV) scenes. In real UAV deployment, the UAV must respond while it flies, so such perception runs in an online streaming manner, where frames arrive sequentially and the model responds to each one without access to future frames.
arXiv AI
Jul 23
Memory-Augmented Multimodal Large Language Models for Small Object Understanding in Streaming Aerial Videos
arXiv:2607. 19857v1 Announce Type: cross Abstract: Language-guided aerial perception aims to understand user-specified tiny targets in complex unmanned aerial vehicle (UAV) scenes.
arXiv AI
2d ago
inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action
The paper introduces inr2action, a memory framework that encodes entire robot episodes as fixed‑size neural implicit representations. Each episode’s video is fitted to a shared, meta‑learned initialization, producing a weight delta that serves as memory. This delta is projected into token embeddings to condition a language model for question answering and a decoder for command regression, enabling recall without retrieving individual frames.