Lightweight Metadata
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MLSkip: Data Skipping for ML Filters via Lightweight Metadata
arXiv:2606.03946v1 Announce Type: new Abstract: Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type.
Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals
arXiv:2606.02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids. It introduces a preliminary continuous-token model built around a one-level Haar DWT/IDWT frontend, a shared coefficient-token layout, optional structural metadata, lightweight modality value adapters, and a shared token-wise encoder-decoder trunk. On Speech Commands, EuroSAT RGB, and...
SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference
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Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives
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NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion
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BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection
arXiv:2606.05586v1 Announce Type: new Abstract: In remote sensing object detection, Convolutional Neural Networks (CNNs) excel at capturing local details while Vision Transformers (ViTs) are better at global context modeling. However, existing detectors typically rely on a single fixed backbone or a manually designed hybrid architecture, and thus fail to adaptively exploit these complementary strengths across inputs of diverse complexity.
TinyContainer: Container Runtime Middleware Enabling Multi-tenant Microcontrollers with Built-in Security
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Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
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PoCQ: Proof of Contribution Quality as a Lightweight Blockchain Consensus for Secure Federated Learning
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Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have
arXiv:2606.05107v1 Announce Type: new Abstract: We propose a label-free approach to adapt powerful but generic vision foundation models to specialized scientific domains. Standard supervised fine-tuning is often ill-suited to these settings: labels are scarce, and task-specific training can collapse the model's generality and hurt robustness. We instead leverage metadata to adapt representations to new domains in a self-supervised manner.