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Related Articles from SNS
Test-Time Training for Zero-Resource Dense Retrieval Reranking
Announce Type: new Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings. Existing approaches face a fundamental dilemma: cross-encoders deliver strong reranking quality but require costly supervised training and incur high latency, while unsupervised BM25 reranking consistently degrades dense retrieval performance on most of BEIR benchmarks. We propose DART (Dense Adaptive Reranking at Test-time), which resolves this...
CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems
Announce Type: new Abstract: Collaborative dense SLAM is essential for multi-robot teams to achieve scalable and consistent 3D perception across large-scale outdoor environments. Existing systems typically depend on depth sensors, incurring significant payload, power, and calibration costs. Monocular RGB cameras are a lightweight alternative, but collaborative monocular dense SLAM remains difficult due to scale ambiguity, unreliable inter-agent data association, especially in outdoor scenes...
Dense Force Estimation with an Event-based Optical Tactile Sensor
arXiv:2606.09451v1 Announce Type: new Abstract: Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting...
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
Announce Type: replace Abstract: While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle with dense predictions. Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlation attention, (e.g., query-query and...
Pruning and Distilling Mixture-of-Experts into Dense Language Models
arXiv:2605.28207v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) is now the dominant architecture for frontier language models, yet it requires all expert parameters to be loaded in memory, making it less preferable for memory-constrained deployment. Existing compression methods reduce the number of experts but the output remains an MoE model with the same fundamental limitation. We present the first systematic framework for converting a trained MoE into a standard fully dense...
Vision-Based Localization in Dense Urban Environments: A Case Study of an Urban Village in China
Announce Type: new Abstract: Urban villages, the widespread informal settlements which have emerged as a result of rapid urbanization, are now major residential hubs for migrant workers in large cities in China. The dense arrangement of buildings in these areas often leads to unreliable GPS signals, while incomplete mapping data further impairs accurate route planning and navigation. These issues not only hinder everyday mobility but also pose significant challenges for emergency response,...
DenseMLLM: Standard Multimodal LLMs for Dense Prediction
arXiv:2602.14134v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense prediction tasks, such as semantic segmentation and depth estimation, typically necessitates the incorporation of complex, task-specific decoders and other customizations. This architectural fragmentation increases model complexity and deviates from the generalist...
Kinetic Theory for Electronic Transport Properties of Warm Dense Matter: Chapman-Enskog Solution of the Uehling-Uhlenbeck Equation
arXiv:2606.02890v1 Announce Type: new Abstract: A kinetic theory is developed to describe the electrical conductivity, thermal conductivity, and electrothermal coefficients in warm dense plasmas. It models electron degeneracy using the Uehling-Uhlenbeck equation, diffraction by computing scattering cross sections quantum mechanically, and strong coupling by treating the scattering events using the potential of mean force.
Plasma Conductivity from Warm Dense Matter to the Spitzer Limit Using Mean-Force Kinetic Theory
arXiv:2606.02881v1 Announce Type: new Abstract: A theoretical model is developed to compute electronic transport coefficients extending from warm and dense to hot and dilute plasma conditions. This kinetic theory-based approach models strong Coulomb correlations by treating interactions using the potential of mean force, electron degeneracy using the Uehling-Uhlenbeck equation, and diffraction by computing cross sections quantum mechanically. The result provides a fast and accurate means to...
HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation
arXiv:2602.07739v2 Announce Type: replace Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However, natural language exhibits hierarchical structure from broad topics to specific entities that Euclidean embeddings fail to preserve, causing semantically distant documents to appear spuriously similar and increasing hallucination risk. To address these...