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A-R Behavioral Space: Execution-Level Profiling of Tool-Using Language Model Agents in Organizational Deployment","rule_based_extractor","2026-05-27T16:06:28.692299","2026-05-27T16:06:28.692301507","eventType","PRODUCT_LAUNCH","eventDate","2026-05-26","eventDescription","The A-R Behavioral Space: Execution-Level Profiling of Tool-Using Language Model Agents in Organizational Deployment arXiv:2604.12116v2 Announce Type: replace \nAbstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguistic signaling and executable behavior under varying","impactLevel","MEDIUM","amount","fundingRound","investors","location","sourceArticleId","article_rss_arxiv_org_abs_2604_12116","event_99ee066a","SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking","2026-05-27T15:29:55.413540648","2026-05-27T15:29:55.413543488","OPEN_SOURCE","SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking arXiv:2605.25160v1 Announce Type: new \nAbstract: Mobile GUI agents powered by large language models have progressed rapidly, creating urgent needs for realistic and comprehensive evaluation. Existing benchmarks prioritize reproducibility but are often limited to open-source apps or file-operation tasks for the difficulty of constructing rewards on real applications, leaving a gap between benchmark settings an","article_rss_arxiv_org_abs_2605_25160","event_c7b3c5dd","2026-05-27T15:29:45.22275825","2026-05-27T15:29:45.222760452","event_c7b350e4","MASt3R-Nav: WayPixel Navigation in Relative 3D Maps","2026-05-27T15:17:35.251348081","2026-05-27T15:17:35.251350462","MASt3R-Nav: WayPixel Navigation in Relative 3D Maps arXiv:2605.24111v1 Announce Type: cross \nAbstract: Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent geometry, image- or object-relative topological graphs almost entirely do away with geometric understanding. But, this comes at the cost of navigation capability, often limiting it to merely teach-and-repeat. In this work, we propose a novel map rep","article_rss_arxiv_org_abs_2605_24111","event_c7b3d528","Artifact Correction for Echo-Planar Imaging at Low-Field and Ultra-Low-Field MRI","2026-05-27T13:46:14.951944898","2026-05-27T13:46:14.951946814","Artifact Correction for Echo-Planar Imaging at Low-Field and Ultra-Low-Field MRI arXiv:2605.25589v1 Announce Type: new \nAbstract: Purpose: Echo-planar imaging (EPI) in low-field (LF) and ultra-low-field MRI (ULF) suffers from severe Nyquist ghost artifacts due to odd-even k-space misalignment. This study develops a reference-free artifact correction pipeline that reduces reliance on conventional reference scans while achieving improved ghost suppression. Methods: Starting from the traditional re","article_rss_arxiv_org_abs_2605_25589","event_93c2609e","ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection","2026-05-27T13:33:48.12286379","2026-05-27T13:33:48.1228676","SHUTDOWN","ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection arXiv:2605.25009v1 Announce Type: new \nAbstract: The rapid advancement of generative models has made synthetic images increasingly realistic, challenging reliable detection. Existing methods are often limited to end-to-end classification or monolithic reasoning, and thus fail to model structured forensic reasoning and heterogeneous visual evidence. We revisit synthetic image detection ","LOW","article_rss_arxiv_org_abs_2605_25009","event_c7b3c16b","2026-05-27T13:33:36.402458952","2026-05-27T13:33:36.402462329","event_c7b367e8","Ghosts in the Point Clouds: De-glaring LiDAR in the Transient Domain","2026-05-27T13:27:34.909944854","2026-05-27T13:27:34.909946783","Ghosts in the Point Clouds: De-glaring LiDAR in the Transient Domain arXiv:2605.24753v1 Announce Type: new \nAbstract: Modern LiDARs are rapidly transitioning from bulky, mechanically scanned systems to ultra-compact, low-cost, solid-state arrays. This miniaturization-while enabling scalability, affordability, and camera-like data structures-introduces a new and severe failure mode: internal-multipath glare. When light from a bright or retroreflective surface reflects and scatters within the LiDA","article_rss_arxiv_org_abs_2605_24753","event_c7b363e6","DexSIM: Real-time Dexterous Simulation with Unified Causal Video Diffusion","2026-05-27T13:24:58.250585305","2026-05-27T13:24:58.250587091","DexSIM: Real-time Dexterous Simulation with Unified Causal Video Diffusion arXiv:2605.24630v1 Announce Type: new \nAbstract: Recent progress of video diffusion models have enabled extensive simulation of the physical world. While simulation with hand object interaction has been less explored. We propose DexSIM, a dexterous simulation framework for simulating dexterous manipulation in real-time. While previous works utilizing video diffusion and 3D reconstruction focus on navigation, dexterous man","article_rss_arxiv_org_abs_2605_24630","event_c7b34d7f","Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction","2026-05-27T13:12:01.415517561","2026-05-27T13:12:01.415522412","Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction arXiv:2605.24040v1 Announce Type: new \nAbstract: Cycling delivers significant public-health and environmental benefits, yet its uptake in cities is often limited by perceived safety. When street environments appear unsafe, individuals are less likely to cycle, making perception a key barrier to adoption. Recent work has shown that pairwise comparisons of street-view images provide a scalable way to learn subjective safety judgme","article_rss_arxiv_org_abs_2605_24040","event_c7b3c642","Hide to Guide: Learning via Semantic Masking","2026-05-27T11:58:09.088146912","2026-05-27T11:58:09.088156579","Hide to Guide: Learning via Semantic Masking arXiv:2605.25198v1 Announce Type: cross \nAbstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but its effectiveness is often limited by exploration. For example, models often fail on hard problems, leaving little useful reward signal. External expert traces offer a natural source of guidance, yet they may also expose reward-relevant content along the ","article_rss_arxiv_org_abs_2605_25198","event_2906f468","Revealing the core dimensions underlying representations in brains, behavior and AI","2026-05-28T22:03:28.940933373","2026-05-28T22:03:28.940935671","2026-05-27","Revealing the core dimensions underlying representations in brains, behavior and AI arXiv:2605.26921v1 Announce Type: new \nAbstract: The study of representations is widespread across fields, including neuroscience, psychology, and artificial intelligence. While representations are often studied and compared through similarities between stimuli, current methods provide only limited access to the dimensions that shape these representations and are often limited in interpretability. To overcome the","article_rss_arxiv_org_abs_2605_26921","event_2906de9d","Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation","2026-05-28T21:25:55.898905801","2026-05-28T21:25:55.898908305","Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation arXiv:2605.26382v1 Announce Type: new \nAbstract: Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D encoders tend to lose fine structural cues (small lesions and sharp boundaries) as downsampling repeats across multi-resolution stages. We propose Detail Consistent Distillation (DCD), a stage-w","article_rss_arxiv_org_abs_2605_26382","event_2adabd1f","NSF-SciFy: Mining the NSF Awards Database for Scientific Claims","2026-05-28T20:34:47.134196454","2026-05-28T20:34:47.134199055","NSF-SciFy: Mining the NSF Awards Database for Scientific Claims arXiv:2503.08600v3 Announce Type: replace \nAbstract: We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with 2.8 million claims from 400,000 abstracts spanning all science and mathematics discipli","article_rss_arxiv_org_abs_2503_08600","event_2906de44","In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective","2026-05-28T19:44:55.78915034","2026-05-28T19:44:55.789152462","In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective arXiv:2605.26356v1 Announce Type: new \nAbstract: In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-pass update. Retrieval-augmented generation (RAG) also relies on context, but retrieved documents are usually treated as static evidence rather than signals for adaptation. We study RAG as an in-context optim","article_rss_arxiv_org_abs_2605_26356","event_bf2a89ea","LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction","2026-05-28T19:24:33.34038629","2026-05-28T19:24:33.340388233","LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction arXiv:2603.12647v3 Announce Type: replace \nAbstract: Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scene reconstruction and novel view synthesis. However, most existing methods either rely solely on cameras or use LiDAR only for Gaussian initialization or depth supervision, while the rich scene information contained in point clouds, such as refl","article_rss_arxiv_org_abs_2603_12647","event_bf2a86bf","Early Pruning for Public Transport Routing","2026-05-28T19:24:17.68040275","2026-05-28T19:24:17.6804054","Early Pruning for Public Transport Routing arXiv:2603.12592v4 Announce Type: replace-cross \nAbstract: Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the transfer relaxation phase, especially on dense transfer graphs, when supporting unlimited transfers. This inefficiency arises from iterating over many potential inter-stop connections (walks, bikes, e-scooters, etc.). To maintain acceptable performance, pra","article_rss_arxiv_org_abs_2603_12592","event_8c3b79db","xKV: Cross-Layer KV-Cache Compression via Aligned Singular Vector Extraction","2026-05-28T15:30:10.572796076","2026-05-28T15:30:10.572798362","2026-05-28","xKV: Cross-Layer KV-Cache Compression via Aligned Singular Vector Extraction arXiv:2503.18893v2 Announce Type: replace \nAbstract: Long-context Large Language Models (LLMs) enable powerful applications but incur high memory costs due to the key-value states (KV-Cache). Recent studies attempt to share KV-Cache across layers, but these approaches either require expensive pretraining or rely on per-token cross-layer cosine similarity that is often limited in practice. We show, via Centered Kernel Al","article_rss_arxiv_org_abs_2503_18893","event_8a5a054c","OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models","2026-05-28T15:01:43.723899272","2026-05-28T15:01:43.72390166","OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models arXiv:2605.27916v1 Announce Type: new \nAbstract: The advancement of general medical Multimodal Large Language Models (MLLMs) has shown great potential for building conversational assistants to support clinical diagnosis. However, their adaptation to highly specialized domains such as ophthalmology remains underexplored, primarily due to the scarcity of large-scale, domain-specific instructi","article_rss_arxiv_org_abs_2605_27916","event_ebad787a","FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation","2026-05-30T12:30:26.394872926","2026-05-30T12:30:26.394875514","FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation arXiv:2605.29461v1 Announce Type: new \nAbstract: LLM-conditioned segmentation has recently advanced rapidly by coupling large language models with iterative mask generation frameworks. However, we identify a persistent failure mode in current propose-then-select pipelines. Although high-quality mask candidates are often generated, the final prediction may fail to match the given linguistic condition. This failure arises because ","article_rss_arxiv_org_abs_2605_29461","event_2095427c","HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering","2026-05-30T12:11:23.752790887","2026-05-30T12:11:23.752794208","HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering arXiv:2512.24562v2 Announce Type: replace \nAbstract: Large language models (LLMs) achieve strong question answering (QA) performance but can produce fluent answers unsupported by available evidence. Existing hallucination detectors often rely on external verification, repeated sampling, or test-time judge calls, which can be costly for real-time QA. We propose \\textbf{HaluNet}, a lightweight hallucination risk e","article_rss_arxiv_org_abs_2512_24562","event_ebad8b9d","Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation","2026-05-30T11:57:18.717439103","2026-05-30T11:57:18.717441526","Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation arXiv:2605.29992v1 Announce Type: new \nAbstract: Sentence embeddings are a foundational component for semantic search, clustering, classification, and retrieval-augmented generation. This paper presents embeddingmagibu-200m, a Turkish-focused sentence embedding model that produces 768-dimensional L2-normalized vectors and supports an 8,192-token context window, far exceeding the 512-tok","article_rss_arxiv_org_abs_2605_29992","event_ebad8b5e","Causal Interventions on Continuous Variables: A Case Study on Verb Bias in Steering Vectors for In-Context Learning","2026-05-30T11:57:04.14691856","2026-05-30T11:57:04.146920439","Causal Interventions on Continuous Variables: A Case Study on Verb Bias in Steering Vectors for In-Context Learning arXiv:2605.29971v1 Announce Type: new \nAbstract: Causal interventions in language model representations have largely targeted discrete features, like grammatical number. However, language models must also make use of features that are graded. We introduce a method for causal intervention on continuous variables: given activation vectors paired with a graded target variable, we loca","article_rss_arxiv_org_abs_2605_29971","event_ebad7440","STAMP: Training Explicit Memory for Mobile GUI Agents in Controllable and Scalable Virtual Environments","2026-05-30T11:47:33.678384142","2026-05-30T11:47:33.678387156","STAMP: Training Explicit Memory for Mobile GUI Agents in Controllable and Scalable Virtual Environments arXiv:2605.29324v1 Announce Type: cross \nAbstract: Mobile GUI agents excel at immediate reactive control but frequently fail in realistic, long-horizon tasks that require memory. This failure stems from a fundamental conflict between limited context windows and token-heavy screenshots. To save the limited context, agents must progressively discard older visual history, permanently losing cruci","article_rss_arxiv_org_abs_2605_29324","event_c1152a47","Model Fusion via Retrofitting","2026-05-30T11:19:01.030515886","2026-05-30T11:19:01.030518238","Model Fusion via Retrofitting arXiv:2507.00037v2 Announce Type: replace-cross \nAbstract: Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusio","article_rss_arxiv_org_abs_2507_00037","event_4cf6a3df","Recurrent Structural Policy Gradient for Partially Observable Mean Field Games","2026-05-30T11:08:44.757070009","2026-05-30T11:08:44.757074896","Recurrent Structural Policy Gradient for Partially Observable Mean Field Games arXiv:2602.20141v2 Announce Type: replace \nAbstract: Mean Field Games (MFGs) provide a principled framework for modelling interactions in large population systems. However, algorithmic progress has been limited since model-free methods are high variance and exact methods scale poorly. Recent Hybrid Structural Methods (HSMs) reduce variance while maintaining tractability by leveraging low-dimensional individual state a","article_rss_arxiv_org_abs_2602_20141","event_ebad87c3","LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training","2026-05-30T10:44:16.799049365","2026-05-30T10:44:16.799051381","LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training arXiv:2605.29888v1 Announce Type: cross \nAbstract: Reinforcement learning (RL) post-training has shown to improve reasoning in large language models (LLMs). However, there has been little exploration on the problem of data contamination in RL post-training, potentially undermining generalization and evaluation reliability of the training process itself. Existing detection methods primarily rely on outpu","article_rss_arxiv_org_abs_2605_29888","event_ebad7b82","Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation","2026-05-30T10:33:43.629480039","2026-05-30T10:33:43.629481613","Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation arXiv:2605.29502v1 Announce Type: new \nAbstract: Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but difficult to use with standard supervised fine-tuning. We propose Source-Grounded Semantic Reinforcement Learning (SG-SRL), a resource-utilization framework that converts source-language monolingual data into","article_rss_arxiv_org_abs_2605_29502","event_ebad12c9","Specialty-Specific Medical Language Model for Immune-Mediated Diseases","2026-05-30T10:03:35.71430378","2026-05-30T10:03:35.714306076","Specialty-Specific Medical Language Model for Immune-Mediated Diseases arXiv:2605.28838v1 Announce Type: new \nAbstract: Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Terminology for immune-mediated and infectious diseases is especially inconsistent across sources, which often limits the ability of general-purpose Natural Language Processing (NLP) systems to capture the relevant biomedical concepts ","article_rss_arxiv_org_abs_2605_28838","event_a6cf3022","Semantic Foam: Unifying Spatial and Semantic Scene Decomposition","2026-06-02T12:31:47.967039518","2026-06-02T12:31:47.96704215","2026-06-01","Semantic Foam: Unifying Spatial and Semantic Scene Decomposition arXiv:2604.26262v3 Announce Type: replace \nAbstract: Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics applications has been limited. A major bottleneck is the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While previous research has attempted to imp","article_rss_arxiv_org_abs_2604_26262","event_dbc14073","Bundesrecht: An Open Library and Corpus for German Statutory Reference Processing","2026-06-02T11:35:46.466356864","2026-06-02T11:35:46.466359699","Bundesrecht: An Open Library and Corpus for German Statutory Reference Processing arXiv:2605.31338v1 Announce Type: new \nAbstract: Statutory references are central to legal language understanding, but are difficult to process automatically, as they appear in compact and variable surface forms, may combine multiple targets, use special abbreviations, and often point to lower-level units. Existing tools for German focus either on parsing references from legal documents or accessing statutory text ","article_rss_arxiv_org_abs_2605_31338","event_2ff5f3ac","Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data","2026-06-02T11:00:44.834419692","2026-06-02T11:00:44.834422716","BREAKTHROUGH","Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data arXiv:2601.19936v2 Announce Type: replace-cross \nAbstract: The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data detection a critical challenge. Existing state-of-the-art methods typically rely on token likelihoods, yet they often overlook the gap between the target token and the model's top-1 prediction, as well as local correlatio","HIGH","article_rss_arxiv_org_abs_2601_19936","event_80fdde9","2026-06-02T11:00:31.481475586","2026-06-02T11:00:31.481478744","event_dbc143d3","The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning","2026-06-02T10:33:29.350532457","2026-06-02T10:33:29.350534918","The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning arXiv:2605.31404v1 Announce Type: new \nAbstract: Large Language Model (LLM)-based navigation systems commonly construct explicit spatial representations (e.g., topological graphs, semantic raster maps) and translate them into textual descriptions as LLMs' inputs. However, the linguistic structures of such text-based spatial representations and ","article_rss_arxiv_org_abs_2605_31404","event_dbc0d3ee","Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)","2026-06-02T09:10:34.97555499","2026-06-02T09:10:34.975559114","Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version) arXiv:2605.30563v1 Announce Type: new \nAbstract: Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angelic nondeterminism. This allows for a more compact representation of tasks than traditional formalisms such as STRIPS or SAS+, and supports a wide range of task transformations. However, existing planning approa","article_rss_arxiv_org_abs_2605_30563","event_3d0645a8","From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model","2026-06-03T19:22:11.588157416","2026-06-03T19:22:11.588160226","2026-06-02","From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model arXiv:2605.22671v2 Announce Type: replace \nAbstract: Vision-Language-Action (VLA) models often suffer from performance degradation under distribution shifts, as they struggle to learn generalized behavior representations across varying environments. While existing approaches attempt to construct behavior representations through action-centric latent variables, they are often limited by short-ho","article_rss_arxiv_org_abs_2605_22671","event_71d4f4e2","SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL","2026-06-03T18:15:35.022047212","2026-06-03T18:15:35.022050062","SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL arXiv:2512.04069v2 Announce Type: replace \nAbstract: Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open ","article_rss_arxiv_org_abs_2512_04069","event_71d04e2c","RoboDream: Compositional World Models for Scalable Robot Data Synthesis","2026-06-03T17:43:46.467328858","2026-06-03T17:43:46.467332504","RoboDream: Compositional World Models for Scalable Robot Data Synthesis arXiv:2606.02577v1 Announce Type: cross \nAbstract: Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yiel","article_rss_arxiv_org_abs_2606_02577","event_71d04dab","Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation","2026-06-03T17:06:03.775385189","2026-06-03T17:06:03.775387475","Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation arXiv:2606.02532v1 Announce Type: new \nAbstract: Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generative data augmentation approach using a mask-conditioned latent diffusion model (LDM) for synthesizing rea","article_rss_arxiv_org_abs_2606_02532","event_71d045cd","Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery","2026-06-03T16:55:20.156702102","2026-06-03T16:55:20.156704522","Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery arXiv:2606.02303v1 Announce Type: new \nAbstract: Detecting dead trees in aerial imagery is vital for assessing forest health, especially as tree mortality increases globally due to climate change, but domain variability and scarce labeled data often limit model generalization. This study advances the TreeMort-1T-UNet (Tree Mortality 1-Task U-Net) model, initially trained on Finnish aerial imagery (source domain), by ap","article_rss_arxiv_org_abs_2606_02303","event_71cf6c92","Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders","2026-06-03T15:48:24.161198522","2026-06-03T15:48:24.161200336","Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders arXiv:2606.00746v1 Announce Type: new \nAbstract: Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining. Subquadratic alternatives such as linear attention and state-space models reduce this cost, but often serialize images into 1D token streams and weaken the 2D spatial structure important for vision. Generalized Spatia","article_rss_arxiv_org_abs_2606_00746","event_d32e5aa4","MineDraft: A Framework for Batch Parallel Speculative Decoding","2026-06-03T13:16:46.954501355","2026-06-03T13:16:46.954504092","MineDraft: A Framework for Batch Parallel Speculative Decoding arXiv:2603.18016v2 Announce Type: replace \nAbstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model. However, the performance of standard SD is often limited by the strictly sequential execution of these drafting and verification stages. To address this, this paper proposes MineDraft, a batch parallel s","article_rss_arxiv_org_abs_2603_18016","event_71e47f48","MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration","2026-06-03T12:50:02.980079934","2026-06-03T12:50:02.980082038","MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration arXiv:2512.17605v2 Announce Type: replace \nAbstract: Robust mammography registration is essential for clinically relevant applications like tracking disease progression in breast tissue. However, progress has been limited by the absence of transparent public datasets and reproducible standardized benchmarks. Existing studies are often not directly comparable, as they use private data and inconsiste","article_rss_arxiv_org_abs_2512_17605","event_a8aaca23","Agricultural Landscape Understanding At Country-Scale","2026-06-03T12:26:55.706802055","2026-06-03T12:26:55.706804757","Agricultural Landscape Understanding At Country-Scale arXiv:2411.05359v2 Announce Type: replace \nAbstract: Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management. This requires mapping not just crop fields, but also vital features like trees and water bodies which form an intricate mosaic in complex \\textit{smallholder} systems dominating the Global South. Previous efforts to develop such land use ","article_rss_arxiv_org_abs_2411_05359","event_71cfe911","Why Do Time Series Models Need Long Context Windows?","2026-06-03T11:50:01.602031293","2026-06-03T11:50:01.602034181","Why Do Time Series Models Need Long Context Windows? arXiv:2606.01999v1 Announce Type: cross \nAbstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows. However, the benefit of increasing the window size is often simply attributed to capturing long-range dependencies, and broader discussion on how global forecasting models leverage input observations has been limited. In this paper, we show that forecasting groups of time series i","article_rss_arxiv_org_abs_2606_01999","event_71cf5245","Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis","2026-06-03T10:18:55.715109799","2026-06-03T10:18:55.715114066","Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis arXiv:2606.00040v1 Announce Type: cross \nAbstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-reported scales, lacking insights into how literacy manifests in actual learning processes. This study leverages Learning Analytics (LA) to bridge this gap. We collected interaction l","article_rss_arxiv_org_abs_2606_00040",{"_15":490,"_27":491,"_29":492,"_30":493,"_32":-5,"_33":-5,"_34":35,"_36":494,"_38":-5,"_39":463,"_41":495,"_43":496,"_466":497,"_468":-5,"_469":498,"_471":472,"_473":-5,"_474":-5,"_475":-5,"_476":-5,"_477":499,"_479":-5,"_480":-5,"_481":-5,"_56":492,"_57":-5},{"_15":482,"_27":483,"_29":-5,"_30":484,"_32":-5,"_33":-5,"_34":35,"_36":485,"_38":-5,"_39":463,"_41":486,"_43":487,"_466":488,"_468":-5,"_469":-5,"_471":472,"_473":-5,"_474":-5,"_475":-5,"_476":-5,"_477":-5,"_479":-5,"_480":-5,"_481":-5,"_56":483,"_57":-5},{"_15":458,"_27":459,"_29":460,"_30":461,"_32":-5,"_33":-5,"_34":35,"_36":462,"_38":-5,"_39":463,"_41":464,"_43":465,"_466":467,"_468":-5,"_469":470,"_471":472,"_473":-5,"_474":-5,"_475":-5,"_476":-5,"_477":478,"_479":-5,"_480":-5,"_481":-5,"_56":460,"_57":-5},"company_I19820366","Chinese Academy of Sciences","中国科学院",[459,460],"{\"ror\":\"034t30j35\",\"openalex\":\"I19820366\"}","openalex","2026-05-14T00:31:27.338078372","2026-06-01T04:09:55.440206889","companyType","COMPANY","foundedDate","headquarters","Beijing, China","country","CN","employeeCount","fundingStage","totalFunding","valuation","website","https://www.cas.cn","stockSymbol","uscc","crunchbaseUrl","company_I4210094879","Shandong Institute of Automation",[483,489],"{\"ror\":\"00qdtba35\",\"openalex\":\"I4210094879\"}","2026-05-14T01:04:19.322851188","2026-06-01T06:07:17.364767143","RESEARCH_INSTITUTE","山东省科学院自动化研究所成","company_I157773358","Sun Yat-sen University","中山大学",[491,492],"{\"ror\":\"0064kty71\",\"openalex\":\"I157773358\"}","2026-05-14T00:32:16.223592967","2026-06-01T04:18:31.281743573","UNIVERSITY","Guangzhou, China","https://www.sysu.edu.cn",{"_15":583,"_27":576,"_29":-5,"_30":-5,"_32":-5,"_33":-5,"_34":35,"_36":-5,"_38":578,"_39":579,"_41":584,"_43":585,"_511":-5,"_513":-5,"_514":-5,"_46":-5,"_516":-5,"_108":-5,"_519":-5,"_521":-5,"_50":-5,"_523":-5,"_524":-5,"_526":-5,"_528":-5,"_56":576,"_57":-5},{"_15":575,"_27":576,"_29":-5,"_30":577,"_32":-5,"_33":-5,"_34":35,"_36":-5,"_38":578,"_39":579,"_41":580,"_43":581,"_511":-5,"_513":-5,"_514":-5,"_46":-5,"_516":-5,"_108":-5,"_519":-5,"_521":-5,"_50":-5,"_523":-5,"_524":-5,"_526":-5,"_528":-5,"_56":576,"_57":-5},{"_15":561,"_27":562,"_29":-5,"_30":-5,"_32":-5,"_33":-5,"_34":35,"_36":563,"_38":-5,"_39":463,"_41":564,"_43":565,"_511":566,"_513":-5,"_514":567,"_46":562,"_516":568,"_108":569,"_519":556,"_521":570,"_50":571,"_523":-5,"_524":572,"_526":573,"_528":85,"_56":562,"_57":-5},{"_15":547,"_27":548,"_29":-5,"_30":-5,"_32":-5,"_33":-5,"_34":35,"_36":549,"_38":-5,"_39":463,"_41":550,"_43":551,"_511":552,"_513":-5,"_514":553,"_46":548,"_516":554,"_108":555,"_519":556,"_521":557,"_50":558,"_523":-5,"_524":559,"_526":-5,"_528":85,"_56":548,"_57":-5},{"_15":532,"_27":533,"_29":-5,"_30":-5,"_32":-5,"_33":-5,"_34":35,"_36":534,"_38":-5,"_39":463,"_41":535,"_43":536,"_511":537,"_513":-5,"_514":538,"_46":533,"_516":539,"_108":540,"_519":541,"_521":542,"_50":543,"_523":-5,"_524":544,"_526":-5,"_528":85,"_56":533,"_57":-5},{"_15":506,"_27":507,"_29":-5,"_30":-5,"_32":-5,"_33":-5,"_34":35,"_36":508,"_38":-5,"_39":463,"_41":509,"_43":510,"_511":512,"_513":-5,"_514":515,"_46":507,"_516":517,"_108":518,"_519":520,"_521":-5,"_50":522,"_523":-5,"_524":525,"_526":527,"_528":85,"_56":507,"_57":-5},"paper_W2809251854","Edge Intelligence","{\"openalex\":\"W2809251854\",\"doi\":\"10.1145/3229556.3229562\"}","2026-05-19T21:36:10.284987721","2026-05-19T21:37:12.189563472","doi","10.1145/3229556.3229562","arxivId","openalexId","W2809251854","abstractText","As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance and energy overhead. While offloading DNNs to the cloud for execution suffers unpredictable performance, due to the uncontrolled long wide-area network latency. To address these challenges, in this paper, we propose Edgent, a collaborative and on-demand DNN co-inference framework with device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that adaptively partitions DNN computation between device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. (2) DNN right-sizing that accelerates DNN inference through early-exit at a proper intermediate DNN layer to further reduce the computation latency. The prototype implementation and extensive evaluations based on Raspberry Pi demonstrate Edgent's effectiveness in enabling on-demand low-latency edge intelligence.","2018-08-01","publishedYear",2018,"venue",377,"authors","keywords",[529,530,531],"pdfUrl","https://dl.acm.org/doi/pdf/10.1145/3229556.3229562","topVenue","IoT and Edge/Fog Computing","Advanced Neural Network Applications","Context-Aware Activity Recognition Systems","paper_W3098650625","A Review on State-of-the-Art Power Line Inspection Techniques","{\"openalex\":\"W3098650625\",\"doi\":\"10.1109/tim.2020.3031194\"}","2026-05-19T17:09:30.399403105","2026-05-19T17:11:17.196243944","10.1109/tim.2020.3031194","W3098650625","With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.","2020-10-15",2020,"IEEE Transactions on Instrumentation and Measurement",394,[545,546,530],"Power Line Inspection Robots","Remote Sensing and LiDAR Applications","paper_W2980856918","Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","{\"openalex\":\"W2980856918\",\"doi\":\"10.1109/twc.2019.2946140\"}","2026-05-14T20:02:51.429246588","2026-05-20T20:28:38.211656395","10.1109/twc.2019.2946140","W2980856918","As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What’s worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent , a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in real-world deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regression-based prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent ’s effectiveness in enabling on-demand low-latency edge intelligence.","2019-10-18",2019,"IEEE Transactions on Wireless Communications",844,[529,530,560],"Blockchain Technology Applications and Security","paper_W2950865323","Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing","{\"openalex\":\"W2950865323\",\"doi\":\"10.1109/jproc.2019.2918951\"}","2026-05-14T04:22:15.241805924","2026-05-21T06:20:11.184718077","10.1109/jproc.2019.2918951","W2950865323","With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.","2019-06-12","Proceedings of the IEEE",2118,[529,530,574],"https://ieeexplore.ieee.org/ielx7/5/8789751/08736011.pdf","Privacy-Preserving Technologies in Data","e4e3bcf0-c228-41cd-be62-498c04e6be32","OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization",[582],"{}","graphrag","2026-05-29T09:35:29.364069319","2026-05-29T09:35:29.364073302","OSCAR","4cb8c2f2-cfe0-4324-84fe-09c5f8e83327","2026-05-29T09:34:03.328698504","2026-05-29T09:34:03.328700328",{"_15":587,"_27":588,"_29":-5,"_30":589,"_32":590,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":599,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":588,"_57":590},"tax_ind_ai","人工智能",[604,605],"Artificial Intelligence","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/人工智能","sortOrder","isLeaf","entityCount","children","AI","AI 技术",{"_58":607,"_60":608},[],[609],{"_63":610,"_65":611,"_67":612,"_69":613},1780536127503,{"_72":615,"_74":75,"_76":610,"_77":-5,"_78":79,"_80":79,"_81":79,"_82":-5,"_83":-5,"_84":85,"_34":86,"_87":88},[71,614],"[\"portal\",\"industries\"]","industries",[616,617,618,619,620,621,622,623,624,625,626,627],{"_15":587,"_27":588,"_29":-5,"_30":677,"_32":590,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":599,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":588,"_57":590},{"_15":673,"_27":674,"_29":-5,"_30":-5,"_32":675,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":676,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":674,"_57":675},{"_15":667,"_27":668,"_29":-5,"_30":669,"_32":670,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":671,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":668,"_57":670},{"_15":663,"_27":664,"_29":-5,"_30":-5,"_32":665,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":666,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":664,"_57":665},{"_15":659,"_27":660,"_29":-5,"_30":-5,"_32":661,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":662,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":660,"_57":661},{"_15":653,"_27":654,"_29":-5,"_30":655,"_32":656,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":657,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":654,"_57":656},{"_15":648,"_27":649,"_29":-5,"_30":-5,"_32":650,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":651,"_600":79,"_601":652,"_602":79,"_603":-5,"_56":649,"_57":650},{"_15":644,"_27":645,"_29":-5,"_30":-5,"_32":646,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":647,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":645,"_57":646},{"_15":640,"_27":641,"_29":-5,"_30":-5,"_32":642,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":643,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":641,"_57":642},{"_15":636,"_27":637,"_29":-5,"_30":-5,"_32":638,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":639,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":637,"_57":638},{"_15":632,"_27":633,"_29":-5,"_30":-5,"_32":634,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":635,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":633,"_57":634},{"_15":628,"_27":629,"_29":-5,"_30":-5,"_32":630,"_33":-5,"_34":35,"_36":-5,"_38":-5,"_39":591,"_41":-5,"_43":-5,"_592":593,"_594":-5,"_595":75,"_596":597,"_598":631,"_600":79,"_601":85,"_602":79,"_603":-5,"_56":629,"_57":630},"tax_ind_smartphone","智能手机","Smartphone","科技行业/智能手机","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[658],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[672],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算",[604,605],"env",{"_680":23},"SITE_BASE_URL"]

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