All entries

Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions

arXiv:2410.08491v2 Announce Type: replace Abstract: Automated vehicles promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains cha

safetyarxiv-cs-ro

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

DGX agent

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

researcharxiv-cs-cv

5 May 2026

EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

model-releasesarxiv-cs-cv

arXiv:2605.02275v1 Announce Type: new Abstract: Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved pe

EditPropBench: Measuring Factual Edit Propagation in Scientific Manuscripts

arXiv:2605.02083v1 Announce Type: new Abstract: Local factual edits in scientific manuscripts often create non-local revision obligations. If a dataset changes from 215 to 80 documents, claims such as

EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

arXiv:2605.01238v1 Announce Type: cross Abstract: Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning e

researcharxiv-cs-cv

Hardware

Effective Capacitance Modeling Using Graph Neural Networks

arXiv:2507.03787v2 Announce Type: replace Abstract: Static timing analysis is a crucial stage in the VLSI design flow that verifies the timing correctness of circuits. Timing analysis depends on the p

hardwarearxiv-cs-lg

Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback

arXiv:2506.18315v2 Announce Type: replace-cross Abstract: LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies h

researcharxiv-cs-ai

Efficient Parameter Estimation of Truncated Boolean Product Distributions

arXiv:2007.02392v3 Announce Type: replace Abstract: We study the problem of estimating the parameters of a Boolean product distribution in d dimensions, when the samples are truncated by a set S subse

Efficient Preference Poisoning Attack on Offline RLHF

arXiv:2605.02495v1 Announce Type: new Abstract: Offline Reinforcement Learning from Human Feedback (RLHF) pipelines such as Direct Preference Optimization (DPO) train on a pre-collected preference dat

Efficient Reasoning with Hidden Thinking

arXiv:2501.19201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Mod

Tutorials

EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer

arXiv:2605.01732v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinde

tutorialsarxiv-cs-cl

Elon: Let’s settle. Greg: Nope. Elon: Ok, let’s talk about your diaries, then.

Elon: Let’s settle. Greg: Nope. Elon: Ok, let’s talk about your diaries, then. On the eve of trial, Elon Musk reached out to Greg Brockman about a potential settlement, according to court documents fi

safetygary-marcus--x

Industry

Elon Musk once revealed in 2019 that only around 5% of SpaceX resources were focused on Starship at that time The other ~95% were running Fa…

Elon Musk once revealed in 2019 that only around 5% of SpaceX resources were focused on Starship at that time The other ~95% were running Falcon 9 and Crew Dragon - one of the most successful orbital

industryelon-musk--x

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders

arXiv:2605.01372v1 Announce Type: new Abstract: Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively en

Embody4D: A Generalist 4D World Model for Embodied AI

arXiv:2605.01799v1 Announce Type: new Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representat

researcharxiv-cs-cv

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

arXiv:2512.11415v2 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of la

applicationsarxiv-cs-lg

EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness

model-releasesarxiv-cs-cv

arXiv:2605.01024v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown prom

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment

model-releasesarxiv-cs-ai

arXiv:2605.00731v1 Announce Type: cross Abstract: While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHG

Local Ai

Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities

arXiv:2605.00960v1 Announce Type: cross Abstract: We introduce energy-based constraint networks -- a modality-agnostic architecture that learns structural coherence from contrastive pairs. The system

local-aiarxiv-cs-cl

ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection

arXiv:2502.06774v4 Announce Type: replace Abstract: Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference a

Enforcing tail calibration when training probabilistic forecast models

arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated

researcharxiv-cs-lg

Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning

arXiv:2605.02073v1 Announce Type: new Abstract: Mathematical reasoning is a key benchmark for large language models. Reinforcement learning is a standard post-training mechanism for improving the reas

Enhancing Game Review Sentiment Classification on Steam Platform with Attention-Based BiLSTM

arXiv:2605.01315v1 Announce Type: new Abstract: This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Usi

researcharxiv-cs-cl

Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization

arXiv:2605.02011v1 Announce Type: new Abstract: Automating the drafting of judgment documents is pivotal to judicial efficiency, yet it remains challenging due to the dual requirements of comprehensiv

Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning

arXiv:2605.02378v1 Announce Type: new Abstract: In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile

researcharxiv-cs-cv

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters

arXiv:2605.02867v1 Announce Type: new Abstract: Despite significant advances in Reinforcement Learning (RL), model performance remains highly sensitive to algorithm and hyperparameter configurations,

applicationsarxiv-cs-lg

Industry

Enterprises are finding that the fastest path to AI efficiency runs through compliance first

Regulated industry AI is evolving beyond compliance foundations to drive greater efficiency gains, reducing routine work and freeing teams to focus on critical, higher-impact decisions. Years ago, Pfi

industrysiliconangle

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins

arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-s

researcharxiv-cs-lg

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

arXiv:2505.01185v3 Announce Type: replace-cross Abstract: Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-station

Agents

Equation-Free Digital Twins for Nonlinear Structural Dynamics

arXiv:2605.00950v1 Announce Type: cross Abstract: Monitoring high-dimensional engineering structures in extreme environments is limited by non-stationary excitation, nonlinear structural kinematics, a

agentsarxiv-cs-lg

Agents

Ergodic Risk Measures: Towards a Risk-Aware Foundation for Continual Reinforcement Learning

arXiv:2510.02945v3 Announce Type: replace Abstract: Continual reinforcement learning (continual RL) seeks to formalize the notions of lifelong learning and endless adaptation in RL. In particular, the

agentsarxiv-cs-lg

ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue

model-releasesarxiv-cs-ro

arXiv:2605.01371v1 Announce Type: new Abstract: The rapid advancement of Multimodal Large Language Models (MLLMs) has empowered Unmanned Aerial Vehicle (UAV) with exceptional capabilities in spatial r

Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks

arXiv:2605.01484v1 Announce Type: new Abstract: With the rapidly improving reasoning abilities of Large Language Models (LLMs), there is also a rising demand to use them in a wide variety of domains.

Evaluating Tabular Representation Learning for Network Intrusion Detection

arXiv:2605.02519v1 Announce Type: new Abstract: Classic Network Intrusion Detection Systems (NIDS) often rely on manual feature engineering to extract meaningful patterns from network traffic data. Ho

EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems

arXiv:2605.00936v1 Announce Type: new Abstract: Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on met

applicationsarxiv-cs-lg

Tools

Every output is traceable. Computer shows where the data came from and links directly to the source behind key numbers. Click any citation o…

Every output is traceable. Computer shows where the data came from and links directly to the source behind key numbers. Click any citation or hyperlinked number to open the underlying SEC filing, earn

toolsperplexity--x

Agents

everyone would have a deeper appreciation for Agent Products that rock because of great Context/Harness Engineering if they… talked to: - LL…

everyone would have a deeper appreciation for Agent Products that rock because of great Context/Harness Engineering if they… talked to: - LLM Base models - Post-Trained models with no harness (no tool

agentsharrison-chase--x

Everything about AI will be contested, because everyone has different interests. So it is and so it will be for so it has been, time out of …

applicationsethan-mollick--x

Mollick argues that AI policy and development will inevitably remain contested territory because different stakeholders—including governments, corporations, researchers, and the public—have fundamenta

Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments

arXiv:2605.01432v1 Announce Type: new Abstract: Autonomous landing in cluttered or unstructured environments remains a safety-critical challenge for unmanned aerial vehicles (UAVs), particularly under

safetyarxiv-cs-ro

Evolutionary BP+OSD Decoding for Low-Latency Quantum Error Correction

arXiv:2512.18273v2 Announce Type: replace-cross Abstract: Quantum error correction (QEC) for fault-tolerant quantum computing requires a balanced decoding solution that offers high performance, low co

researcharxiv-cs-ai

Evolving Token Communication with Parametric Memory Network

model-releasesarxiv-cs-cv

arXiv:2605.01869v1 Announce Type: cross Abstract: Token communication has emerged as a promising framework for efficient wireless transmission by representing source data as compact semantic tokens. H

Exact Higher-Order Derivatives for SE(3) via Analytical/AD Methods

model-releasesarxiv-cs-ro

arXiv:2605.02252v1 Announce Type: new Abstract: Fast prototyping of new SE(3) estimation objectives remains awkward in practice. Modern Lie-group frameworks -- GTSAM, manif, Sophus, SymForce, Ceres --

Exact Loop Controllers for ReLU Realization of Homogeneous Curve Refinements

arXiv:2605.01655v1 Announce Type: cross Abstract: We study homogeneous refinement operators ((Vgamma)(t)=sum_{jinmathbb Z}A_jgamma(Mt-j)), acting on compactly supported continuous piecewise linear cur

researcharxiv-cs-lg

Excited to introduce Gemma 4 Multi-Token Prediction Drafters⚡️Accelerated inference right in your pockets - Up to a 3x speedup - Same qualit…

model-releasesjeremy-howard--x

Excited to introduce Gemma 4 Multi-Token Prediction Drafters⚡️Accelerated inference right in your pockets - Up to a 3x speedup - Same quality guarantees - Available in your favorite open-source tools

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments

arXiv:2605.02165v1 Announce Type: new Abstract: Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generate

safetyarxiv-cs-lg

Tutorials

Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study

arXiv:2510.02729v2 Announce Type: replace Abstract: Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress

tutorialsarxiv-cs-lg

Exploring Data-Free LoRA Transferability for Video Diffusion Models

arXiv:2605.01929v1 Announce Type: new Abstract: Video diffusion models leveraging step distillation or causal distillation have achieved remarkable performance. However, adapting existing LoRAs to the

safetyarxiv-cs-cv

Exploring Entropy-based Active Learning for Fair Brain Segmentation

arXiv:2605.01706v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard

safetyarxiv-cs-cv