All entries

DynGraphAgentBench: A Benchmark for Agentic Lifecycle Control in Dynamic Graph Anomaly Detection

arXiv:2609.33980v1 Announce Type: new Abstract: Dynamic graph anomaly detection requires repeated decisions as graph structure and class prevalence drift, yet detector benchmarks usually score a fixed

Content type

All Blog X Post Paper YouTube Reddit GitHub

Applications

E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding

DGX agent

arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complex

applicationsarxiv-cs-lg

29 Sept 2026

E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding

arXiv:2609.34346v1 Announce Type: new Abstract: Temporally dense optical flow is essential for dynamic perception in immersive VR/AR systems, where rapid head, hand, and object motion must be continuo

applicationsarxiv-cs-cv

E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU

arXiv:2609.35099v1 Announce Type: new Abstract: We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU. Leveraging both optimized

Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-based Robots

arXiv:2412.16633v4 Announce Type: replace Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_to(), a

safetyarxiv-cs-ro

ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiograms

arXiv:2609.33117v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can now interpret a standard ten-second, twelve-lead electrocardiogram (ECG) with clinically grounded, reward-v

ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation

arXiv:2609.34893v1 Announce Type: new Abstract: Learning-based manipulation policies relying on RGB cameras often suffer from degraded observations under extreme exposure. Event cameras mitigate this

Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction

arXiv:2609.34729v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs col

Edge Selection for the Effective use of Piecewise-Constant Distributions as Neural Network Outputs for Event Prediction

arXiv:2507.21616v3 Announce Type: replace Abstract: We study the output representation of a neural network used for next event prediction. We propose partitioning the time axis into a fixed set of int

applicationsarxiv-cs-lg

Tutorials

EdgeCraft: Automated Model Crafting for Edge IoT

arXiv:2609.35167v1 Announce Type: new Abstract: Machine learning (ML) increasingly powers Internet of Things (IoT) applications at the edge. Yet producing a deployable edge ML artifact for a specific

tutorialsarxiv-cs-lg

EdgeVLN: Runtime-Aware Deployment Ready Quantized Vision Language Navigation Model

arXiv:2609.35570v1 Announce Type: cross Abstract: Vision-language navigation (VLN) models perform well but target compute-rich platforms, limiting deployment on memory- and power-constrained robotic e

Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation

arXiv:2609.32360v1 Announce Type: new Abstract: Geospatial simulation of infrastructure interventions requires mobility generators that respond directly to edited maps, yet many data-driven generators

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

arXiv:2609.32930v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices.

EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding

arXiv:2609.33962v1 Announce Type: cross Abstract: Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject

safetyarxiv-cs-lg

EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis

arXiv:2609.31632v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis is evolving from short-segment classification toward long-horizon interpretation that demands iterative evidence a

EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning

arXiv:2503.10439v4 Announce Type: replace Abstract: Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper,

Efficient Bezier Velocity Optimization for Free-Floating Space Manipulators

arXiv:2609.31880v1 Announce Type: new Abstract: We present FAVOR (Free-floating Arm Velocity Optimization with Recursive Sensitivities), a planner for collision-free reaching, tracking, and prescribed

researcharxiv-cs-ro

Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation

arXiv:2609.32929v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient o

Efficient Message Passing for Partial Differential Equation Priors

arXiv:2609.32956v1 Announce Type: new Abstract: Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a

Efficient Support Recovery of Mixtures of Sparse Linear Classifiers with Less Measurements

arXiv:2609.32176v1 Announce Type: new Abstract: The support recovery problem in mixture of linear classifiers intends to identify which features actually matter when data is generated by a mixture of

Hardware

Efficient World Action Model Inference with Adaptive Intermediate States

arXiv:2609.34608v1 Announce Type: new Abstract: World Action Models (WAMs) enable future-aware control by jointly modeling actions and environment dynamics. However, iterative diffusion or flow infere

hardwarearxiv-cs-ro

EfficientAgent: What Makes KV Cache Offloading Work for Concurrent Agents?

arXiv:2609.33762v1 Announce Type: cross Abstract: LLM agents resend their whole conversation on every turn, and most of it was already processed on the previous turn. Serving systems avoid recomputing

safetyarxiv-cs-lg

EffiPair: Improving the Efficiency of LLM-generated Code with Differential Execution Feedback

arXiv:2604.05137v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can generate functionally correct programs that differ substantially in execution efficiency. Existing inference-

Ego-Forge: Text and Geometric-Attention Free Exo-to-Egocentric Video Generation

arXiv:2609.35368v1 Announce Type: new Abstract: Exo-to-egocentric video generation aims to synthesize what a person sees from their own viewpoint given third-person footage and a target head trajector

EgoTSR++: Egocentric Spatiotemporal Reasoning for Task Progress Understanding

arXiv:2609.31751v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in static visual understanding, yet remain unreliable when judging how an egocentric task is progres

Elastic Selective Spectral Hybrids for Train-Once, Export-Many Budgeted Inference

arXiv:2609.32486v1 Announce Type: new Abstract: Deploying a language model under different computing and latency budgets calls for compact models with different quality-cost trade-offs. Towards this e

Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques

arXiv:2609.31804v1 Announce Type: cross Abstract: Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure,

applicationsarxiv-cs-cv

Elicitation and Decision Geometry in Single-Index Bandits

arXiv:2609.35622v1 Announce Type: cross Abstract: We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link. Monotonicity makes the optimal action depend

EliseAI raises $350M to enhance its AI work automation suite

EliseAI, a startup that develops artificial intelligence tools for real-estate companies and healthcare providers, has raised 350 million in funding. Andreessen Horowitz and Bessemer jointly led the r

applicationssiliconangle

Industry

EliseAI, which provides AI tools for health care and housing industries, raised 350M at a 4B valuation, up from 2.2B after raising 250M in August 2025 (Nick Lichtenberg/Fortune)

Nick Lichtenberg / Fortune: EliseAI, which provides AI tools for health care and housing industries, raised 350M at a 4B valuation, up from 2.2B after raising 250M in August 2025 — EliseAI, the artifi

industrytechmeme

Elucidating the Design Space of Regression-based Diffusion Reinforcement Learning

arXiv:2609.33444v1 Announce Type: cross Abstract: A nascent family of methods that forgoes the policy gradient and reweights a supervised regression instead has garnered momentum in reinforcement lear

Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication

arXiv:2609.34373v1 Announce Type: new Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should

safetyarxiv-cs-lg

Tutorials

Emergent One-Third Scaling Law as Attention Tries to Concentrate

arXiv:2609.32100v1 Announce Type: new Abstract: The neural scaling law relating longer training to better performance through a power law is central to today's large language models (LLMs), yet its or

tutorialsarxiv-cs-lg

Agents

EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning

arXiv:2609.35047v1 Announce Type: cross Abstract: A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from

agentsarxiv-cs-lg

End-to-end QP-based policies: A unified perspective on robust control and robot learning

arXiv:2609.31905v1 Announce Type: new Abstract: We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the

safetyarxiv-cs-ro

Endo-TSR: Temporal Spectral Modeling of Appearance and Motion for Endoscopic Reconstruction

arXiv:2609.32399v1 Announce Type: new Abstract: Endoscopic scene reconstruction requires modeling tissue motion and temporal appearance while recovering fine surface detail. Deformable Gaussian models

researcharxiv-cs-cv

Energy-aware frugal Bayesian optimization

arXiv:2609.31638v1 Announce Type: new Abstract: Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It

Energy Vision--Language--Action: A Controlled Multimodal Benchmark for Intent-Conditioned Residential Energy Management

arXiv:2609.31648v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are studied mainly in robotics, where visual observations and language instructions are mapped to physical actions.

ENet-GP: Unified Document Image Restoration

arXiv:2609.33758v1 Announce Type: new Abstract: Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than

applicationsarxiv-cs-cv

EngIntervene: Benchmarking Multimodal Engineering State Understanding and Design Intervention Reasoning

arXiv:2609.33261v1 Announce Type: new Abstract: Multimodal engineering benchmarks largely evaluate static understanding, such as recognizing components, interpreting diagrams, or answering technical q

Enhanced Video Text Editing with Trajectory-Aligned Glyph Rendering

arXiv:2609.34178v1 Announce Type: new Abstract: Video text editing aims to replace or add text in a video while keeping the rest of the video unchanged, which requires the edited text to be correct in

Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling

arXiv:2609.31657v1 Announce Type: new Abstract: Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not we

Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators

arXiv:2609.31633v1 Announce Type: new Abstract: In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turb

Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning

arXiv:2609.31911v1 Announce Type: new Abstract: Medical report generation has made significant progress with the rise of modern vision-language models and the growing availability of large-scale medic

researcharxiv-cs-cv

EntroPack: Fast and Accurate Entropy-Coded Weight Compression at Arbitrary Bitrates

arXiv:2609.34185v1 Announce Type: new Abstract: Weight compression helps large neural networks fit deployment memory budgets, but common fixed-width formats offer only coarse storage choices. Entropy

Environment-Conditioned Tail Reweighting for Invariant Learning under Mixed Shifts

arXiv:2601.22944v3 Announce Type: replace Abstract: Out-of-distribution generalization becomes challenging when spurious correlations vary across environments while difficult or underrepresented sampl

applicationsarxiv-cs-lg

Epistemic Learning from Imprecise Annotation

arXiv:2609.34285v1 Announce Type: new Abstract: Imprecise annotations may support several plausible labelling distributions, yet learning methods often resolve this ambiguity into a single predictive

Agents

EpiTransfer: Sparse, Training-Free Long-Range Depth Estimation from Temporal Monocular Aerial Frames

arXiv:2609.33939v1 Announce Type: cross Abstract: Reliable 3D spatial understanding is essential for autonomous navigation, obstacle avoidance, and scene reconstruction. While state-of-the-art learned

agentsarxiv-cs-cv