Search: “framework”

DynaWeb: Model-Based Reinforcement Learning of Web Agents

arXiv:2601.22149v2 Announce Type: replace Abstract: The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step tow

safetyarxiv-cs-cl

E2E-GMNER: End-to-End Generative Grounded Multimodal Named Entity Recognition

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2604.17319v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to jointly identify named entity mentions in text, predict their semantic types, and ground

researcharxiv-cs-cl

21 Apr 2026

EAST: Early Action Prediction Sampling Strategy with Token Masking

arXiv:2604.18367v1 Announce Type: new Abstract: Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We in

EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation

arXiv:2604.18220v1 Announce Type: cross Abstract: Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit thei

researcharxiv-cs-lg

Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos

arXiv:2604.17749v1 Announce Type: new Abstract: Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric

Applications

EmbodiTTA: Resource-Efficient Test-Time Adaptation for Embodied Visual Systems

arXiv:2505.00986v2 Announce Type: replace-cross Abstract: Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy

applicationsarxiv-cs-cv

Emergency Stopping for Liquid-manipulating Robots

arXiv:2604.16667v1 Announce Type: new Abstract: Manipulating open liquid containers is challenging because liquids are highly sensitive to vessel accelerations and jerks. Although spill-free liquid ma

safetyarxiv-cs-ro

Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection

arXiv:2602.16161v3 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction. We present Emotion Collider (EC-Net), a hyperbo

Hardware

Enabling AI ASICs for Zero Knowledge Proof

arXiv:2604.17808v1 Announce Type: cross Abstract: Zero-knowledge proof (ZKP) provers remain costly because multi-scalar multiplication (MSM) and number-theoretic transforms (NTTs) dominate runtime as

hardwarearxiv-cs-cl

Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors

arXiv:2601.05508v2 Announce Type: replace-cross Abstract: Hieroglyphs, as logographic writing systems, encode rich semantic and cultural information within their internal structural composition. Yet,

Enhancing classification accuracy through chaos

arXiv:2603.15299v2 Announce Type: replace Abstract: We propose a novel approach which exploits chaos to enhance classification accuracy. Specifically, the available data that need to be classified are

researcharxiv-cs-lg

Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting

arXiv:2604.18075v1 Announce Type: new Abstract: We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge us

Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

arXiv:2604.18336v1 Announce Type: cross Abstract: Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth A

ENTIRE: Learning-based Volume Rendering Time Prediction

arXiv:2501.12119v3 Announce Type: replace-cross Abstract: We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs

arXiv:2510.00861v2 Announce Type: replace Abstract: While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limite

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

model-releasesarxiv-cs-cl

arXiv:2410.04509v3 Announce Type: replace Abstract: As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particular

Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings

arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog

Estimating Commonsense Plausibility through Semantic Shifts

arXiv:2502.13464v2 Announce Type: replace Abstract: Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or

Evaluating the Impact of Verbal Multiword Expressions on Machine Translation

arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component

EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations

arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr

safetyarxiv-cs-cv

EvoComp: Learning Visual Token Compression for Multimodal Large Language Models via Semantic-Guided Evolutionary Labeling

arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie

Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution

arXiv:2512.11108v3 Announce Type: replace Abstract: Good quality explanations strengthen the understanding of language models and data. Feature attribution methods, such as Integrated Gradient, are a

safetyarxiv-cs-cl

Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection

arXiv:2604.17879v1 Announce Type: new Abstract: Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current

Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness

safetyarxiv-cs-lg

Agents

Federation over Text: Insight Sharing for Multi-Agent Reasoning

arXiv:2604.16778v1 Announce Type: new Abstract: LLM-powered agents often reason from scratch when presented with a new problem instance and lack automatic mechanisms to transfer learned skills to othe

agentsarxiv-cs-lg

Fisher Decorator: Refining Flow Policy via A Local Transport Map

arXiv:2604.17919v1 Announce Type: new Abstract: Recent advances in flow-based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However

safetyarxiv-cs-lg

FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching

arXiv:2604.17720v1 Announce Type: cross Abstract: Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sa

Tutorials

FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation

arXiv:2604.17625v1 Announce Type: new Abstract: This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-tra

tutorialsarxiv-cs-cv

Local Ai

FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models

arXiv:2604.16448v1 Announce Type: cross Abstract: As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged

local-aiarxiv-cs-lg

FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts

arXiv:2603.19857v2 Announce Type: replace-cross Abstract: Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

model-releasesarxiv-cs-lg

arXiv:2604.18379v1 Announce Type: new Abstract: Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-base

FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning

model-releasesarxiv-cs-cl

arXiv:2601.03938v2 Announce Type: replace-cross Abstract: Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memo

FrameVGGT: Geometry-Aligned Frame-Level Memory for Bounded Streaming VGGT

arXiv:2603.07690v2 Announce Type: replace Abstract: Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception, but their KV-cache grows unbounded over long streams,

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

arXiv:2601.04278v2 Announce Type: replace Abstract: Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfull

From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2604.17941v1 Announce Type: cross Abstract: Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions.

From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs

arXiv:2601.03682v2 Announce Type: replace Abstract: Recent studies reveal that large language models (LLMs) exhibit limited logical reasoning abilities in mathematical problem-solving, instead often r

Local Ai

Fully Analog Resonant Recurrent Neural Network via Metacircuit

arXiv:2604.17277v1 Announce Type: new Abstract: Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to convent

local-aiarxiv-cs-lg

Functional Similarity Metric for Neural Networks: Overcoming Parametric Ambiguity via Activation Region Analysis

model-releasesarxiv-cs-lg

arXiv:2604.16426v1 Announce Type: new Abstract: As modern deep learning architectures grow in complexity, representational ambiguity emerges as a critical barrier to their interpretability and reliabl

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving

model-releasesarxiv-cs-lg

arXiv:2604.16436v1 Announce Type: cross Abstract: This paper develops an end-to-end fuzzy encoder-decoder architecture for enhancing vision-based multi-modal deep spiking Q-networks in autonomous driv

G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes

arXiv:2604.16533v1 Announce Type: new Abstract: Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding

researcharxiv-cs-lg

GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning

arXiv:2604.17241v1 Announce Type: new Abstract: Implicit spatial relations and deep semantic structures encoded in object attributes are crucial for procedural planning in embodied AI systems. However

researcharxiv-cs-ro

Geometry-Guided 3D Visual Token Pruning for Video-Language Models

arXiv:2604.18260v1 Announce Type: new Abstract: Multimodal large language models have demonstrated remarkable capabilities in 2D vision, motivating their extension to 3D scene understanding. Recent st

GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization

arXiv:2506.07160v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is

safetyarxiv-cs-cl

Hardware

Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction

arXiv:2604.16590v1 Announce Type: new Abstract: Accurate weather and climate prediction relies on data assimilation (DA), which estimates the Earth system state by integrating observations with models

hardwarearxiv-cs-lg

GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning

arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua

Agents

GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning

arXiv:2604.16871v1 Announce Type: cross Abstract: Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizabl

agentsarxiv-cs-lg

Tutorials

Graph neural network for colliding particles with an application to sea ice floe modeling

arXiv:2602.16213v2 Announce Type: replace-cross Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ic

tutorialsarxiv-cs-cv