Search: “framework”

LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design

arXiv:2604.08636v1 Announce Type: cross Abstract: Designing robot morphologies and kinematics has traditionally relied on human intuition, with little systematic foundation. Motion-design co-optimizat

tutorialsarxiv-cs-ai

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Listener-Rewarded Thinking in VLMs for Image Preferences

DGX agent

arXiv:2506.22832v3 Announce Type: replace-cross Abstract: Training robust and generalizable reward models for human visual preferences is essential for aligning text-to-image and text-to-video generat

model-releasesarxiv-cs-ai

13 Apr 2026

LLM-Rosetta: A Hub-and-Spoke Intermediate Representation for Cross-Provider LLM API Translation

arXiv:2604.09360v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Model (LLM) providers--each exposing proprietary API formats--has created a fragmented ecosystem where appli

applicationsarxiv-cs-ai

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving

arXiv:2604.08719v1 Announce Type: cross Abstract: Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck

LoBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction

arXiv:2510.01767v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has established itself as an efficient representation for real-time, high-fidelity 3D scene reconstruction. However, sc

researcharxiv-cs-cv

Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

arXiv:2604.09206v1 Announce Type: new Abstract: Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing ho

Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks

model-releasesarxiv-cs-lg

arXiv:2502.02345v2 Announce Type: replace Abstract: Subspace inference for neural networks assumes that a subspace of their parameter space suffices to produce a reliable uncertainty quantification. I

Lucky to work with a fantastic team!

Harrison Chase, co-founder and CEO of LangChain, expressed gratitude for his team in a post on X (formerly Twitter). The post likely highlights appreciation for colleagues and collaborators contributi

agentsharrison-chase--x

MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits

arXiv:2604.08952v1 Announce Type: new Abstract: Document Question Answering (DQA) involves generating answers from a document based on a user's query, representing a key task in document understanding

safetyarxiv-cs-cl

Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space

model-releasesarxiv-cs-lg

arXiv:2501.15461v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as

Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory

arXiv:2604.08995v1 Announce Type: new Abstract: With the advancement of interactive video generation, diffusion models have increasingly demonstrated their potential as world models. However, existing

applicationsarxiv-cs-cv

Maybe hot take - I’ve read a bunch of RL for image generation papers over last few months and honestly it’s been pretty disappointing. All o…

tutorialsjeremy-howard--x

Maybe hot take - I’ve read a bunch of RL for image generation papers over last few months and honestly it’s been pretty disappointing. All of them are variations of GRPO and all of them are incrementa

Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation

model-releasesarxiv-cs-cv

arXiv:2604.09088v1 Announce Type: new Abstract: Memory-efficient transfer learning (METL) approaches have recently achieved promising performance in adapting pre-trained models to downstream tasks. Th

MeshOn: Intersection-Free Mesh-to-Mesh Composition

arXiv:2604.08799v1 Announce Type: cross Abstract: We propose MeshOn, a method that finds physically and semantically realistic compositions of two input meshes. Given an accessory, a base mesh with a

Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym

arXiv:2604.09338v1 Announce Type: new Abstract: Spatial reasoning is central to navigation and robotics, yet measuring model capabilities on these tasks remains difficult. Existing benchmarks evaluate

tutorialsarxiv-cs-ai

Mitigating Extrinsic Gender Bias for Bangla Classification Tasks

arXiv:2411.10636v2 Announce Type: replace-cross Abstract: In this study, we investigate extrinsic gender bias in Bangla pretrained language models, a largely underexplored area in low-resource languag

Model Space Reasoning as Search in Feedback Space for Planning Domain Generation

arXiv:2604.08712v1 Announce Type: new Abstract: The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasonin

agentsarxiv-cs-ai

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization

arXiv:2604.09253v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visu

MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis

arXiv:2502.13718v2 Announce Type: replace Abstract: Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies

safetyarxiv-cs-cl

MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation

arXiv:2604.08782v1 Announce Type: new Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational

agentsarxiv-cs-cl

Multimodal Anomaly Detection for Human-Robot Interaction

arXiv:2604.09326v1 Announce Type: cross Abstract: Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures

Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation

arXiv:2604.08582v1 Announce Type: cross Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. M

Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait

arXiv:2604.09431v1 Announce Type: new Abstract: Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimi

safetyarxiv-cs-ro

MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator

arXiv:2604.08947v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly prevalent in text simplification, systematically evaluating their outputs across diverse prompting

MV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation

arXiv:2604.08916v1 Announce Type: new Abstract: Conventional 3D instance segmentation methods rely on labor-intensive 3D annotations for supervised training, which limits their scalability and general

tutorialsarxiv-cs-cv

Natural Riemannian gradient for learning functional tensor networks

model-releasesarxiv-cs-lg

arXiv:2604.09263v1 Announce Type: cross Abstract: We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regre

Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning

arXiv:2604.08980v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer visi

applicationsarxiv-cs-ai

Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening

arXiv:2604.09062v1 Announce Type: new Abstract: Valid segmentation of the optic disc (OD) and optic cup (OC) from fundus photographs is essential for glaucoma screening. Unfortunately, existing deep l

researcharxiv-cs-cv

Local Ai

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution

arXiv:2502.06809v3 Announce Type: replace-cross Abstract: Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron-concept attribution, posing a significant challenge for m

local-aiarxiv-cs-ai

NOMAD: Generating Embeddings for Massive Distributed Graphs

arXiv:2604.09419v1 Announce Type: new Abstract: Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preser

applicationsarxiv-cs-lg

Local Ai

Off-the-shelf Vision Models Benefit Image Manipulation Localization

arXiv:2604.09096v1 Announce Type: new Abstract: Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differen

local-aiarxiv-cs-cv

On Semiotic-Grounded Interpretive Evaluation of Generative Art

arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve

Online Quantile Regression for Nonparametric Additive Models

arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli

researcharxiv-cs-lg

Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks

arXiv:2604.09028v1 Announce Type: cross Abstract: Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and tra

safetyarxiv-cs-lg

Post-Selection Distributional Model Evaluation

arXiv:2603.23055v2 Announce Type: replace-cross Abstract: Formal model evaluation methods typically certify that a model satisfies a prescribed target key performance indicator (KPI) level. However, i

safetyarxiv-cs-lg

Precise Shield: Explaining and Aligning VLLM Safety via Neuron-Level Guidance

model-releasesarxiv-cs-cv

arXiv:2604.08881v1 Announce Type: new Abstract: In real-world deployments, Vision-Language Large Models (VLLMs) face critical challenges from multilingual and multimodal composite attacks: harmful ima

Prototype-Regularized Federated Learning for Cross-Domain Aspect Sentiment Triplet Extraction

arXiv:2604.09123v1 Announce Type: new Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract all sentiment triplets of aspect terms, opinion terms, and sentiment polarities from a senten

researcharxiv-cs-cl

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos

arXiv:2508.04853v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including

RAM: Recover Any 3D Human Motion in-the-Wild

arXiv:2603.19929v2 Announce Type: replace-cross Abstract: RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions

researcharxiv-cs-ai

Region-Constrained Group Relative Policy Optimization for Flow-Based Image Editing

arXiv:2604.09386v1 Announce Type: new Abstract: Instruction-guided image editing requires balancing target modification with non-target preservation. Recently, flow-based models have emerged as a stro

ReplicatorBench: Benchmarking LLM Agents for Replicability in Social and Behavioral Sciences

arXiv:2602.11354v2 Announce Type: replace Abstract: The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers. Existing benchmarks focus primarily on

Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios

model-releasesarxiv-cs-cv

arXiv:2509.20006v3 Announce Type: replace Abstract: With the large models easing the labor-intensive manipulation process, image manipulations in today's real scenarios often entail a complex manipula

Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments

arXiv:2604.09323v1 Announce Type: new Abstract: This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. T

researcharxiv-cs-ro

SAGE: A Service Agent Graph-guided Evaluation Benchmark

arXiv:2604.09285v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Ex

SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

arXiv:2604.08988v1 Announce Type: new Abstract: Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, faili

Self-Sovereign Agent

arXiv:2604.08551v1 Announce Type: cross Abstract: We investigate the emerging prospect of self-sovereign agents -- AI systems that can economically sustain and extend their own operation without human

agentsarxiv-cs-lg

Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI

arXiv:2601.22990v2 Announce Type: replace-cross Abstract: Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume r

researcharxiv-cs-ai

Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines

arXiv:2604.08608v1 Announce Type: cross Abstract: We introduce Semantic Intent Fragmentation (SIF), an attack class against LLM orchestration systems where a single, legitimately phrased request cause