Search: “models”

On The Application of Linear Attention in Multimodal Transformers

arXiv:2604.10064v1 Announce Type: new Abstract: Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical b

researcharxiv-cs-cv

Local Ai

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Pair2Scene: Learning Local Object Relations for Procedural Scene Generation

DGX agent

arXiv:2604.11808v1 Announce Type: new Abstract: Generating high-fidelity 3D indoor scenes remains a significant challenge due to data scarcity and the complexity of modeling intricate spatial relation

local-aiarxiv-cs-cv

14 Apr 2026

PEMANT: Persona-Enriched Multi-Agent Negotiation for Travel

arXiv:2604.10475v1 Announce Type: new Abstract: Modeling household-level trip generation is fundamental to accurate demand forecasting, traffic flow estimation, and urban system planning. Existing stu

safetyarxiv-cs-ai

Persona Non Grata: Single-Method Safety Evaluation Is Incomplete for Persona-Imbued LLMs

arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti

Query Lower Bounds for Diffusion Sampling

arXiv:2604.10857v1 Announce Type: cross Abstract: Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by mi

researcharxiv-cs-ai

Applications

Resisting Humanization: Ethical Front-End Design Choices in AI for Sensitive Contexts

arXiv:2603.24853v2 Announce Type: replace Abstract: Ethical debates in AI have primarily focused on back-end issues such as data governance, model training, and algorithmic decision-making. Less atten

applicationsarxiv-cs-ai

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

arXiv:2604.09860v1 Announce Type: cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid

S4M: 4-points to Segment Anything

arXiv:2503.05534v3 Announce Type: replace Abstract: Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred bo

researcharxiv-cs-cv

SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering

arXiv:2508.11290v3 Announce Type: replace Abstract: LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful

safetyarxiv-cs-cl

Hardware

SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation

arXiv:2604.10306v1 Announce Type: new Abstract: As Earth-observation workloads move toward onboard and edge processing, remote-sensing segmentation models must operate under tight latency and energy c

hardwarearxiv-cs-cv

SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?

arXiv:2604.10718v1 Announce Type: new Abstract: Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly p

Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding

model-releasesarxiv-cs-cv

arXiv:2604.11244v1 Announce Type: new Abstract: Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both vi

SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding

arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs)

SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding

arXiv:2604.09557v1 Announce Type: cross Abstract: Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optim

StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs

arXiv:2509.22220v2 Announce Type: replace Abstract: Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrel

researcharxiv-cs-cl

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604.09737v1 Announce Type: cross Abstract: Structured prediction requires models to generate ontology-constrained labels, grounded evidence, and valid structure under ambiguity, label skew, and

Tutorials

SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning

arXiv:2604.10228v1 Announce Type: new Abstract: Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this

tutorialsarxiv-cs-ai

The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise

arXiv:2604.09780v1 Announce Type: new Abstract: Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their 'expert specialization' remain poorly understood.

researcharxiv-cs-ai

Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations

model-releasesarxiv-cs-cl

arXiv:2604.09625v1 Announce Type: new Abstract: We study whether large-scale unlabelled web data and LLM-based synthetic annotations can improve multilingual hate speech detection. Starting from texts

Trajectory-based actuator identification via differentiable simulation

arXiv:2604.10351v1 Announce Type: new Abstract: Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics ty

safetyarxiv-cs-ro

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards

arXiv:2604.10110v1 Announce Type: new Abstract: Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how sma

Tuning Qwen2.5-VL to Improve Its Web Interaction Skills

arXiv:2604.09571v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independe

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization

arXiv:2604.10721v1 Announce Type: cross Abstract: Natural-language Guided Cross-view Geo-localization (NGCG) aims to retrieve geo-tagged satellite imagery using textual descriptions of ground scenes.

Variable Selection Using Relative Importance Rankings

model-releasesarxiv-cs-lg

arXiv:2509.10853v2 Announce Type: replace-cross Abstract: Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature.

Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks

arXiv:2604.10166v1 Announce Type: cross Abstract: Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven contr

VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites

arXiv:2506.14629v3 Announce Type: replace-cross Abstract: Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreak

researcharxiv-cs-cl

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval

arXiv:2604.10167v1 Announce Type: cross Abstract: Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational

researcharxiv-cs-cl

What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data

arXiv:2510.26202v2 Announce Type: replace-cross Abstract: Human feedback can alter language models in unpredictable and undesirable ways, as practitioners lack a clear understanding of what feedback d

safetyarxiv-cs-ai

A Benchmark of Dexterity for Anthropomorphic Robotic Hands

model-releasesarxiv-cs-ro

arXiv:2604.09294v1 Announce Type: new Abstract: Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands. In practice, the term is often use

A Closer Look at the Application of Causal Inference in Graph Representation Learning

arXiv:2604.08890v1 Announce Type: cross Abstract: Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods

researcharxiv-cs-ai

Agentic Jackal: Live Execution and Semantic Value Grounding for Text-to-JQL

model-releasesarxiv-cs-cl

arXiv:2604.09470v1 Announce Type: new Abstract: Translating natural language into Jira Query Language (JQL) requires resolving ambiguous field references, instance-specific categorical values, and com

Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography

model-releasesarxiv-cs-cl

arXiv:2604.09066v1 Announce Type: new Abstract: Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to

Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design

model-releasesarxiv-cs-lg

arXiv:2604.09369v1 Announce Type: cross Abstract: Generative models can now propose thousands of de novo antibody sequences, yet translating these designs into viable therapeutics remains constrained

CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention

arXiv:2603.18561v2 Announce Type: replace Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relatio

safetyarxiv-cs-cv

Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment

arXiv:2505.18600v3 Announce Type: replace-cross Abstract: Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collaps

safetyarxiv-cs-ai

Tutorials

Generative View Stitching

arXiv:2510.24718v3 Announce Type: replace Abstract: Autoregressive video diffusion models are capable of long rollouts that are stable and consistent with history, but they are unable to guide the cur

tutorialsarxiv-cs-cv

HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?

arXiv:2604.09408v1 Announce Type: new Abstract: Frontier coding agents solve complex tasks when given complete context but collapse when specifications are incomplete or ambiguous. The bottleneck is n

Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

arXiv:2604.09231v1 Announce Type: new Abstract: Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-vie

safetyarxiv-cs-cv

Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation

arXiv:2604.08797v1 Announce Type: cross Abstract: Stories are key to transmitting values across cultures, but their interpretation varies across linguistic and cultural contexts. Thus, we introduce mu

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

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

arXiv:2505.20638v2 Announce Type: replace-cross Abstract: While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music nec

researcharxiv-cs-cv

PhysInOne: Visual Physics Learning and Reasoning in One Suite

arXiv:2604.09415v1 Announce Type: cross Abstract: We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike exi

PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos

arXiv:2604.08991v1 Announce Type: cross Abstract: Small object-centric spatial understanding in indoor videos remains a significant challenge for multimodal large language models (MLLMs), despite its

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

Applications

Relational Visual Similarity

arXiv:2512.07833v2 Announce Type: replace-cross Abstract: Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but

applicationsarxiv-cs-ai

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

arXiv:2510.17640v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable performance on complex tasks through imitation learning in recent robotic man

SenBen: Sensitive Scene Graphs for Explainable Content Moderation

arXiv:2604.08819v1 Announce Type: cross Abstract: Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behav

SHIFT: Steering Hidden Intermediates in Flow Transformers

arXiv:2604.09213v1 Announce Type: new Abstract: Diffusion models have become leading approaches for high-fidelity image generation. Recent DiT-based diffusion models, in particular, achieve strong pro

safetyarxiv-cs-cv