Search: “models”

Motif-Video 2B: Technical Report

arXiv:2604.16503v1 Announce Type: new Abstract: Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether

Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

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DGX agent

arXiv:2301.13331v3 Announce Type: replace-cross Abstract: Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, an

researcharxiv-cs-lg

21 Apr 2026

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

arXiv:2604.18471v1 Announce Type: new Abstract: Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexi

researcharxiv-cs-lg

ProTrain: Efficient LLM Training via Memory-Aware Techniques

arXiv:2406.08334v2 Announce Type: replace-cross Abstract: Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constra

ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval

arXiv:2510.08252v2 Announce Type: replace-cross Abstract: In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes

Representation-Guided Parameter-Efficient LLM Unlearning

arXiv:2604.17396v1 Announce Type: new Abstract: Large Language Models (LLMs) often memorize sensitive or harmful information, necessitating effective machine unlearning techniques. While existing para

Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting

arXiv:2601.18231v4 Announce Type: replace Abstract: Adapting pre-trained models to unseen feature modalities has become increasingly important due to the growing need for cross-disciplinary knowledge

Local Ai

RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding

arXiv:2604.17504v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remote

local-aiarxiv-cs-cv

Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection

arXiv:2604.16437v1 Announce Type: cross Abstract: Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sa

Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Reasoning

arXiv:2505.13353v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long

SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention

arXiv:2604.17324v1 Announce Type: new Abstract: Graph transformers achieve strong results on molecular and long-range reasoning tasks, yet remain hampered by over-smoothing (the progressive collapse o

Source-Free Domain Adaptation with Vision-Language Prior

arXiv:2604.17748v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) seeks to adapt a source model, which is pre-trained on a supervised source domain, for a target domain, with only a

safetyarxiv-cs-cv

Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization

arXiv:2604.18284v1 Announce Type: new Abstract: Pre-trained vision models have found widespread application across diverse domains. Prompt tuning-based methods have emerged as a parameter-efficient pa

Stable On-Policy Distillation through Adaptive Target Reformulation

arXiv:2601.07155v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however,

Style over Story: Measuring LLM Narrative Preferences via Structured Selection

arXiv:2510.02025v4 Announce Type: replace Abstract: We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers

Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers

arXiv:2510.07761v2 Announce Type: replace Abstract: Large language models (LLMs) now give reasoning before answering, excelling in tasks like multiple-choice question answering (MCQA). Yet, a concern

The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability

arXiv:2604.17698v1 Announce Type: cross Abstract: Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a mo

Applications

Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification

arXiv:2604.17480v1 Announce Type: new Abstract: In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a

applicationsarxiv-cs-lg

Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark

arXiv:2510.13759v3 Announce Type: replace Abstract: Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. E

1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization

arXiv:2602.00114v4 Announce Type: replace-cross Abstract: Few-shot learning (FSL) challenges model generalization to novel classes based on just a few shots of labeled examples, a testbed where tradit

Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue

arXiv:2604.05552v2 Announce Type: replace-cross Abstract: Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-lin

Facial-Expression-Aware Prompting for Empathetic LLM Tutoring

arXiv:2604.15336v1 Announce Type: cross Abstract: Large language models (LLMs) enable increasingly capable tutoring-style conversational agents, yet effective tutoring requires sensitivity to learners

From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text

arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solu

Applications

FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users

arXiv:2502.19312v2 Announce Type: replace-cross Abstract: Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curati

applicationsarxiv-cs-ai

Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification

arXiv:2504.15214v3 Announce Type: replace Abstract: Parameter-efficient transfer learning (PETL) methods adapt large artificial neural networks to downstream tasks without fine-tuning the entire model

Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation

arXiv:2505.13792v2 Announce Type: replace-cross Abstract: Recent advances in reasoning-focused Large Language Models (LLMs) have introduced Chain-of-Thought (CoT) traces - intermediate reasoning steps

LLMs Corrupt Your Documents When You Delegate

arXiv:2604.15597v1 Announce Type: new Abstract: Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding

Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions

arXiv:2604.15917v1 Announce Type: new Abstract: Instruction guided image editing has advanced substantially with recent generative models, yet it still fails to produce reliable results across many se

Qwen3.5-Omni Technical Report

arXiv:2604.15804v1 Announce Type: new Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor,

Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting

arXiv:2604.15794v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degra

safetyarxiv-cs-ai

Stochasticity in Tokenisation Improves Robustness

arXiv:2604.16037v1 Announce Type: new Abstract: The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation

Transformer Neural Processes - Kernel Regression

arXiv:2411.12502v4 Announce Type: replace-cross Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic p

UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs

arXiv:2604.15871v1 Announce Type: cross Abstract: The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms

What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context

arXiv:2506.02261v3 Announce Type: replace-cross Abstract: What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that e

safetyarxiv-cs-lg

Why Fine-Tuning Encourages Hallucinations and How to Fix It

arXiv:2604.15574v1 Announce Type: cross Abstract: Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information t

Benchmarking Linguistic Adaptation in Comparable-Sized LLMs: A Study of Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B on Romanized Nepali

arXiv:2604.14171v1 Announce Type: new Abstract: Romanized Nepali, the Nepali language written in the Latin alphabet, is the dominant medium for informal digital communication in Nepal, yet it remains

Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs

arXiv:2509.05367v4 Announce Type: replace-cross Abstract: Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classificati

safetyarxiv-cs-ai

Beyond Translation: Evaluating Mathematical Reasoning Capabilities of LLMs in Sinhala and Tamil

arXiv:2602.14517v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support

ConfLayers: Adaptive Confidence-based Layer Skipping for Self-Speculative Decoding

arXiv:2604.14612v1 Announce Type: cross Abstract: Self-speculative decoding is an inference technique for large language models designed to speed up generation without sacrificing output quality. It c

safetyarxiv-cs-cl

FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images

arXiv:2604.14388v1 Announce Type: new Abstract: Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision lang

Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems

arXiv:2510.14133v2 Announce Type: replace Abstract: Agentic AI systems, which leverage multiple autonomous agents and large language models (LLMs), are increasingly used to address complex, multi-step

safetyarxiv-cs-ai

From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning

arXiv:2604.15244v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target mod

Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments

arXiv:2604.14781v1 Announce Type: new Abstract: Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and

safetyarxiv-cs-cv

Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data

arXiv:2604.14229v1 Announce Type: cross Abstract: Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models naturally operate in complex Hilbert spa

MARCA: A Checklist-Based Benchmark for Multilingual Web Search

arXiv:2604.14448v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select rel

Mechanistic Decoding of Cognitive Constructs in LLMs

arXiv:2604.14593v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate increasingly sophisticated affective capabilities, the internal mechanisms by which they process complex

Agents

MIND: AI Co-Scientist for Material Research

arXiv:2604.13699v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning witho

agentsarxiv-cs-ai

Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification

arXiv:2604.14643v1 Announce Type: new Abstract: Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification. Most existing methods r