Search: “models”

Toward Better Geometric Representations for Molecule Generative Models

arXiv:2605.07693v1 Announce Type: new Abstract: Geometric representation-conditioned molecule generation provides an effective paradigm that decouples molecule representation modeling from structure g

11 May 2026

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Applications

A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset

DGX agent

arXiv:2605.04888v1 Announce Type: new Abstract: The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This stud

applicationsarxiv-cs-cl

7 May 2026

Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models

arXiv:2505.24187v2 Announce Type: replace Abstract: The prevailing assumption of an exponential decay in large language model (LLM) reliability with sequence length, predicated on independent per-toke

Bridging Input Feature Spaces Towards Graph Foundation Models

arXiv:2605.04834v1 Announce Type: new Abstract: Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but

CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

arXiv:2605.04641v1 Announce Type: new Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviat

Distilling Bayesian Belief States into Language Models for Auditable Negotiation

arXiv:2605.04507v1 Announce Type: new Abstract: Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end

safetyarxiv-cs-cl

On the Wasserstein Gradient Flow Interpretation of Drifting Models

arXiv:2605.05118v1 Announce Type: new Abstract: Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of

Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization

arXiv:2605.04700v1 Announce Type: cross Abstract: Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire wave

safetyarxiv-cs-cl

A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

arXiv:2605.03440v1 Announce Type: new Abstract: The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents

Audio-Visual Intelligence in Large Foundation Models

arXiv:2605.04045v1 Announce Type: new Abstract: Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines

Controllable and Verifiable Process Data Synthesis for Process Reward Models

arXiv:2605.02395v1 Announce Type: new Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error l

CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding

arXiv:2605.02218v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devi

False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models

arXiv:2601.07885v2 Announce Type: replace-cross Abstract: Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs)

safetyarxiv-cs-ai

Position: How can Graphs Help Large Language Models?

arXiv:2605.02452v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding o

Tutorials

Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News

arXiv:2605.03450v1 Announce Type: new Abstract: In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. St

tutorialsarxiv-cs-cl

Stochastic Schrodinger Diffusion Models for Pure-State Ensemble Generation

arXiv:2605.03573v1 Announce Type: cross Abstract: In quantum machine learning (QML), classical data are often encoded as quantum pure states and processed directly as quantum representations, motivati

The Coding Limits of Robust Watermarking for Generative Models

arXiv:2509.10577v3 Announce Type: replace-cross Abstract: We study a basic question about cryptographic watermarking for generative models: how reliable can a watermark remain when an adversary is all

Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models

arXiv:2605.03609v1 Announce Type: cross Abstract: Large language models often display heterogeneous moral preferences across settings. We study inference-time steering toward a desired ethical framewo

Tutorials

Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models

arXiv:2601.08517v2 Announce Type: replace Abstract: Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial chal

tutorialsarxiv-cs-cv

CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint

arXiv:2605.01479v1 Announce Type: new Abstract: Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free

Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models

arXiv:2605.02715v1 Announce Type: cross Abstract: Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural a

Exploring Data-Free LoRA Transferability for Video Diffusion Models

arXiv:2605.01929v1 Announce Type: new Abstract: Video diffusion models leveraging step distillation or causal distillation have achieved remarkable performance. However, adapting existing LoRAs to the

MIRL: Mutual Information-Guided Reinforcement Learning for Vision-Language Models

arXiv:2605.01520v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning

Referring Multiple Regions with Large Multimodal Models via Contextual Latent Steering

arXiv:2605.01827v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently demonstrated their proficiency in holistic visual comprehension. However, most of them struggle to tackle r

Research on Vision-Language Question Answering Models for Industrial Robots

arXiv:2605.01483v1 Announce Type: new Abstract: A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of se

Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models

arXiv:2605.01317v1 Announce Type: new Abstract: This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,00

TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks

arXiv:2605.01761v1 Announce Type: new Abstract: Text-to-Video (T2V) models have demonstrated remarkable capability in generating temporally coherent videos from natural language prompts, yet they also

Beyond Suffixes: Token Position in GCG Adversarial Attacks on Large Language Models

arXiv:2602.03265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have seen widespread adoption across multiple domains, creating an urgent need for robust safety alignment mechanisms.

4 May 2026

Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models

arXiv:2605.00591v1 Announce Type: new Abstract: Contrastive vision-language models like CLIP exhibit remarkable zero-shot generalization. However, prompt tuning remains highly sensitive to label noise

4 May 2026

Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models

arXiv:2601.21214v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems. However, rece

4 May 2026

Hardware

EdgeFM: Efficient Edge Inference for Vision-Language Models

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

hardwarearxiv-cs-cv

1 May 2026

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models

arXiv:2604.27467v1 Announce Type: cross Abstract: Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback

1 May 2026

Applications

Sentiment Analysis of AI Adoption in Indonesian Higher Education Using Machine Learning and Transformer-Based Models

arXiv:2604.27439v1 Announce Type: new Abstract: This study analyzes Indonesian student opinions on the adoption of artificial intelligence in higher education using two approaches: TF-IDF-based machin

applicationsarxiv-cs-cl

1 May 2026

Domain-Adapted Small Language Models for Reliable Clinical Triage

arXiv:2604.26766v1 Announce Type: cross Abstract: Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-

Applications

Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models

arXiv:2401.00761v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine

applicationsarxiv-cs-ai

Applications

Large Language Models for Multilingual Code Intelligence: A Survey

arXiv:2604.25960v1 Announce Type: cross Abstract: Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Py

applicationsarxiv-cs-lg

Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models

arXiv:2604.26251v1 Announce Type: cross Abstract: We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the h

SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models

arXiv:2512.18583v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy op

Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models

arXiv:2604.26951v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters f

DGLight: DQN-Guided GRPO Fine-Tuning of Large Language Models for Traffic Signal Control

arXiv:2604.25259v1 Announce Type: new Abstract: Traffic signal control (TSC) plays a central role in reducing congestion and maintaining urban mobility. This dissertation introduces DGLight, a critic-

29 Apr 2026

Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

arXiv:2512.20677v4 Announce Type: replace-cross Abstract: The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically eval

safetyarxiv-cs-cl

29 Apr 2026

Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature

arXiv:2508.18717v3 Announce Type: replace-cross Abstract: Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the dat

29 Apr 2026

AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models

arXiv:2409.01579v2 Announce Type: replace-cross Abstract: Retrieved documents containing noise will hinder RAG from detecting answer clues and make the inference process slow and expensive. Therefore,

Agents

Agent-Aided Design for Dynamic CAD Models

arXiv:2604.15184v2 Announce Type: replace Abstract: In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of

agentsarxiv-cs-ai

DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making

arXiv:2604.23557v1 Announce Type: cross Abstract: Building scalable and reusable multi-agent decision policies from offline datasets remains a challenge in offline multi-agent reinforcement learning (

safetyarxiv-cs-ai

Tutorials

Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models

arXiv:2604.24608v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document rel

tutorialsarxiv-cs-ai

Mammographic Lesion Segmentation with Lightweight Models: A Comparative Study

arXiv:2604.23899v1 Announce Type: new Abstract: Breast cancer is a leading cause of cancer-related mortality among women worldwide, with mammography as the primary screening tool. While deep learning

MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches

arXiv:2604.22881v1 Announce Type: cross Abstract: Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long

safetyarxiv-cs-ai