Search: “models”

Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild

arXiv:2605.22064v1 Announce Type: new Abstract: Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B,

22 May 2026

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Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons

DGX agent

arXiv:2605.16975v1 Announce Type: cross Abstract: Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a r

model-releasesarxiv-cs-ai

19 May 2026

Foundation Models for Credit Risk Prediction: A Game Changer?

arXiv:2605.18147v1 Announce Type: new Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and los

researcharxiv-cs-lg

19 May 2026

GenTS: A Comprehensive Benchmark Library for Generative Time Series Models

arXiv:2605.17804v1 Announce Type: new Abstract: Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering

19 May 2026

Beyond Mode-Seeking RL: Trajectory-Balance Post-Training for Diffusion Language Models

arXiv:2605.13935v1 Announce Type: cross Abstract: Diffusion language models are a promising alternative to autoregressive models, yet post-training methods for them largely adapt reward-maximizing obj

15 May 2026

Hidden State Poisoning Attacks against Mamba-based Language Models

arXiv:2601.01972v4 Announce Type: cross Abstract: State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their advers

15 May 2026

Understanding and Accelerating the Training of Masked Diffusion Language Models

arXiv:2605.13026v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known

14 May 2026

When is Warmstarting Effective for Scaling Language Models?

arXiv:2605.13405v1 Announce Type: new Abstract: Model growth from a given checkpoint aims to accelerate training of a larger model, offering potential resource savings. Despite recent interest, warmst

14 May 2026

Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models

arXiv:2605.11887v1 Announce Type: new Abstract: Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, li

13 May 2026

Safety

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models

arXiv:2605.09291v1 Announce Type: new Abstract: Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be v

safetyarxiv-cs-lg

12 May 2026

GIFT: Guided Importance-Aware Fine-Tuning for Diffusion Language Models

arXiv:2509.20863v3 Announce Type: replace Abstract: Diffusion models have recently shown strong potential in language modeling, offering faster generation compared to traditional autoregressive approa

12 May 2026

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

arXiv:2501.12202v4 Announce Type: replace Abstract: We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two fo

12 May 2026

Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models

arXiv:2602.00217v2 Announce Type: replace Abstract: Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational costs.

Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning

arXiv:2605.02247v1 Announce Type: new Abstract: Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distrib

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models

arXiv:2605.01256v1 Announce Type: new Abstract: A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge

tutorialsarxiv-cs-cl

The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles

arXiv:2605.01407v1 Announce Type: cross Abstract: Masked Language Modeling (MLM) pre-training is one of the primary ways to initialize Neural Information Retrieval (IR) models prior to retrieval fine-

researcharxiv-cs-cl

When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models

arXiv:2605.02363v1 Announce Type: new Abstract: Deployed language models must produce outputs that are both correct and format-compliant. We study this structured-output reliability gap using two math

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

arXiv:2507.01955v3 Announce Type: replace Abstract: Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond que

4 May 2026

Jailbroken Frontier Models Retain Their Capabilities

arXiv:2605.00267v1 Announce Type: new Abstract: As language model safeguards become more robust, attackers are pushed toward developing increasingly complex jailbreaks. Prior work has found that this

4 May 2026

Learning Illumination Control in Diffusion Models

arXiv:2604.24877v1 Announce Type: new Abstract: Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive ill

researcharxiv-cs-cv

29 Apr 2026

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving

arXiv:2604.22851v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) have advanced highlevel reasoning in autonomous driving, their ability to ground this reasoning in the underlying

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Safety

LinguDistill: Recovering Linguistic Ability in Vision-Language Models via Selective Cross-Modal Distillation

arXiv:2604.00829v3 Announce Type: replace-cross Abstract: Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representat

safetyarxiv-cs-cl

On the Memorization of Consistency Distillation for Diffusion Models

arXiv:2604.23552v1 Announce Type: cross Abstract: Diffusion models are central to modern generative modeling, and understanding how they balance memorization and generalization is critical for reliabl

researcharxiv-cs-ai

Patching LLM Like Software: A Lightweight Method for Improving Safety Policy in Large Language Models

arXiv:2511.08484v2 Announce Type: replace Abstract: We propose patching for large language models (LLMs) like software versions, a lightweight and modular approach for addressing safety vulnerabilitie

Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?

arXiv:2510.10254v2 Announce Type: replace Abstract: Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-sh

researcharxiv-cs-cv

27 Apr 2026

Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models

arXiv:2507.04023v3 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while gene

24 Apr 2026

Applications

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs

applicationsarxiv-cs-ai

22 Apr 2026

OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models

arXiv:2604.00688v3 Announce Type: replace Abstract: We present OmniVoice, a massively multilingual zero-shot text-to-speech (TTS) model that scales to over 600 languages. At its core is a novel diffus

researcharxiv-cs-cl

22 Apr 2026

Finding Culture-Sensitive Neurons in Vision-Language Models

arXiv:2510.24942v2 Announce Type: replace-cross Abstract: Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs proce

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

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.

Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?

arXiv:2604.17930v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence: while they learn some linguistic phenomena with near-pe

tutorialsarxiv-cs-cl

Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning

arXiv:2604.16378v1 Announce Type: new Abstract: Large language models (LLMs) and classical machine learning methods offer complementary strengths for predictive modeling, yet their fundamentally diffe

tutorialsarxiv-cs-cl

ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

arXiv:2603.05863v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

arXiv:2508.11281v3 Announce Type: replace Abstract: Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in

Understanding Counting Mechanisms in Large Language and Vision-Language Models

arXiv:2511.17699v2 Announce Type: replace Abstract: Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these

researcharxiv-cs-cv

BAGEL: Benchmarking Animal Knowledge Expertise in Language Models

arXiv:2604.16241v1 Announce Type: cross Abstract: Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language model

20 Apr 2026

Applications

Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

arXiv:2604.16280v1 Announce Type: new Abstract: Explaining Machine Learning (ML) results in a transparent and user-friendly manner remains a challenging task of Explainable Artificial Intelligence (XA

applicationsarxiv-cs-ai

20 Apr 2026

Local Ai

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting

arXiv:2505.11017v2 Announce Type: replace Abstract: Time series forecasting is critical across multiple domains, where time series data exhibit both local patterns and global dependencies. While Trans

local-aiarxiv-cs-lg

17 Apr 2026

Dataset-Level Metrics Attenuate Non-Determinism: A Fine-Grained Non-Determinism Evaluation in Diffusion Language Models

arXiv:2604.13413v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising paradigm for large language models (LLMs), yet the non-deterministic behavior of DLMs remai

researcharxiv-cs-lg

16 Apr 2026

From Plausibility to Verifiability: Risk-Controlled Generative OCR with Vision-Language Models

arXiv:2603.19790v3 Announce Type: replace Abstract: Modern vision-language models (VLMs) can act as generative OCR engines, yet open-ended decoding can expose rare but consequential failures. We ident

16 Apr 2026

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

arXiv:2604.05478v2 Announce Type: replace-cross Abstract: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resi

16 Apr 2026

NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models

arXiv:2510.13793v2 Announce Type: replace Abstract: With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This ch

tutorialsarxiv-cs-cv

15 Apr 2026

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework

arXiv:2509.18127v3 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, un

15 Apr 2026

CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity

arXiv:2604.11632v1 Announce Type: new Abstract: We introduce CARTBENCH, a museum-grounded benchmark for evaluating vision-language models (VLMs) on Chinese artworks beyond short-form recognition and Q

Design Principles for Sequence Models via Coefficient Dynamics

arXiv:2510.09389v2 Announce Type: replace-cross Abstract: Deep sequence models, ranging from Transformers and State Space Models (SSMs) to more recent approaches such as gated linear RNNs, fundamental

Do LLMs Build Spatial World Models? Evidence from Grid-World Maze Tasks

arXiv:2604.10690v1 Announce Type: new Abstract: Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and

Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts

arXiv:2604.10200v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become integral to educational decision-making, ensuring their fairness is paramount. However, current text-centric eva