Search: “models”

arXiv:2605.23972v1 Announce Type: new Abstract: Large language models achieve strong performance in language generation and knowledge-intensive tasks, yet remain limited in settings requiring causal r

arXiv:2605.23093v1 Announce Type: new Abstract: Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based appro

arXiv:2605.23238v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior i

arXiv:2605.23080v1 Announce Type: new Abstract: Feature attribution methods promise to identify which input features matter for a model output. In generative language models, however, it is often uncl

arXiv:2605.21543v1 Announce Type: new Abstract: Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audit

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,

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

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

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

Emad Mostaque, CEO of Stability AI, suggests that many autoregressive models may transition to or be replaced by diffusion-based approaches. This reflects speculation or prediction about a potential s

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

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

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

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

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

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

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

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

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.

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

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

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-

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

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

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

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

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

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

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

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

Open Models & a potential looming Haiku-apocalypse? 🚀 chart comparison UI courtesy of @OpenRouter there's a large class of problems where you don't need frontier intelligence. you want cheap, fast, an

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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