Search: “models”

arXiv:2601.15724v2 Announce Type: replace Abstract: Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning

arXiv:2604.18574v1 Announce Type: new Abstract: Large language models have achieved significant reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capab

arXiv:2604.15453v1 Announce Type: cross Abstract: Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, token

arXiv:2604.15351v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoR

arXiv:2604.16158v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both

arXiv:2604.15842v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain unde

arXiv:2604.16009v1 Announce Type: new Abstract: Metacognition, the ability to monitor and regulate one's own reasoning, remains under-evaluated in AI benchmarking. We introduce MEDLEY-BENCH, a benchma

arXiv:2604.15859v1 Announce Type: cross Abstract: Forecasting has become a natural benchmark for reasoning under uncertainty. Yet existing evaluations of large language models remain limited to judgme

arXiv:2604.14914v1 Announce Type: new Abstract: Text-driven inversion of generative models is a core paradigm for manipulating 2D or 3D content, unlocking numerous applications such as text-based edit

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude. Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on t

arXiv:2604.13521v1 Announce Type: new Abstract: Neural network models with latent recurrent processing, where identical layers are recursively applied to the latent state, have gained attention as pro

arXiv:2603.00192v2 Announce Type: replace Abstract: In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk est

arXiv:2604.13297v1 Announce Type: cross Abstract: This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and sta

arXiv:2604.12989v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model

arXiv:2604.12911v1 Announce Type: cross Abstract: Multilingual benchmarks guide the development of frontier models. Yet multilingual evaluations reported by frontier models are structured similar to p

arXiv:2510.10073v2 Announce Type: replace-cross Abstract: Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed

arXiv:2510.03989v2 Announce Type: replace-cross Abstract: The Transformer architecture has revolutionized the field of sequence modeling and underpins the recent breakthroughs in large language models

arXiv:2601.11044v3 Announce Type: replace Abstract: Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. Howev

arXiv:2510.10925v2 Announce Type: replace-cross Abstract: Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. Ho

arXiv:2604.09793v1 Announce Type: cross Abstract: Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific d

arXiv:2305.16272v5 Announce Type: replace Abstract: Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single enti

arXiv:2604.11666v1 Announce Type: cross Abstract: As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dial

arXiv:2601.17172v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias a

arXiv:2604.10495v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in real-world applications, reliable uncertainty quantification (UQ) becomes critical for safe

arXiv:2511.20151v2 Announce Type: replace Abstract: Learned image compression (LIC) has recently benefited from Transformer- and state space models (SSM)- based backbones for modeling long-range depen

arXiv:2604.08779v1 Announce Type: new Abstract: Large language models (LLMs) have significant potential to improve operational efficiency in operations management. Deploying these models requires spec

arXiv:2604.08722v1 Announce Type: cross Abstract: Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and e

arXiv:2604.09497v1 Announce Type: cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

arXiv:2604.08357v2 Announce Type: replace Abstract: Conditional Diffusion Models are powerful surrogates for emulating complex spatiotemporal dynamics, yet they often fail to match the accuracy of det

arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c

arXiv:2604.09037v1 Announce Type: cross Abstract: Current video benchmarks for multimodal large language models (MLLMs) focus on event recognition, temporal ordering, and long-context recall, but over

arXiv:2311.14756v2 Announce Type: replace-cross Abstract: Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models w

After seeing that Claude Mythos marketing turned out to be, as expected, a scam, I wanted to make a master list of tricks being used to market LLMs. The master list includes statements directly from l

arXiv:2511.20162v2 Announce Type: replace Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given

arXiv:2601.02670v2 Announce Type: replace Abstract: We introduce self-jailbreaking, a threat model in which an aligned LLM guides its own compromise. Unlike most jailbreak techniques, which oft

arXiv:2604.06398v1 Announce Type: cross Abstract: Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To add

arXiv:2601.08258v3 Announce Type: replace Abstract: Large language models increasingly fail in a way that scalar accuracy cannot diagnose: they produce a sound reasoning trace and then abandon it unde

arXiv:2604.07147v1 Announce Type: cross Abstract: Large language models produce repetitive output when prompted independently across many batches, a phenomenon we term cross-batch mode collapse: the p

GLM-5.1 by @Zai_org is now #3 in Code Arena - surpassing Gemini 3.1 and GPT-5.4, and now on par with Claude Sonnet 4.6. The first frontier level open model to break into the top 3. It’s a major +90 po

arXiv:2604.03044v2 Announce Type: replace-cross Abstract: We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performan

arXiv:2604.06188v1 Announce Type: cross Abstract: People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such

arXiv:2509.09926v5 Announce Type: replace Abstract: Long-tailed semi-supervised learning (LTSSL) presents a formidable challenge where models must overcome the scarcity of tail samples while mitigatin

arXiv:2604.04771v2 Announce Type: replace-cross Abstract: Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remain

Ollama released a preview version (0.19) on March 31, 2026, built on top of Apple's open-source MLX framework, enabling local LLMs to run significantly faster on Apple Silicon Macs by leveraging th...

arXiv:2604.02073v2 Announce Type: replace Abstract: Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by g

arXiv:2512.01236v2 Announce Type: replace Abstract: Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, w