Image, video, audio, and text generation
There’s been a lot of discussion recently about how expensive AI video generation is compared to text, and it feels like this is more tha...
MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...
![[2603.10202] Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2603.10202: Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Ap...
This paper addresses the performance gap between text and speech understanding in large language models (LLMs), proposing a new method, S...
The paper introduces DITTO, a spoofing attack framework that exploits vulnerabilities in watermarked large language models (LLMs) via kno...
This paper explores the Bayesian nature of large language models (LLMs) in expectation rather than realization, highlighting the impact o...
The paper introduces SocialHarmBench, a dataset designed to evaluate the vulnerabilities of large language models (LLMs) to socially harm...
The paper introduces Eso-LMs, a novel language model that integrates autoregressive and masked diffusion paradigms, enhancing inference e...
The paper introduces Consistency Mid-Training (CMT), a novel method for enhancing the efficiency of training flow map models, achieving s...
The paper introduces PinRec, a unified generative retrieval model for Pinterest's recommendation systems, enhancing performance across va...
The paper presents ReMemR1, a novel approach for enhancing long-context reasoning in large language models by integrating revisitable mem...
The paper presents MEt3R, a novel metric for assessing multi-view consistency in generated images, addressing limitations of traditional ...
The paper introduces CORE, a metric for evaluating language quality in multi-agent LLM interactions under game-theoretic conditions, reve...
The paper presents a novel watermarking scheme for black-box language models, enabling detection of model outputs without requiring white...
The paper presents AbstRaL, a method to enhance large language models' reasoning capabilities by reinforcing abstract thinking, particula...
The paper explores how fine-tuning large language models can unintentionally create vulnerabilities, analyzing factors like dataset chara...
The paper presents BitHydra, a framework for executing bit-flip inference cost attacks on large language models (LLMs), demonstrating how...
This paper explores the balance between watermark strength and speculative sampling efficiency in language models, proposing a new approa...
The paper presents a novel method for post-training quantization (PTQ) of diffusion models, addressing inefficiencies in existing calibra...
This article surveys advancements in multi-turn interactions with large language models (LLMs), focusing on evaluation methods, challenge...
The paper presents JavisDiT, a novel Joint Audio-Video Diffusion Transformer that enhances synchronized audio-video generation through a ...
This article presents a novel approach to polyphonic music generation using structural inductive bias, focusing on Beethoven's piano sona...
This article presents a novel approach to training medical large language models (LLMs) through dialogue-based fine-tuning, improving the...
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