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...
The paper presents DIVER, a framework for enhancing reasoning in Large Language Models through diversity-incentivized exploration, addres...
GenesisGeo presents a novel approach to geometric reasoning by introducing a large-scale dataset and a multi-task training paradigm that ...
This article presents a novel method, Physics-Based Flow Matching (PBFM), which integrates physical constraints into generative modeling,...
SOP-Bench introduces a benchmark for evaluating LLM agents on complex industrial SOPs, featuring over 2,000 tasks across various domains,...
This paper presents a novel optimization framework for large language models (LLMs) based on generalized Gaussian distributions, enhancin...
The paper introduces QiMeng-CodeV-R1, a framework for reasoning-enhanced Verilog generation using reinforcement learning with verifiable ...
The paper introduces Meta-Continual Learning of Neural Fields (MCL-NF), a novel approach that enhances the efficiency and quality of neur...
This paper presents a novel geometric perspective on diffusion models, revealing flaws in traditional decoding methods and proposing a ne...
The paper introduces Ambig-SWE, a framework for evaluating AI agents' ability to handle underspecified instructions in software engineeri...
The article presents CodePDE, an innovative framework leveraging large language models (LLMs) for generating solvers for partial differen...
This paper presents a Bayesian model that unifies logical reasoning and statistical learning, proposing a framework for human-like machin...
The paper presents AdaGC, a novel adaptive gradient clipping method aimed at enhancing training stability in large language model pretrai...
The paper explores how Test-Time Training (TTT) enhances transformer models as in-context learners, demonstrating significant efficiency ...
The paper presents Selective Chain-of-Thought (Selective CoT), a method to enhance medical question answering efficiency using large lang...
This paper introduces the 'Curse of Depth' in Large Language Models (LLMs), revealing that many deep layers are ineffective due to Pre-La...
NovaPlan introduces a framework for zero-shot long-horizon manipulation in robotics, integrating video language planning with geometrical...
StyleStream introduces a novel real-time zero-shot voice style conversion system that enhances voice synthesis by disentangling linguisti...
The paper presents vCache, a verified semantic prompt caching system that enhances LLM inference efficiency by dynamically adjusting simi...
This article examines the performance of multilingual large language models (LLMs) across various languages, revealing that comprehension...
The LLMbda Calculus introduces a formal framework for understanding AI agents' conversations, addressing vulnerabilities like prompt inje...
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