Search: “models”

arXiv:2604.18311v1 Announce Type: new Abstract: Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. T

arXiv:2604.17219v1 Announce Type: cross Abstract: We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters ob

arXiv:2604.17570v1 Announce Type: new Abstract: Peripheral Blood Smear (PBS) is a critical microscopic examination in hematopathology that yields whole-slide imaging (WSI). Unlike solid tissue patholo

arXiv:2604.17831v1 Announce Type: new Abstract: Neural surface reconstruction methods typically treat camera poses as fixed values, assuming perfect accuracy from Structure-from-Motion (SfM) systems.

arXiv:2604.17800v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observat

arXiv:2604.17175v1 Announce Type: new Abstract: We introduce RosettaSearch, an inference-time multi-objective optimization approach for protein sequence optimization. We use large language models (LLM

arXiv:2604.16362v1 Announce Type: cross Abstract: Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography,

arXiv:2604.17739v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments

arXiv:2604.18264v1 Announce Type: new Abstract: Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. Howe

arXiv:2604.16259v1 Announce Type: cross Abstract: Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into their train

arXiv:2510.24328v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal co

arXiv:2604.15503v1 Announce Type: new Abstract: Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language

arXiv:2604.15521v1 Announce Type: new Abstract: Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively

arXiv:2604.15671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential for embodied decision-making; however, their application in complex chemical

arXiv:2604.16146v1 Announce Type: new Abstract: Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned)

arXiv:2604.15494v1 Announce Type: cross Abstract: Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balan

arXiv:2604.15736v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) excel at generic video understanding, their ability to support specialized, rule-grounded decision-maki

arXiv:2510.27617v2 Announce Type: replace Abstract: Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show prom

arXiv:2604.14191v1 Announce Type: new Abstract: State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher thro

arXiv:2602.06930v2 Announce Type: replace Abstract: We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. W

arXiv:2506.19807v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an in

arXiv:2604.14922v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent ad

arXiv:2604.15093v1 Announce Type: cross Abstract: Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achie

arXiv:2604.14324v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit hallucinations due to their inability to accurately perceive their own knowledge boundaries. Existing abstent

arXiv:2604.13474v1 Announce Type: cross Abstract: We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering bot

arXiv:2604.14379v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences, typically by optimizing a single rewa

arXiv:2604.14631v1 Announce Type: new Abstract: Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan. Existing app

arXiv:2601.12145v2 Announce Type: replace Abstract: Softmax attention struggles with long contexts due to structural limitations: the strict sum-to-one constraint forces attention sinks on irrelevant

arXiv:2604.13448v1 Announce Type: new Abstract: Human-object interaction (HOI) detection aims to detect interactions between humans and objects in images. While recent advances have improved performan

arXiv:2604.13459v1 Announce Type: new Abstract: Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Use

arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen

arXiv:2604.13891v1 Announce Type: new Abstract: Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency.

arXiv:2604.14030v1 Announce Type: new Abstract: Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1)

arXiv:2604.14137v1 Announce Type: new Abstract: Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real-world usefulness. Instead, users often rely on ``vibe-testing'':

arXiv:2604.14140v1 Announce Type: new Abstract: As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An

arXiv:2604.13756v1 Announce Type: new Abstract: The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation framework

arXiv:2604.13328v1 Announce Type: new Abstract: Pathology reports serve as the definitive record for breast cancer staging, yet their unstructured format impedes large-scale data curation. While Large

arXiv:2604.13538v1 Announce Type: new Abstract: Using responses generated by high-performing large language models (LLMs) for instruction tuning has become a widely adopted approach. However, the exis

arXiv:2604.14116v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, s

arXiv:2512.14234v2 Announce Type: replace Abstract: Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human

arXiv:2604.12191v1 Announce Type: new Abstract: Current evaluations of large language models aggregate performance across diverse tasks into single scores. This obscures fine-grained ability variation

arXiv:2603.20475v3 Announce Type: replace Abstract: Standard attribution heatmaps show where a vision-language model (VLM) focuses, but they do not reveal whether the recovered evidence is organized b

arXiv:2604.12035v1 Announce Type: new Abstract: Visual token pruning is a widely used strategy for efficient inference in multimodal large language models (MLLMs), but existing work mainly evaluates i

arXiv:2604.12403v1 Announce Type: new Abstract: Test-Time Prompt Tuning (TPT) adapts vision-language models using augmented views, but its effectiveness is hindered by the challenge of determining whi

arXiv:2604.12780v1 Announce Type: cross Abstract: Vision Transformer (ViT) models have achieved remarkable performance across various vision tasks, with scalability being a key advantage when applied

arXiv:2604.12837v1 Announce Type: new Abstract: Visual SLAM algorithms achieve significant improvements through the exploration of 3D Gaussian Splatting (3DGS) representations, particularly in generat