2055 papers
The paper proposes CLoE, a consistency-driven framework that enhances missing-modality medical image segmentation by enforcing decision-level agreement among modality experts on both global and clinically critical foreground regions, thereby improving robustness and generalization compared to state-of-the-art methods.
The paper proposes DenoiseSplat, a feed-forward 3D Gaussian splatting method that achieves robust novel-view synthesis from noisy multi-view images by training end-to-end on a newly constructed noisy-clean benchmark without requiring 3D ground truth.
This paper introduces DendroNN, a novel dendrocentric neural network that leverages non-differentiable sequence detection and a rewiring phase to efficiently classify event-based spatiotemporal data, achieving competitive accuracy with up to 4x higher energy efficiency than state-of-the-art neuromorphic hardware through a dedicated asynchronous digital architecture.
This study presents a comprehensive multi-model deep learning approach that integrates pre-trained and custom neural networks with advanced data augmentation and transfer learning techniques to enhance autonomous driving capabilities by effectively addressing traffic sign classification, vehicle and lane detection, and behavioral cloning across diverse datasets.
BridgeDiff is a diffusion-based framework that improves virtual try-off by bridging the gap between on-body observations and flat-garment synthesis through a Garment Condition Bridge Module for robust appearance inference and a Flat Structure Constraint Module for enhanced structural stability.
This paper presents a scalable, reinforcement learning-driven simulation framework featuring a full-body musculoskeletal model that enables the quantitative co-optimization of robotic structural design and control policies by providing direct access to internal human biomechanical metrics for interactive robotics.
This paper investigates emotion as a latent factor influencing LLM attention and reasoning, introducing the AURA-QA dataset and an emotional regularization framework that demonstrably improves reading comprehension performance across both emotionally varying and standard benchmarks.
Latent-DARM is a novel latent-space communication framework that bridges Discrete Diffusion Language Models for global planning and Autoregressive Models for fluent execution, significantly improving reasoning accuracy on benchmarks like DART-5 and AIME2024 while drastically reducing token usage compared to state-of-the-art reasoning models.
DuplexCascade is a VAD-free, cascaded ASR-LLM-TTS pipeline that enables full-duplex speech-to-speech dialogue by converting long turns into micro-turns and utilizing special control tokens to coordinate turn-taking while preserving the conversational intelligence of large language models.
This paper proposes a physics-informed generative modeling framework that derives a differentiable, distributional traffic dynamics model from stochastic Ito-type equations, enabling the estimation of traffic density distributions, credible intervals, and congestion risks through a score network trained with denoising score matching and Fokker-Planck residual loss.
This paper introduces AlphaEvolve, an LLM-based code mutation agent that serves as a unified meta-algorithm to improve the lower bounds of five classical Ramsey numbers and successfully recover or match existing bounds across various cases, demonstrating a shift from bespoke search methods to a single, adaptable framework for combinatorial structure generation.
ZeroWBC is a novel framework that enables natural, versatile whole-body control for humanoid robots by learning visuomotor policies directly from human egocentric videos, thereby eliminating the need for expensive and time-consuming teleoperation data collection.
The paper proposes GIAT, a novel Geologically-Informed Attention Transformer that integrates Category-Wise Sequence Correlation filters into the self-attention mechanism to guide lithology identification with geological priors, achieving state-of-the-art accuracy and enhanced interpretability on well log datasets.
This paper proposes a cost-effective framework that leverages structurally informative but functionally imperfect LLM-generated RTL to train netlist representation models, effectively overcoming data scarcity and outperforming methods reliant on scarce high-quality labeled datasets.
RubiCap introduces a novel reinforcement learning framework that leverages LLM-generated rubrics to create structured, multi-faceted reward signals for dense image captioning, thereby overcoming the limitations of supervised distillation and deterministic checkers to achieve state-of-the-art performance and superior word efficiency across various benchmarks.
This paper proposes a Probability of Necessity and Sufficiency (PNS)-based regularization method for Class-Incremental Learning that utilizes a dual-scope counterfactual generator to mitigate feature collisions caused by intra-task shortcut reliance and inter-task semantic confusion, thereby ensuring both the causal completeness and separability of task-specific representations.
The paper proposes QUSR, a novel diffusion-based image super-resolution model that combines an Uncertainty-Guided Noise Generation module to adaptively perturb high-uncertainty regions and a Quality-Aware Prior leveraging Multimodal Large Language Models to guide restoration, thereby achieving high-fidelity results in real-world scenarios with unknown and non-uniform degradations.
DexHiL is the first integrated human-in-the-loop framework for dexterous Vision-Language-Action models that combines coordinated arm-hand teleoperation with intervention-aware data sampling to significantly improve post-training performance and reliability in complex manipulation tasks.
The paper introduces PM-Nav, a novel framework that leverages priori-semantic maps and hierarchical chain-of-thought prompting to overcome the challenges of language-driven navigation in functional buildings with highly similar features, achieving substantial performance improvements over existing methods in both simulation and real-world environments.
VIVID-Med introduces a novel framework that leverages a frozen large language model as a structured semantic teacher to pretrain lightweight, deployable medical Vision Transformers via a Unified Medical Schema and Structured Prediction Decomposition, achieving state-of-the-art performance across diverse medical imaging tasks with significantly reduced data requirements compared to existing vision-language models.