11673 papers
This paper proposes a neural dynamics-informed pre-trained framework that overcomes the limitations of traditional atlas-based methods by extracting personalized neural activity representations to guide brain parcellation and correlation estimation, thereby achieving superior performance in constructing personalized brain functional networks across heterogeneous scenarios.
This paper introduces UniLongGen, a training-free inference strategy that improves long-horizon interleaved image generation by dynamically curating context to discard accumulated visual noise, thereby overcoming the reliability collapse caused by dense visual token interference in unified multimodal models.
DreamSAC is a framework that enhances extrapolative generalization in physics simulations by combining an unsupervised symmetry exploration strategy, which actively probes conservation laws via a Hamiltonian-based curiosity bonus, with a Hamiltonian-based world model that learns invariant physical states from raw observations through a novel contrastive objective.
The paper introduces COOL-MC, a framework that verifies and explains reinforcement learning policies for multi-bridge network maintenance by applying probabilistic model checking and explainability methods to a PRISM-encoded Markov decision process, thereby revealing safety violation probabilities and systematic policy biases.
This paper proposes a learning-free text-to-speech framework that generates L2-accented speech by applying phonological rules to phoneme sequences within a multilingual TTS model, enabling explicit accent control without requiring large-scale accented training datasets.
This paper introduces a novel Speech Generation Speaker Poisoning (SGSP) framework to address privacy risks in zero-shot text-to-speech by modifying trained models to prevent the generation of specific speaker identities while maintaining utility for others, demonstrating effective protection for up to 15 speakers but revealing scalability challenges with larger sets due to identity overlap.
This paper introduces Nw\=ach\=a Mun\=a, the first manually transcribed Devanagari speech corpus for the endangered Nepal Bhasha, and demonstrates that proximal cross-lingual transfer from Nepali achieves competitive automatic speech recognition performance comparable to large multilingual models while being significantly more computationally efficient.
The paper proposes GRD-Net, a novel architecture combining a generative adversarial network with a region-of-interest attention module to improve industrial surface anomaly detection and localization by learning from normal products and synthetic defects while focusing on relevant areas, thereby reducing reliance on biased post-processing algorithms.
This paper systematically evaluates four training objectives—Cross-Entropy, Prototype, Triplet, and Average Precision Losses—for out-of-distribution detection in image classification, revealing that while they achieve comparable in-distribution accuracy, Cross-Entropy Loss delivers the most consistent performance across both near- and far-OOD scenarios under standardized protocols.
This paper presents a semi-supervised deep generative anomaly detection framework, utilizing a residual autoencoder with a dense bottleneck, that achieves high-accuracy, real-time defect detection and localization on high-speed pharmaceutical Blow-Fill-Seal production lines while operating within strict 500 ms timing constraints.
The paper proposes Difficulty-Scaled Segment-Wise GRPO (DSS-GRPO), a reinforcement learning method that decomposes training signals into separate "think" and "answer" segments with difficulty-aware scaling to compress reasoning traces without compromising answer quality.
The paper proposes Staged Multi-Agent Training (SMAT), a four-stage curriculum that progressively trains a human-exoskeleton system to achieve stable co-adaptation, resulting in a control policy that significantly reduces hip muscle activation and delivers consistent, positive mechanical power across diverse users without requiring subject-specific retraining.
This paper proposes a high-fidelity synthetic data generation pipeline using NVIDIA Omniverse to address data scarcity and privacy constraints in airport logistics, demonstrating that mixed training with synthetic data and only 40% of real annotations achieves performance comparable to full real-data baselines while reducing annotation effort by 25–35%.
The paper proposes AtomicVLA, a unified planning-and-execution framework that utilizes a Skill-Guided Mixture-of-Experts architecture to dynamically compose atomic skill abstractions, thereby significantly improving scalability and performance in long-horizon robotic manipulation and continual learning tasks compared to existing monolithic VLA models.
Ref-DGS is an efficient, rasterization-based framework that achieves state-of-the-art novel view synthesis on reflective scenes by decoupling surface geometry from specular reflections using a dual Gaussian representation and a lightweight adaptive mixing shader, thereby avoiding the high computational cost of explicit ray tracing.
This paper presents an AI-driven workflow combining a Gaussian mixture variational autoencoder with Pearson correlation coefficients to analyze sparsely sampled X-ray hyperspectral data, enabling the generation of nanometer-resolution multiphase maps that reveal complex phase heterogeneity and transition zones in individual Na-ion cathode particles during electrochemical cycling.
This survey provides a comprehensive overview of memory mechanisms in autonomous LLM agents from 2022 to early 2026, formalizing a write–manage–read framework, introducing a three-dimensional taxonomy, analyzing key mechanisms and evaluation benchmarks, and outlining critical applications and future challenges.
This paper proposes CDA-VSR, a compressed-domain-aware online video super-resolution network that leverages motion vectors, residual maps, and frame types to achieve real-time, high-quality reconstruction with significantly reduced computational cost compared to state-of-the-art methods.
TDM-R1 introduces a novel reinforcement learning paradigm that enables few-step diffusion models to effectively incorporate non-differentiable rewards by decoupling surrogate reward learning from generator training, achieving state-of-the-art performance across various metrics and scaling to powerful models like Z-Image with only 4 inference steps.
VoiceSHIELD-Small is a lightweight, real-time model built on Whisper-small that simultaneously transcribes speech and detects malicious content with 99.16% accuracy, offering a faster and more secure alternative to traditional text-based filtering for voice AI systems.