4555 papers
The paper proposes EvolveReason, a self-evolving reasoning paradigm that combines a human-like chain-of-thought framework, a forgery latent-space distribution capture module, and a reinforcement learning-based self-evolution strategy to enhance the accuracy, detail, and reliability of explainable deepfake facial image identification.
InterReal is a unified physics-based imitation learning framework that enables humanoid robots to robustly learn and execute complex human-object interaction skills in real-world settings through a novel motion data augmentation scheme and an automatic reward learner.
The paper proposes GP-Tree, a novel in-memory spatial index that combines adaptive grid cells with a prefix tree structure to replace coarse minimum bounding rectangles with fine-grained approximations, thereby significantly improving filtering accuracy and query performance for complex spatial objects compared to traditional indexes.
This paper presents the first extensive study evaluating over 500 models to demonstrate that graph-based code representations consistently outperform other methods in predicting patch correctness, thereby significantly improving the effectiveness of automated program repair tools.
This paper introduces SketchGraphNet, a memory-efficient hybrid graph transformer that models free-hand sketches as structured graphs to achieve state-of-the-art recognition accuracy on the newly constructed 3.44-million-sample SketchGraph benchmark while significantly reducing computational resource requirements.
The paper presents ICLR, a novel framework that enhances in-context imitation learning for robots by augmenting demonstration prompts with structured visual reasoning traces and jointly training a unified autoregressive transformer to predict both future trajectories and actions, thereby improving success rates and generalization in complex manipulation tasks.
MIRO is a privacy-preserving framework that combines distributed particulate matter sensors with a multi-radar mmWave re-identification system, utilizing GAN-based view adaptation to track workers and estimate their specific exposure to airborne pollutants in industrial environments without relying on visual data.
The paper proposes ACCURATE, a robust 3D reconstruction framework that combines image segmentation with geometry-constrained topology traversal and dynamic programming to achieve high-accuracy reconstruction of arbitrary-shaped, deformable continuum bodies like guidewires and catheters under biplanar X-ray imaging.
This paper proposes a semantic geometric framework that leverages small vehicles as metric anchors within a decoupled stereoscopic projection model to recover absolute scale from monocular UAV images, thereby enabling scale-adaptive satellite image cropping and significantly improving cross-view geo-localization robustness under real-world scale ambiguity.
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.
The paper introduces CONSTANT, a novel one-shot handwriting generation framework that leverages Style-Aware Quantization and a latent patch-based contrastive objective within a diffusion model to overcome existing limitations in capturing diverse writer styles and generating high-quality, realistic handwritten images across multiple languages.
This paper evaluates the trade-off between privacy and Parkinson's disease detection in anonymized speech, demonstrating that while STT-TTS anonymization severely degrades diagnostic performance by erasing prosodic cues, kNN-VC effectively preserves macro-prosodic features to maintain high detection accuracy with only a minor performance drop.
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.
ReconDrive is a fast, feed-forward framework that adapts the VGGT foundation model with hybrid prediction heads and static-dynamic composition to achieve high-fidelity, scalable 4D Gaussian Splatting for autonomous driving scenes, outperforming existing feed-forward methods while matching the quality of slower optimization-based approaches.
This paper introduces a new deterministic symmetric recursive algorithm designed to solve mean-payoff games.
This paper introduces AgentRaft, an automated framework that combines program analysis and semantic reasoning to detect and quantify the systemic risk of Data Over-Exposure in LLM agents, demonstrating high accuracy and efficiency across thousands of real-world tools.
This paper proposes an active inference-based framework for micro-gesture recognition that utilizes Expected Free Energy-guided temporal sampling and uncertainty-aware adaptive learning to overcome challenges like low amplitude, noise, and inter-subject variability, demonstrating significant performance improvements on the SMG dataset.
This paper introduces StageFinder, a temporal graph learning framework that fuses host and network provenance data with graph neural networks and LSTMs to accurately and stably estimate Advanced Persistent Threat (APT) attack stages, achieving a macro F1-score of 0.96 on DARPA datasets.
PureCC is a novel concept customization framework that employs a decoupled learning objective and a dual-branch training pipeline to achieve high-fidelity text-to-image personalization while effectively preserving the original model's behavior and capabilities.
Brain-WM is a pioneering brain glioblastoma world model that utilizes a novel Y-shaped Mixture-of-Transformers architecture to unify next-step treatment prediction and future MRI generation, effectively capturing the co-evolutionary dynamics between tumor progression and treatment response to optimize clinical outcomes.