3852 papers
AlphaFlowTSE is a one-step conditional generative model for target speaker extraction that utilizes a JVP-free AlphaFlow objective and interval-consistency training to achieve high-fidelity speech recovery with low latency and improved generalization for downstream ASR tasks.
This paper introduces PV-VASM, a model-agnostic probabilistic framework that provides formal robustness guarantees and estimates misclassification probabilities for voice anti-spoofing models against various speech synthesis attacks and unseen perturbations.
This paper proposes CTCNet, a novel cross-spectral guided network featuring a Prototype-Guided Knowledge Embedding module and a Quality-Aware Spectral Compensation module to enhance UAV traffic scene understanding under adverse conditions, accompanied by the introduction of Traffic-VQA, the first large-scale optical-thermal benchmark for cognitive traffic analysis.
This paper introduces HIR-SDD, a novel speech deepfake detection framework that leverages Large Audio Language Models and a human-annotated dataset to achieve robust generalization across audio domains while providing interpretable, human-like reasoning for its predictions.
CUPID is a novel, plug-in framework that enables joint estimation of aleatoric and epistemic uncertainty in pretrained deep learning models without requiring retraining or architectural modifications, thereby enhancing interpretability and trust in high-stakes AI applications.
This paper proposes a deep learning-based autoencoder architecture for the Randomized Distributed Function Computation (RDFC) framework that minimizes the total variation distance to an unknown target distribution using only data samples, demonstrating superior communication efficiency compared to traditional data compression methods, particularly under limited common randomness.
This paper introduces "Super Neurons," a training-free method that leverages scalar activations from the first generated token to create highly accurate classifiers for categorical VQA, enabling extreme early exiting from the first layer and achieving up to a 5.10x speedup while improving performance over the original network.
This paper proposes an AI-driven framework that improves 5G/6G traffic demand prediction accuracy and spatial generalization by employing a context-aware two-stage splitting strategy and residual error correction to mitigate neighborhood leakage, as validated by experiments across five Canadian cities.
This paper proposes a hierarchical Graph Attention Network (HR-GAT) model that leverages public deployment records to accurately estimate fine-grained spectrum demand across multiple cities, significantly outperforming existing baselines and providing regulators with actionable insights for efficient spectrum sharing and allocation.
This paper introduces a risk-aware evaluation framework for Large Language Models in financial services, featuring a domain-specific taxonomy, an automated multi-round red-teaming pipeline, and a Risk-Adjusted Harm Score (RAHS) metric to better capture and quantify severe, operationally actionable security failures that traditional domain-agnostic benchmarks miss.
This paper proposes Nurture-First Development (NFD), a paradigm that shifts AI agent creation from static engineering to a continuous, conversational co-evolution process with domain experts, utilizing a Knowledge Crystallization Cycle to progressively transform tacit operational dialogue into structured, reusable expertise.
This paper introduces MCCOP, a framework that leverages a pretrained diffusion model within a joint sequence-structure latent space to generate minimal, biologically plausible protein mutations that flip predictive model outcomes to desired states, thereby bridging the gap between deep learning predictions and actionable protein engineering.
This paper evaluates the weak speaker verification capabilities of existing speech-aware large language models and proposes a lightweight augmentation method using frozen ECAPA-TDNN embeddings and LoRA adapters to significantly enhance speaker discrimination while preserving natural language interfaces.
This paper introduces BALD-SAM, a principled framework that adapts Bayesian Active Learning by Disagreement to spatial prompt selection in interactive segmentation, enabling a lightweight uncertainty estimation head on frozen foundation models to significantly outperform human and oracle prompting across diverse domains.
This paper critiques the instability and ambiguity of current cue-conflict benchmarks for measuring neural network shape-texture bias and introduces REFINED-BIAS, a new framework with balanced cue pairs and ranking-based metrics to enable reliable, interpretable, and fair cross-model comparisons.
This paper introduces a novel, low-complexity Human Presence Detection system for commodity laptops that utilizes the built-in Wi-Fi hardware and a new Range-Filtered Doppler Spectrum technique to achieve privacy-preserving, calibration-free occupancy sensing without requiring external sensors or infrastructure.
The paper introduces EvoKernel, a self-evolving agentic framework that leverages value-driven memory and reinforcement learning to overcome data scarcity in NPU kernel synthesis, significantly improving model correctness and achieving substantial speedups through automated drafting and iterative refinement.
This paper introduces a Semantic Landmark Particle Filter (SLPF) that enhances robot localisation in vineyards by integrating trunk and pole detections with LiDAR and GNSS to overcome perceptual aliasing caused by parallel crop rows, achieving significantly lower pose errors and improved row correctness compared to existing geometry-only, vision-based, and GNSS-only baselines.
The paper proposes , a novel method that dynamically fuses a Generalist Value Model's prior with sparse RL rollouts via real-time statistical testing to minimize baseline estimation error, thereby achieving faster convergence and over 10% performance gains on mathematical reasoning benchmarks compared to GRPO and DAPO.
This paper introduces GRACE, a unified 2D simulator and benchmark that enables transparent, reproducible comparisons of multi-robot path planning algorithms across grid, roadmap, and continuous environments by standardizing task instantiation, execution, and evaluation protocols.