All Articles
18316 articles total
PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
PerceptionDLM is a multimodal diffusion language model that enables efficient parallel region perception and captioning in images.
Review of Machine Learning Models for Solar Energetic Particle Prediction
A review of machine learning models and datasets used for predicting solar energetic particle events to safeguard space technology.
GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
GDGU is a gradient difference-based graph unlearning method designed for privacy-compliant cyberattack localization in EV charging networks.
Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification
A systematic study on how feature extraction parameters significantly affect the accuracy of acoustic gunshot classification models.
FlowFake: Liquid Networks for Audio Deepfake Detection
FlowFake uses a Liquid Time-Constant architecture to detect audio deepfakes with high efficiency and superior cross-dataset generalization.
A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
A technical report on a BART-based hierarchical strategy for abstractive multi-document summarization of the Vietnamese language.
IHBench: Evaluating Post-Interruption Recovery in Voice Agents with Structured Workflows
IHBench is a new benchmark for evaluating how voice agents recover after user interruptions in structured enterprise workflows.
PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets
PrefSQA is a preference-based prediction method for speech quality assessment that reduces labeling noise compared to traditional MOS scores.
Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement
A new hardware generation framework that uses LLMs and formal methods to convert design specifications into verifiable RTL programs, reducing hallucinations in chip design.
Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework
A protocol-driven framework for agentic AI that binds SBOM and AIBOM artifacts to runtime telemetry to automate CSAF-VEX security advisories.
VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving
VERITAS is a zero-shot framework for formal theorem proving that uses a critic-guided MCTS pass to utilize verifier signals for improved proof search.
JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis
JustDiag is a diagnostic justification engine for root cause analysis that emphasizes process accountability over simple fluent answers in high-stakes operations.
Playful Agentic Robot Learning
Introduces RATs (Robotics Agent Teams) that use self-directed play to build a reusable code skill library for improved robot learning and task execution.
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
A billion-parameter generative foundation model for chest radiographs using Rectified Flow Transformers to improve clinical data diversity and diagnostic robustness.
Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix
Analyzes 'Secure Coding Drift' in Post-Quantum Cryptography development due to LLM reliance and proposes a gamified framework to mitigate these risks.
Can In-Context Learning Support Intrinsic Curiosity?
Research on whether in-context learning can support intrinsic curiosity in machine learning by using prediction errors to drive data collection policies.
Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks
Concept Flow Models (CFMs) introduce a hierarchical bottleneck for concept-based reasoning to reduce information leakage and improve model interpretability.
Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices
A suite of memory-reduction techniques for LoRA fine-tuning of LLMs on edge devices, achieving up to 28x reduction in peak memory.
Ten years of ClickHouse in open source
A discussion on the ten-year journey of ClickHouse as an open-source project.
ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification
ProMUSE is a staged evidential network for Alzheimer's classification that adaptively determines when expensive MRI or PET imaging is needed, reducing costs while maintaining accuracy.