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전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

VGR: Visual Grounded Reasoning

Improving Multimodal Learning Balance and Sufficiency through Data Remixing

Autonomous Computer Vision Development with Agentic AI

A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation

Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models

From Reasoning to Code: GRPO Optimization for Underrepresented Languages

Farseer: A Refined Scaling Law in Large Language Models

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors

VideoDeepResearch: Long Video Understanding With Agentic Tool Using

Data Shifts Hurt CoT: A Theoretical Study

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations

Disclosure Audits for LLM Agents

A Survey of Generative Categories and Techniques in Multimodal Large Language Models

Resa: Transparent Reasoning Models via SAEs

EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

Latent Multi-Head Attention for Small Language Models

Intra-Trajectory Consistency for Reward Modeling

Foundation Models in Medical Imaging -- A Review and Outlook

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model

Segment Concealed Objects with Incomplete Supervision

Inherently Faithful Attention Maps for Vision Transformers

DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression

Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework

Reparameterized LLM Training via Orthogonal Equivalence Transformation

Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries

LeVo: High-Quality Song Generation with Multi-Preference Alignment

FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement

SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments

MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks

Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

SAFE: Finding Sparse and Flat Minima to Improve Pruning

Tactile MNIST: Benchmarking Active Tactile Perception

SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System

MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP

Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

From Argumentative Text to Argument Knowledge Graph: A New Framework for Structured Argumentation

An Incremental Framework for Topological Dialogue Semantics: Efficient Reasoning in Discrete Spaces

Diffusion Graph Neural Networks for Robustness in Olfaction Sensors and Datasets

COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning

R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

Quantum computing and artificial intelligence: status and perspectives

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks

On the performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning

SOSBENCH: Benchmarking Safety Alignment on Scientific Knowledge

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Transparency in Healthcare AI: Testing European Regulatory Provisions against Users' Transparency Needs

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision

Fixed Point Explainability

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

JAEGER: Dual-Level Humanoid Whole-Body Controller

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

AI Recommendations and Non-instrumental Image Concerns

Entropic Time Schedulers for Generative Diffusion Models

JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture

Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design

Scholar Inbox: Personalized Paper Recommendations for Scientists

On Synthesizing Data for Context Attribution in Question Answering

Evaluating how LLM annotations represent diverse views on contentious topics

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion

Transformers without Normalization

Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model

From Euler to AI: Unifying Formulas for Mathematical Constants

C2-DPO: Constrained Controlled Direct Preference Optimization

Less is More: Improving LLM Alignment via Preference Data Selection

Quantifying Memorization and Parametric Response Rates in Retrieval-Augmented Vision-Language Models

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series

Boosting Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

HARBOR: Exploring Persona Dynamics in Multi-Agent Competition

SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models

Truth Knows No Language: Evaluating Truthfulness Beyond English

PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification

Is attention all you need to solve the correlated electron problem?

Optimizing Temperature for Language Models with Multi-Sample Inference

Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training

The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search

Activation-Informed Merging of Large Language Models

Layer by Layer: Uncovering Hidden Representations in Language Models

Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss

An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology

Created by

Haebom

저자

Changchun Yang (Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Center of Excellence for Smart Health, Center of Excellence on Generative AI, King Abdullah University of Science and Technology), Weiqian Dai (Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine), Yilan Zhang (Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Center of Excellence for Smart Health, Center of Excellence on Generative AI, King Abdullah University of Science and Technology), Siyuan Chen (Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Center of Excellence for Smart Health, Center of Excellence on Generative AI, King Abdullah University of Science and Technology), Jingdong Hu (Smiltec), Junkai Su (Smiltec), Yuxuan Chen (Smiltec), Ao Xu (Smiltec), Na Li (Smiltec), Xin Gao (Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Center of Excellence for Smart Health, Center of Excellence on Generative AI, King Abdullah University of Science and Technology), Yongguo Yu (Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine)

개요

CHROMA는 84,000개 이상의 검체(약 4백만 장의 염색체 이미지)를 사용하여 자기 지도 학습 방식으로 사전 훈련된 세포유전체학 기반 모델입니다. 다양한 자원 조건을 포괄하는 포괄적인 데이터 세트의 부족으로 인해 자동화된 방법이 특정 작업에 국한되고 일반화가 어려운 기존의 한계를 극복하기 위해 설계되었습니다. 다양한 유형의 염색체 이상에 대한 일반화 가능한 표현을 학습하여 모든 유형의 이상에서 다른 방법보다 우수한 성능을 보이며, 특히 적은 양의 레이블이 지정된 데이터와 불균형 데이터 세트에서도 효과적입니다. CHROMA는 다양한 이상 유형에 걸쳐 불안정성과 클론 병변을 포괄적으로 매핑하여 신뢰할 수 있고 자동화된 임상 분석을 위한 확장 가능하고 일반화 가능한 솔루션을 제공하며, 전문가의 주석 작업량을 줄이고 희귀 유전체 이상의 조기 진단을 통해 정밀 종양학을 발전시킵니다. 이를 통해 광범위한 임상 AI 애플리케이션을 가능하게 하고 고급 유전체 분석에 대한 접근성을 높입니다.

시사점, 한계점

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시사점:

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다양한 염색체 이상 유형에 대한 일반화 가능한 AI 모델 제공

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기존 방법보다 우수한 성능 (특히 적은 양의 레이블 데이터 및 불균형 데이터 세트에서)

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전문가의 주석 작업량 감소

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희귀 유전체 이상의 조기 진단 및 정밀 종양학 발전

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광범위한 임상 AI 애플리케이션 및 고급 유전체 분석 접근성 향상

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한계점:

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본 논문에서는 구체적인 한계점이 언급되지 않았습니다. 추가 연구를 통해 실제 임상 환경에서의 성능 및 일반화 가능성에 대한 추가 평가가 필요합니다.

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