Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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Distillation Robustifies Unlearning

Cartridges: Lightweight and general-purpose long context representations via self-study

Text-to-LoRA: Instant Transformer Adaption

Information Bargaining: Bilateral Commitment in Bayesian Persuasion

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models

Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery

TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood

A Red Teaming Roadmap Towards System-Level Safety

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation

Context Is Not Comprehension

Feature-Based Lie Group Transformer for Real-World Applications

Horizon Reduction Makes RL Scalable

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO AMD Progression Challenge

CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

Rethinking the effects of data contamination in Code Intelligence

MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping

Protap: A Benchmark for Protein Modeling on Realistic Downstream Applications

NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction

SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race

Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws

Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert

Large Language Models Often Know When They Are Being Evaluated

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation

PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation

RainFusion: Adaptive Video Generation Acceleration via Multi-Dimensional Visual Redundancy

VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation

When Two LLMs Debate, Both Think They'll Win

Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias

GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains

Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps

Sample Complexity of Diffusion Model Training Without Empirical Risk Minimizer Access

EVADE: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications

Simulating Macroeconomic Expectations using LLM Agents

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

Mechanistic evaluation of Transformers and state space models

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

Pel, A Programming Language for Orchestrating AI Agents

MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models

Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning

BLEUBERI: BLEU is a surprisingly effective reward for instruction following

Position: We Need Responsible, Application-Driven (RAD) AI Research

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach

LookAlike: Consistent Distractor Generation in Math MCQs

Tree-Sliced Wasserstein Distance with Nonlinear Projection

Test-time Correlation Alignment

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

MIB: A Mechanistic Interpretability Benchmark

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization

Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure

Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation

sudo rm -rf agentic_security

Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models

Towards Achieving Perfect Multimodal Alignment

nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning

FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA

RONA: Pragmatically Diverse Image Captioning with Coherence Relations

Unifying 2D and 3D Vision-Language Understanding

Revisiting semi-supervised learning in the era of foundation models

AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation

Generalized Interpolating Discrete Diffusion

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models

Straight-Line Diffusion Model for Efficient 3D Molecular Generation

Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach

Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets

Dialogue Without Limits: Constant-Sized KV Caches for Extended Responses in LLMs

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement

from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

EgoNormia: Benchmarking Physical Social Norm Understanding

PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation

NeoBERT: A Next-Generation BERT

From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients

AMPO: Active Multi-Preference Optimization for Self-play Preference Selection

SYNTHIA: Novel Concept Design with Affordance Composition

DBudgetKV: Dynamic Budget in KV Cache Compression for Ensuring Optimal Performance

AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay

DISC: DISC: Dynamic Decomposition Improves LLM Inference Scaling

Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach

Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G

MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding

Improving the Diffusability of Autoencoders

Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis

MMTEB: Massive Multilingual Text Embedding Benchmark

From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MARKERGEN

How Expressive are Knowledge Graph Foundation Models?

Machine Learning Should Maximize Welfare, but Not by (Only) Maximizing Accuracy

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging

Created by

Haebom

저자

Xuanru Zhou, Jiarun Liu, Shoujun Yu, Hao Yang, Cheng Li, Tao Tan, Shanshan Wang

개요

TSSC-Net은 의료 영상에서 빠른 움직임이 있는 4D MRI 데이터의 시간적 및 공간적 해상도를 향상시키는 새로운 프레임워크입니다. 기존의 registration-based interpolation 방식의 한계를 극복하기 위해, 시작 프레임과 끝 프레임을 기준으로 확산 기반의 시간적 초해상도 네트워크를 사용하여 6배의 시간적 초해상도를 단일 추론 단계에서 달성합니다. 또한, 새로운 삼방향 Mamba 기반 모듈을 도입하여 슬라이스 간의 정렬 오류를 해결하고, 부피 일관성을 향상시켜 공간적 일관성을 유지합니다. ACDC 심장 MRI 데이터셋과 실제 4D 무릎 관절 데이터셋을 사용한 실험 결과, TSSC-Net이 빠른 움직임 데이터에서 구조적 충실도와 공간적 일관성을 유지하면서 고해상도 동적 MRI를 생성하는 것을 보여줍니다.

시사점, 한계점

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시사점:

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빠른 움직임이 있는 4D MRI 데이터에서 시간적 및 공간적 해상도를 효과적으로 향상시키는 새로운 방법 제시.

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단일 추론 단계에서 6배의 시간적 초해상도 달성.

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슬라이스 간 정렬 오류를 해결하고 부피 일관성을 향상시키는 삼방향 Mamba 기반 모듈의 효과적인 활용.

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실제 데이터셋에서의 성능 검증을 통해 실용성을 입증.

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한계점:

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제시된 방법의 계산 비용 및 메모리 사용량에 대한 자세한 분석 부족.

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다양한 유형의 4D MRI 데이터에 대한 일반화 성능에 대한 추가 연구 필요.

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다른 초해상도 기법들과의 비교 분석이 더욱 상세하게 필요.

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