/
/
Daily Arxiv
Daily Arxiv
전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.
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
Load more
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Created by
Haebom
저자
Yuyan Ni, Shikun Feng, Haohan Chi, Bowen Zheng, Huan-ang Gao, Wei-Ying Ma, Zhi-Ming Ma, Yanyan Lan
개요
본 논문은 분자 생성에서 확산 기반 모델의 많은 샘플링 단계 문제를 해결하기 위해 직선 확산 모델(SLDM)을 제안합니다. SLDM은 확산 과정을 선형 궤적을 따르도록 공식화하여 분자 구조의 노이즈 민감도 특성과 잘 맞추고 생성 과정 전반에 걸쳐 재구성 노력을 고르게 분배함으로써 학습 효율과 효과를 향상시킵니다. 결과적으로 SLDM은 3D 분자 생성 벤치마크에서 최첨단 성능을 달성하며 샘플링 효율을 100배 향상시킵니다.
시사점, 한계점
•
시사점:
◦
분자 생성에서 확산 모델의 샘플링 효율을 획기적으로 개선 (100배 향상).
◦
선형 확산 과정을 통해 분자 구조의 노이즈 민감도 특성을 효과적으로 활용.
◦
학습 효율 및 생성 성능 향상.
◦
3D 분자 생성 분야에서 새로운 state-of-the-art 성능 달성.
•
한계점:
◦
제시된 모델의 일반화 성능에 대한 추가적인 검증 필요.
◦
다른 유형의 분자 또는 더 복잡한 분자 구조에 대한 적용 가능성에 대한 추가 연구 필요.
◦
선형 궤적 가정의 한계 및 다른 확산 경로와의 비교 분석 필요.
PDF 보기
Made with Slashpage
