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Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation
Potemkin Understanding in Large Language Models
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs
How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Arabic Dialect Classification using RNNs, Transformers, and Large Language Models: A Comparative Analysis
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
GLIMPSE: Gradient-Layer Importance Mapping for Prompted Visual Saliency Explanation for Generative LVLMs
Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Generalizing vision-language models to novel domains: A comprehensive survey
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
AI-Generated Song Detection via Lyrics Transcripts
KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
Value-Free Policy Optimization via Reward Partitioning
VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
Enabling Precise Topic Alignment in Large Language Models Via Sparse Autoencoders
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
StepProof: Step-by-step verification of natural language mathematical proofs
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed
VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code
Multi Layered Autonomy and AI Ecologies in Robotic Art Installations
Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
Quantum computing and artificial intelligence: status and perspectives
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization
A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech
WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data
Error Optimization: Overcoming Exponential Signal Decay in Deep Predictive Coding Networks
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
Super-Resolution Generative Adversarial Networks based Video Enhancement
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix
INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models
SConU: Selective Conformal Uncertainty in Large Language Models
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation
Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization
Achieving binary weight and activation for LLMs using Post-Training Quantization
A Consequentialist Critique of Binary Classification Evaluation Practices
Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
Test-Time Reasoning Through Visual Human Preferences with VLMs and Soft Rewards
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
Automating Adjudication of Cardiovascular Events Using Large Language Models
ATTENTION2D: Communication Efficient Distributed Self-Attention Mechanism
Visual Position Prompt for MLLM ベースの Visual Grounding
Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI
Characterizing GPU Resilience and Impact on AI/HPC Systems
Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
The Problem of the Priors, or Posteriors?
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy
What can large language models do for sustainable food?
Enough Coin Flips Can Make LLMs Act Bayesian
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning
HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning
LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention
No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning
A general language model for peptide identification
Cluster and Predict Latent Patches for Improved Masked Image Modeling
Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
Mechanistic Interpretability of Emotion Inference in Large Language Models
Multimodal Medical Code Tokenizer
Time to Rethink AI for Combinatorial Optimization: Classical Algorithms Remain Tough to Match
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Environment-Driven Online LiDAR-Camera Extrinsic Calibration
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
A Library for Learning Neural Operators
ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality
Pretrained Reversible Generation as Unsupervised Visual Representation Learning
FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Foundation Models for Wearable Movement Data in Mental Health Research
GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
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Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization
Created by
Haebom
作者
Gen Li, Yang Xiao, Jie Ji, Kaiyuan Deng, Bo Hui, Linke Guo, Xiaolong Ma
概要
テキスト画像（T2I）拡散モデルは高品質の画像生成に大きな成功を収めていますが、膨大な知識を保存する能力のために、著作権コンテンツの削除、偏りの軽減、または有害な概念の削除など、選択的な忘却が必要な場合に問題が発生します。従来のアンランニング方法は特定の概念を排除することができますが、不安定性、残りの知識の持続、および生成品質の低下により、複数の概念の忘却に困難があります。この問題を解決するために、この論文では、拡散モデルにおける多概念忘却のための新しいアンランニングフレームワークである
Dynamic MaskとConcept-Aware Loss
を提案します。
ダイナミックマスク
メカニズムは、現在の最適化状態に応じて勾配マスクを適応的に更新し、無関係な知識との干渉を防ぐためのオプションの重み付け修正を可能にします。さらに、
Concept-Aware Loss
は上位クラスのソートを通じて意味的な一貫性を高め、アンランニングプロセスを明示的に案内しますが、知識蒸留ベースの規制損失は、以前にアンランニングされた概念がシーケンシャルアンランニング中に忘れられないようにします。広範な実験で提案された方法を評価した結果、特にマルチコンセプトシナリオでは、従来のアンランニング技術よりも忘却効果、出力忠実度、および意味的一貫性の点で優れた性能が得られました。この研究は、生成モデルで信頼性が高く高忠実度のアンランニングのための原則的で柔軟なフレームワークを提供します。コードは公に配布される予定です。
Takeaways、Limitations
•
Takeaways：
◦
多概念忘却問題を効果的に解決する新しいアンランニングフレームワークの提示
◦
Dynamic Mask と Concept-Aware Loss により安定的かつ高品質なランニングを実現
◦
著作権問題、偏向低減、有害概念の除去など様々な分野に適用可能性を提示
◦
公開コード提供による研究の再現性と拡張性の確保
•
Limitations：
◦
提案された方法の一般化性能に関するさらなる研究の必要性
◦
さまざまな拡散モデルとデータセットに幅広い実験が必要
◦
Concept-Aware Lossの上位クラスソート方式の限界と改善可能性の探求
◦
実際のアプリケーションで発生する可能性がある追加の問題と解決策の研究が必要
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