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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.
DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift
Holistic Tokenizer for Autoregressive Image Generation
MAGIC: Mask-Guided Diffusion Inpainting with Multi-Level Perturbations and Context-Aware Alignment for Few-Shot Anomaly Generation
Order Acquisition Under Competitive Pressure: A Rapidly Adaptive Reinforcement Learning Approach for Ride-Hailing Subsidy Strategies
Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
GPU-based complete search for nonlinear minimization subject to bounds
Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization
On-Policy Optimization of ANFIS Policies Using Proximal Policy Optimization
Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
Horus: A Protocol for Trustless Delegation Under Uncertainty
Visual Anagrams Reveal Hidden Differences in Holistic Shape Processing Across Vision Models
Diversity Conscious Refined Random Forest
GLU Attention Improve Transformer
Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections
CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation
Treatment, evidence, imitation, and chat
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
Towards Understanding the Cognitive Habits of Large Reasoning Models
SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summary
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Discrete Diffusion in Large Language and Multimodal Models: A Survey
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model
The Geometries of Truth Are Orthogonal Across Tasks
TrajFlow: Multi-modal Motion Prediction via Flow Matching
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Making a Pipeline Production-Ready: Challenges and Lessons Learned in the Healthcare Domain
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Recommender systems, stigmergy, and the tyranny of popularity
HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
RewardAnything: Generalizable Principle-Following Reward Models
BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance
A Quantum Information Theoretic Approach to Tractable Probabilistic Models
Playing with Transformer at 30+ FPS via Next-Frame Diffusion
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals
Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Domain Adaptation of VLM for Soccer Video Understanding
Towards Universal Semantics With Large Language Models
ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
An Exploration of Default Images in Text-to-Image Generation
Aggregating Concepts of Fairness and Accuracy in Prediction Algorithms
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling
Explainable Coarse-to-Fine Ancient Manuscript Duplicates Discovery
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
The Role of Open-Source LLMs in Shaping the Future of GeoAI
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
AI for the Open-World: the Learning Principles
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
TerraMind: Large-Scale Generative Multimodality for Earth Observation
NativQA Framework: Enabling LLMs with Native, Local, and Everyday Knowledge
Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward
EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition
Construction Identification and Disambiguation Using BERT: A Case Study of NPN
PVChat: Personalized Video Chat with One-Shot Learning
Neural Discrete Token Representation Learning for Extreme Token Reduction in Video Large Language Models
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
Long Context Modeling with Ranked Memory-Augmented Retrieval
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Language Models can Self-Improve at State-Value Estimation for Better Search
Offline RLAIF: Piloting VLM Feedback for RL via SFO
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
Graded Neural Networks
Do LLMs Understand the Safety of Their Inputs? Training-Free Moderation via Latent Prototypes
FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
Federated Continual Learning: Concepts, Challenges, and Solutions
UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation
Code Simulation as a Proxy for High-order Tasks in Large Language Models
An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks
Learning Traffic Anomalies from Generative Models on Real-Time Observations
Explainable AI for Mental Health Emergency Returns: Integrating LLMs with Predictive Modeling
In-Context Meta LoRA Generation
Rethinking Table Instruction Tuning
Autonomous Microscopy Experiments through Large Language Model Agents
Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation
Random weights of DNNs and emergence of fixed points
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
Enhancing Long Video Generation Consistency without Tuning
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Using LLMs to Advance the Cognitive Science of Collectives
Created by
Haebom
저자
Ilia Sucholutsky, Katherine M. Collins, Nori Jacoby, Bill D. Thompson, Robert D. Hawkins
개요
LLM(대규모 언어 모델)은 개인 인지 연구를 변화시키고 있지만, 집단 인지 연구에 대한 적용은 아직 미개척 분야입니다. 본 논문은 LLM이 집단 연구를 어렵게 만드는 복잡성을 해결할 수 있는 방법을 제시하고, 새로운 방법을 필요로 하는 잠재적 위험을 제기합니다.
시사점, 한계점
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시사점:
LLM을 활용하여 집단 인지 연구의 복잡성을 해결할 가능성을 제시합니다.
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한계점:
LLM을 집단 인지 연구에 적용할 때 발생할 수 있는 잠재적 위험에 대한 새로운 방법론이 필요합니다.
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