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.

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning

Token-based Audio Inpainting via Discrete Diffusion

Mechanistic Indicators of Understanding in Large Language Models

PyVision: Agentic Vision with Dynamic Tooling

Low Resource Reconstruction Attacks Through Benign Prompts

DTECT: Dynamic Topic Explorer & Context Tracker

Your Absorbing Discrete Diffusion Secretly Models the Bayesian Posterior

Bias-Aware Mislabeling Detection via Decoupled Confident Learning

Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs

FlexOlmo: Open Language Models for Flexible Data Use

Democratizing High-Fidelity Co-Speech Gesture Video Generation

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance

BayesSDF: Surface-Based Laplacian Uncertainty Estimation for 3D Geometry with Neural Signed Distance Fields

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning

Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion

Hear-Your-Click: Interactive Object-Specific Video-to-Audio Generation

PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes

LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop

Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding

From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis

An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography (CT) Images

De-Fake: Style based Anomaly Deepfake Detection

ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis

Expert-level validation of AI-generated medical text with scalable language models

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction

FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference

DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values

Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence

Description of the Training Process of Neural Networks via Ergodic Theorem : Ghost nodes

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information

Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention

When Small Guides Large: Cross-Model Co-Learning for Test-Time Adaptation

Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

Interpretable Time Series Autoregression for Periodicity Quantification

Teaching Models to Verbalize Reward Hacking in Chain-of-Thought Reasoning

Exploration Behavior of Untrained Policies

Ark: An Open-source Python-based Framework for Robot Learning

Hyperspherical Variational Autoencoders Using Efficient Spherical Cauchy Distribution

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

Towards Effective Complementary Security Analysis using Large Language Models

Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding

Learning from M-Tuple Dominant Positive and Unlabeled Data

BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementation for Treatment Response Prediction

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks

Subgroups Matter for Robust Bias Mitigation

Robust Stability Analysis of Positive Lure System with Neural Network Feedback

Next-token pretraining implies in-context learning

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents

Learning Flexible Forward Trajectories for Masked Molecular Diffusion

LEXam: Benchmarking Legal Reasoning on 340 Law Exams

Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement

Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks

Visual Test-time Scaling for GUI Agent Grounding

Consistency in Language Models: Current Landscape, Challenges, and Future Directions

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction

Adaptive Non-local Observable on Quantum Neural Networks

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems

Divergence of Empirical Neural Tangent Kernel in Classification Problems

Rethinking the Foundations for Continual Reinforcement Learning

Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs

Unfair Learning: GenAI Exceptionalism and Copyright Law

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

LLMs' Leaning in European Elections

COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach

Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy

Learning-Order Autoregressive Models with Application to Molecular Graph Generation

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?

No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms

Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing

IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector

B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability

Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

Integrating Generative Artificial Intelligence in ADRD: A Roadmap for Streamlining Diagnosis and Care in Neurodegenerative Diseases

Logits are All We Need to Adapt Closed Models

Oracular Programming: A Modular Foundation for Building LLM-Enabled Software

Learning Traffic Anomalies from Generative Models on Real-Time Observations

BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction

Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection

Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation

An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge

DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential Equation

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models

Visual Theory of Mind Enables the Invention of Proto-Writing

Created by

Haebom

저자

Benjamin A. Spiegel, Lucas Gelfond, George Konidaris

개요

본 논문은 다중 에이전트 강화 학습을 기반으로 한 '의미화 게임(Signification Game)'이라는 새로운 테스트베드를 제시하여, 원시 그림 문자(pictograph)를 사용한 의사소통의 출현을 연구합니다. 기존 연구들이 비자연적인 방법론을 사용하여 인간과 동물 인지와의 명확한 유추를 어렵게 했던 것과 달리, 본 연구는 시각적 마음 이론(visual theory of mind)을 활용하여 에이전트들이 그림 문자를 사용하여 행동을 소통할 수 있도록 하는 추론적 의사소통 모델을 제시합니다. 이 모델은 동물 의사소통에 대한 광범위한 형식주의 내에 위치하여 원시 문자의 출현을 뒷받침하는 인지적 및 문화적 과정에 대한 통찰력을 제공합니다.

시사점, 한계점

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시사점:

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다중 에이전트 강화 학습을 통해 원시 그림 문자 기반 의사소통의 출현 과정을 자연스럽게 모사하는 새로운 모델을 제시.

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시각적 마음 이론을 통합하여 인지적 측면을 강조한 추론적 의사소통 모델을 제시.

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인간과 동물 인지에 대한 유추를 가능하게 하는 보다 자연스러운 방법론 제시.

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원시 문자 발생의 인지적 및 문화적 과정에 대한 새로운 이해 제공.

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한계점:

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모델의 복잡성으로 인해 해석 및 일반화의 어려움 존재 가능성.

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실제 인간의 의사소통 과정과의 완벽한 일치 여부에 대한 추가 검증 필요.

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모델의 단순화로 인한 실제 세계의 복잡성 반영 부족 가능성.

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제한된 환경에서의 실험 결과이므로 일반화 가능성에 대한 추가 연구 필요.

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