Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation

EarthLink: A Self-Evolving AI Agent for Climate Science

Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations

Leveraging multi-source and heterogeneous signals for fatigue detection

Segmentation-free Goodness of Pronunciation

Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers

Compositional Coordination for Multi-Robot Teams with Large Language Models

Diffusion Beats Autoregressive in Data-Constrained Settings

The New LLM Bottleneck: A Systems Perspective on Latent Attention and Mixture-of-Experts

EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control

Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

Omni-Thinker: Scaling Cross-Domain Generalization in LLMs via Multi-Task RL with Hybrid Rewards

GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks

SDSC:A Structure-Aware Metric for Semantic Signal Representation Learning

Multilingual LLMs Are Not Multilingual Thinkers: Evidence from Hindi Analogy Evaluation

Frequency-Dynamic Attention Modulation for Dense Prediction

A Survey of Deep Learning for Geometry Problem Solving

EEG Foundation Models: A Critical Review of Current Progress and Future Directions

Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models

A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma

Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks

OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1

Mechanistic Indicators of Understanding in Large Language Models

Scaling RL to Long Videos

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

Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses

SyncMapV2: Robust and Adaptive Unsupervised Segmentation

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation

Diffuse and Disperse: Image Generation with Representation Regularization

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection

PALADIN : Robust Neural Fingerprinting for Text-to-Image Diffusion Models

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits

Machine Learning Solutions Integrated in an IoT Healthcare Platform for Heart Failure Risk Stratification

Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning

Vision Transformers in Precision Agriculture: A Comprehensive Survey

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

LagKV: Lag-Relative Information of the KV Cache Tells Which Tokens Are Important

Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models

Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs

Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning

Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder

When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning

Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation

Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS

Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?

A general language model for peptide identification

ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models

LLM Alignment as Retriever Optimization: An Information Retrieval Perspective

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings

Online Housing Market

Integrated Learning and Optimization for Congestion Management and Profit Maximization in Real-Time Electricity Market

Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction

Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks

A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects

Neural Corrective Machine Unranking

Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation

Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints

Zeroth-Order Fine-Tuning of LLMs in Random Subspaces

RUMI: Rummaging Using Mutual Information

Neural Machine Unranking

VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks

Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time

A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models

DualXDA: Towards Sparse, Efficient and Explainable Data Attribution in Large AI Models

Quantifying the Uniqueness and Divisiveness of Presidential Discourse

DocTER: Evaluating Document-based Knowledge Editing

Learning Concepts Definable in First-Order Logic with Counting

Recognizing and Eliciting Weakly Single Crossing Profiles on Trees

Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environments

Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs

When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis

DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification

Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games

Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem

EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework

SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic Probability

IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation

OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM

Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation

BEARCUBS: A benchmark for computer-using web agents

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

HPS: Hard Preference Sampling for Human Preference Alignment

A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms

Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases

On the Structure of Game Provenance and its Applications

I-CEE: Tailoring Explanations of Image Classification Models to User Expertise

SIDA: Synthetic Image Driven Zero-shot Domain Adaptation

3D Software Synthesis Guided by Constraint-Expressive Intermediate Representation

Moving Out: Physically-grounded Human-AI Collaboration

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning

Approximate SMT Counting Beyond Discrete Domains

DRWKV: Focusing on Object Edges for Low-Light Image Enhancement

SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic Probability

Created by

Haebom

저자

Alberto Hernandez-Espinosa, Luan Ozelim, Felipe S. Abrahao, Hector Zenil

개요

본 논문은 인공 일반 지능(AGI) 및 초지능(ASI)에 대한 주장과 관련하여 최첨단 모델의 정량적 평가에서 벤치마크 오염을 피할 수 있는 알고리즘 확률에 기반한 개방형 테스트를 제시합니다. 기존 테스트와 달리, 섀넌 엔트로피와 밀접하게 관련되어 단순 패턴 매칭 이상을 테스트할 수 없는 GZIP이나 LZW와 같은 통계적 압축 방법에 의존하지 않습니다. 이 테스트는 역문제 맥락에서 합성 및 모델 생성과 같은 근본적인 지능 특징과 관련하여 AI, 특히 LLM에 대한 과제를 제시합니다. 모델 추상화 및 귀납(최적 베이지안 추론)에 기반한 예측적 계획을 위한 지표가 자연 지능(인간과 동물), 협소 AI, AGI 및 ASI를 포함한 지능 테스트를 위한 견고한 프레임워크를 제공할 수 있다고 주장합니다. LLM 모델 버전은 주로 암기의 결과로 취약하고 증분적이며, 진전은 주로 훈련 데이터의 크기에 의해 이루어지는 경향이 있음을 발견했습니다. 알고리즘 확률과 콜모고로프 복잡성의 원리에 기반하여 이론적으로 보편적 지능을 보장하는 하이브리드 신경기호 접근 방식과 결과를 비교했습니다. 짧은 이진 시퀀스에 대한 개념 증명에서 이 방법이 LLM보다 성능이 우수함을 증명했습니다. 압축은 시스템의 예측력과 동등하고 직접적으로 비례한다는 것을 증명했습니다. 즉, 시스템이 더 잘 예측할 수 있다면 더 잘 압축할 수 있으며, 더 잘 압축할 수 있다면 더 잘 예측할 수 있습니다. 이러한 결과는 LLM의 근본적인 한계에 대한 의혹을 강화하여 인간 언어에 대한 숙달 인식을 위해 최적화된 시스템으로 노출합니다.

시사점, 한계점

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시사점:

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AGI/ASI 평가를 위한 새로운 벤치마크 테스트 제시

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LLM의 한계(암기 중심, 인간 언어 숙달 인식 최적화)를 드러냄

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압축과 예측 능력 간의 상관관계 증명

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하이브리드 신경기호 접근 방식의 우수성 제시

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한계점:

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개념 증명은 짧은 이진 시퀀스에 한정됨. 더 복잡한 데이터셋으로의 확장성 검증 필요.

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제안된 테스트의 범용성 및 일반화 가능성에 대한 추가 연구 필요.

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하이브리드 신경기호 접근 방식의 실제 구현 및 확장성에 대한 추가 연구 필요.

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