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.

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs

Accurate and scalable exchange-correlation with deep learning

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

GRAM: A Generative Foundation Reward Model for Reward Generalization

VideoMAR: Autoregressive Video Generatio with Continuous Tokens

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Serving Large Language Models on Huawei CloudMatrix384

PLD: A Choice-Theoretic List-Wise Knowledge Distillation

TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy

Refactoring Codebases through Library Design

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

Multi-Task Reward Learning from Human Ratings

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers

Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning

Vision Transformers Don't Need Trained Registers

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

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models

Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation

Efficient Long CoT Reasoning in Small Language Models

Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Fractured Chain-of-Thought Reasoning

DreamGen: Unlocking Generalization in Robot Learning through Video World Models

UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions

Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs

Influential Bandits: Pulling an Arm May Change the Environment

SCAM: A Real-World Typographic Robustness Evaluation for Multimodal Foundation Models

Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning

Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos

A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do\~nana

KANITE: Kolmogorov-Arnold Networks for ITE estimation

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization

Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing

Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation

EgoBlind: Towards Egocentric Visual Assistance for the Blind

PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data

Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform

CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Perspective Transition of Large Language Models for Solving Subjective Tasks

Can LLMs Ask Good Questions?

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation

Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition

Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization

FLARE: Towards Universal Dataset Purification against Backdoor Attacks

Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification

Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation

LL\"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes

The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers

Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs

Deep Graph Anomaly Detection: A Survey and New Perspectives

A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS

Style-Preserving Lip Sync via Audio-Aware Style Reference

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Informed Correctors for Discrete Diffusion Models

RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

A Systematic Survey of Natural Language Processing for the Greek Language

Predicting the Understandability of Computational Notebooks through Code Metrics Analysis

An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

The NordDRG AI Benchmark for Large Language Models

From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Entropy-based Exploration Conduction for Multi-step Reasoning

Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization

Optimal Transport for Probabilistic Circuits

OM4OV: Leveraging Ontology Matching for Ontology Versioning

Behaviour Planning: A Toolkit for Diverse Planning

Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition

"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Dense SAE Latents Are Features, Not Bugs

Sekai: A Video Dataset towards World Exploration

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

Demystifying the Visual Quality Paradox in Multimodal Large Language Models

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

The Compositional Architecture of Regret in Large Language Models

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning

Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systems

Created by

Haebom

저자

Gordon Dai, Yunze Xiao

개요

본 논문은 책임있는 AI(RAI) 지표들 간의 이론적 불일치(예: 상이한 공정성 정의, 정확도와 프라이버시 간의 상충 관계)를 제거해야 할 결함이 아닌, 귀중한 특징으로 받아들여야 한다고 주장하는 입장 논문입니다. 저자들은 여러 지표를 상반된 목표로 다룸으로써 세 가지 주요 이점을 얻을 수 있다고 주장합니다. 첫째, 규범적 다원주의: 상반될 수 있는 여러 지표를 유지함으로써 RAI에 내재된 다양한 도덕적 입장과 이해관계자의 가치를 충분히 반영할 수 있습니다. 둘째, 인식론적 완전성: 때때로 상충하는 여러 지표를 사용하면 다면적인 윤리적 개념을 더 포괄적으로 포착하여 단일의 단순화된 정의보다 더 큰 정보 충실도를 유지할 수 있습니다. 셋째, 암묵적 규제: 이론적으로 상충하는 목표를 함께 최적화하면 특정 지표에 대한 과적합을 방지하고 실제 세계의 복잡성 속에서 향상된 일반화 및 강건성을 가진 솔루션으로 모델을 유도합니다. 반대로, 지표를 단순화하거나 제거하여 이론적 일관성을 강화하려는 노력은 가치 다양성을 좁히고, 개념적 깊이를 잃고, 모델 성능을 저하시킬 위험이 있습니다. 따라서 저자는 RAI 이론 및 실제에서 불일관성에 갇히는 것이 아니라 허용 가능한 불일관성 임계값을 특징짓고 실제로 강력하고 근사적인 일관성을 허용하는 메커니즘을 명확히 하는 방향으로 전환해야 한다고 주장합니다.

시사점, 한계점

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시사점:

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RAI 지표 간의 불일치를 장점으로 활용하여 다양한 윤리적 관점과 이해관계자의 가치를 포괄적으로 고려할 수 있음을 제시합니다.

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여러 지표를 통해 윤리적 개념에 대한 더욱 포괄적이고 정확한 이해를 가능하게 합니다.

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상충하는 지표들을 함께 최적화하여 모델의 과적합을 방지하고 일반화 성능 및 강건성을 향상시킬 수 있습니다.

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RAI 연구 및 개발에 있어 불일치를 관리하고 허용 가능한 수준을 정의하는 새로운 패러다임을 제시합니다.

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한계점:

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불일치의 허용 가능한 수준을 정의하는 객관적인 기준이 제시되지 않았습니다.

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실제 응용에 있어 상충하는 지표들을 효과적으로 관리하고 최적화하는 구체적인 방법론이 부족합니다.

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다양한 이해관계자의 가치를 조율하고 우선순위를 결정하는 과정에 대한 논의가 미흡합니다.

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제시된 이론적 주장을 검증하기 위한 실증적인 연구가 필요합니다.

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