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.

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 based 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

Affordable AI Assistants with Knowledge Graph of Thoughts

Created by

Haebom

저자

Maciej Besta, Lorenzo Paleari, Jia Hao Andrea Jiang, Robert Gerstenberger, You Wu, Jon Gunnar Hannesson, Patrick Iff, Ales Kubicek, Piotr Nyczyk, Diana Khimey, Nils Blach, Haiqiang Zhang, Tao Zhang, Peiran Ma, Grzegorz Kwasniewski, Marcin Copik, Hubert Niewiadomski, Torsten Hoefler

개요

본 논문은 대규모 언어 모델(LLM) 기반 AI 어시스턴트의 높은 운영 비용 및 복잡한 벤치마크(예: GAIA)에서의 낮은 성공률 문제를 해결하기 위해, 지식 그래프(KG)를 동적으로 구성하여 LLM 추론과 통합하는 새로운 아키텍처인 Knowledge Graph of Thoughts(KGoT)를 제안한다. KGoT는 작업 관련 지식을 동적 KG 표현으로 추출 및 구조화하고, 수학 솔버, 웹 크롤러, Python 스크립트와 같은 외부 도구를 통해 반복적으로 개선한다. 이러한 구조화된 표현은 저비용 모델이 복잡한 작업을 효과적으로 해결하고 편향과 노이즈를 최소화하도록 한다. GAIA 벤치마크에서 GPT-4o mini를 사용한 Hugging Face Agents 대비 29%의 성공률 향상을 달성했으며, GPT-4o 대비 운영 비용을 36배 이상 절감했다. Qwen2.5-32B 및 Deepseek-R1-70B와 같은 다른 모델 및 SimpleQA와 같은 다른 벤치마크에서도 유사한 성능 향상을 보였다. KGoT는 확장 가능하고, 저렴하며, 다재다능하고 고성능인 AI 어시스턴트 솔루션을 제공한다.

시사점, 한계점

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시사점:

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LLM 기반 AI 어시스턴트의 비용 효율성을 크게 향상시킬 수 있는 새로운 아키텍처 제시.

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복잡한 작업에 대한 LLM의 성능을 개선하고 성공률을 높일 수 있음.

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다양한 모델과 벤치마크에서 일관된 성능 향상을 보임.

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편향과 노이즈를 감소시키는 효과적인 방법 제시.

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한계점:

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제시된 KGoT 아키텍처의 일반화 성능에 대한 추가적인 연구 필요.

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다양한 유형의 작업 및 도메인에 대한 KGoT의 적용 가능성에 대한 추가적인 평가 필요.

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외부 도구 의존성에 따른 안정성 및 신뢰성 문제 고려 필요.

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KGoT 구축 및 유지보수에 대한 비용 및 복잡성 분석 필요.

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