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.

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

DeepEyesV2: Toward Agentic Multimodal Model

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

Addressing divergent representations from causal interventions on neural networks

Alternative Fairness and Accuracy Optimization in Criminal Justice

Leveraging LLM-based agents for social science research: insights from citation network simulations

Conversational Collective Intelligence (CCI) using Hyperchat AI in a Real-world Forecasting Task

Explaining Human Choice Probabilities with Simple Vector Representations

Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control

GraphCliff: Short-Long Range Gating for Subtle Differences but Critical Changes

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation

Reasoning Planning for Language Models

Aligning Brain Signals with Multimodal Speech and Vision Embeddings

Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models

Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets

Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs

Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees

Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models

Meronymic Ontology Extraction via Large Language Models

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops

The Markovian Thinker

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Pure Vision Language Action (VLA) Models: A Comprehensive Survey

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning

Normality and the Turing Test

GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach

ANO : Faster is Better in Noisy Landscape

Generative Medical Event Models Improve with Scale

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment

Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning

Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor

Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning

RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection

Controllable Hybrid Captioner for Improved Long-form Video Understanding

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning

When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

Evaluating LLM-based Workflows for Switched-Mode Power Supply Design

Monitoring Risks in Test-Time Adaptation

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery

CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

GPT, But Backwards: Exactly Inverting Language Model Outputs

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

ReCode: Updating Code API Knowledge with Reinforcement Learning

Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

Sekai: A Video Dataset towards World Exploration

Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation

A Fairness-Aware Strategy for B5G Physical-layer Security Leveraging Reconfigurable Intelligent Surfaces

Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

GRAM: Spatial general-purpose audio representation models for real-world applications

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications

VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video

When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering

Rethinking Text-based Protein Understanding: Retrieval or LLM?

Language Model Distillation: A Temporal Difference Imitation Learning Perspective

TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute

DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations

When Bias Helps Learning: Bridging Initial Prejudice and Trainability

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

Video CLIP Model for Multi-View Echocardiography Interpretation

Quantum Doubly Stochastic Transformers

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

Simulator Ensembles for Trustworthy Autonomous Driving Testing

Continual Pre-training of MoEs: How robust is your router?

Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding

Created by

Haebom

저자

Melanie Subbiah, Akankshya Mishra, Grace Kim, Liyan Tang, Greg Durrett, Kathleen McKeown

개요

본 논문은 주장의 출처 문서에 대한 신뢰성 판단 문제를 다룹니다. 기존의 이진 분류 방식(지지/비지지)의 한계를 지적하며, 모호한 주장에 대한 객관적인 평가의 어려움을 강조합니다. 이에, 대규모 언어 모델(LLM)을 이용하여 요약문을 수정하는 방식을 제안합니다. 요약문의 수정 정도를 측정하는 "모호성 재작성 지표(ARM)"를 통해 주장의 신뢰성을 보다 정교하게 평가할 수 있음을 보입니다. 특히, 모호성과 주관적 해석이 빈번한 서사 요약 분야에 초점을 맞춰, ARM이 평가자 간의 일치도를 21% 향상시킨다는 것을 실험적으로 입증합니다.

시사점, 한계점

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시사점:

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LLM 기반의 새로운 평가 지표(ARM)를 제시하여 모호한 주장에 대한 신뢰성 평가의 정확성을 높였습니다.

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이진 분류의 한계를 극복하고, 주관성을 줄여 더욱 정교한 평가가 가능해졌습니다.

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서사 요약 분야에서 평가자 간의 일치도를 크게 향상시켰습니다.

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한계점:

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ARM의 성능은 LLM의 성능에 의존적일 수 있습니다.

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서사 요약에만 국한된 연구이므로, 다른 분야로의 일반화 가능성은 추가 연구가 필요합니다.

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LLM을 이용한 수정 작업의 계산 비용이 상대적으로 높을 수 있습니다.

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