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.

AI/ML in 3GPP 5G Advanced -- Services and Architecture

CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration

Concept-Guided Backdoor Attack on Vision Language Models

On the Holographic Geometry of Deterministic Computation

Decoding inner speech with an end-to-end brain-to-text neural interface

Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation

MindEval: Benchmarking Language Models on Multi-turn Mental Health Support

LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling

Data-Augmented Deep Learning for Downhole Depth Sensing and Field Validation

Chinese Discharge Drug Recommendation in Metabolic Diseases with Large Language Models

Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting

Reinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives

TempoControl: Temporal Attention Guidance for Text-to-Video Models

AortaDiff: A Unified Multitask Diffusion Framework For Contrast-Free AAA Imaging

SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction

The AI Productivity Index (APEX)

Uncovering Grounding IDs: How External Cues Shape Multimodal Binding

Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization

V-CECE: Visual Counterfactual Explanations via Conceptual Edits

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios

HARP: Hallucination Detection via Reasoning Subspace Projection

Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders

ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal

Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

A Survey on Diffusion Language Models

CodeNER: Code Prompting for Named Entity Recognition

ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation

VERIRAG: A Post-Retrieval Auditing of Scientific Study Summaries

SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models

LittleBit: Ultra Low-Bit Quantization via Latent Factorization

Real-Time Execution of Action Chunking Flow Policies

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents

Enhancing SPARQL Query Rewriting for Complex Ontology Alignments

Exploring Ordinal Bias in Action Recognition for Instructional Videos

Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation

Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software

SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models

Large Language Models: An Applied Econometric Framework

Edge-Only Universal Adversarial Attacks in Distributed Learning

Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection

Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action Recognition

A Scene-aware Models Adaptation Scheme for Cross-scene Online Inference on Mobile Devices

Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm

GTM: Simulating the World of Tools for AI Agents

Self-Transparency Failures in Expert-Persona LLMs: How Instruction-Following Overrides Honesty

Learning the Value of Value Learning

OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe

ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering

Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models

Generalized Parallel Scaling with Interdependent Generations

SOCK: A Benchmark for Measuring Self-Replication in Large Language Models

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems

IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks

FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes

Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning

Rolling in the deep of cognitive and AI biases

Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms

Training-Time Action Conditioning for Efficient Real-Time Chunking

Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity

AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

Trusted AI Agents in the Cloud

Impugan: Learning Conditional Generative Models for Robust Data Imputation

Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception

World Models That Know When They Don't Know: Controllable Video Generation with Calibrated Uncertainty

Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures

Neural Coherence : Find higher performance to out-of-distribution tasks from few samples

Sparse Attention Post-Training for Mechanistic Interpretability

Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework

NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation

Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning

Approximation of Box Decomposition Algorithm for Fast Hypervolume-Based Multi-Objective Optimization

Probing the effectiveness of World Models for Spatial Reasoning through Test-time Scaling

3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering

Mechanistic Interpretability of Antibody Language Models Using SAEs

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Efficient Text Classification with Conformal In-Context Learning

Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias

Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning

Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domains

Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

Feasibility of AI-Assisted Programming for End-User Development

Grounded Multilingual Medical Reasoning for Question Answering with Large Language Models

Modular Jets for Supervised Pipelines: Diagnosing Mirage vs Identifiability

A Comprehensive Framework for Automated Quality Control in the Automotive Industry

Evaluation-Driven Development of LLM Agents: A Process Model and Reference Architecture

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Haebom

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저자

Boming Xia, Qinghua Lu, Liming Zhu, Zhenchang Xing, Dehai Zhao, Hao Zhang

개요

본 논문은 대규모 언어 모델(LLM) 에이전트의 평가 및 개발을 위한 새로운 접근법을 제시합니다. 기존의 사전 정의된 테스트 사례나 표준 재개발 파이프라인은 LLM 에이전트의 동적이고 확률적이며 진화하는 특성을 다루는 데 어려움을 겪기 때문에, 본 논문은 테스트 주도 개발 및 행동 주도 개발에서 영감을 받은 평가 주도 개발 접근 방식을 제안합니다. 이 접근 방식은 온라인(런타임) 및 오프라인(재개발) 평가를 통합하여 에이전트의 라이프사이클 전반에 걸쳐 적응형 런타임 조정과 파이프라인, 아티팩트, 시스템 아키텍처 및 LLM 자체의 체계적인 반복적 개선을 가능하게 합니다. 여기에는 인간 및 AI 평가자의 세분화된 피드백을 포함한 평가 결과를 개발 및 운영의 각 단계에 지속적으로 통합하여 LLM 에이전트가 진화하는 목표, 사용자 요구 및 거버넌스 표준과 일치하도록 합니다. 다양한 문헌 검토를 통해 기존 LLM 평가 방법의 한계를 분석하고, LLM 에이전트의 평가 주도 개발을 위한 새로운 프로세스 모델과 참조 아키텍처를 제시합니다.

시사점, 한계점

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시사점:

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LLM 에이전트의 동적이고 복잡한 특성을 고려한 새로운 평가 프레임워크 제공

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온라인 및 오프라인 평가를 통합하여 지속적인 개선 및 적응 가능

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인간 및 AI 평가자의 피드백을 활용한 정교한 평가 시스템 구축

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LLM 에이전트의 안전성 및 성능 향상에 기여

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진화하는 목표, 사용자 요구, 거버넌스 표준과의 지속적인 정렬 가능

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한계점:

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제안된 프레임워크의 실제 적용 및 효과에 대한 추가적인 실험 및 검증 필요

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다양한 유형의 LLM 에이전트에 대한 일반화 가능성에 대한 추가 연구 필요

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인간 및 AI 평가자의 피드백 통합 과정의 효율성 및 신뢰성에 대한 추가 분석 필요

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평가 과정의 복잡성 및 비용에 대한 고려 필요

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