Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning

HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking

Evalet: Evaluating Large Language Models by Fragmenting Outputs into Functions

Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers

Physics-informed neural network solves minimal surfaces in curved spacetime

A funny companion: Distinct neural responses to perceived AI- versus human-generated humor

National Running Club Database: Assessing Collegiate Club Athletes' Cross Country Race Results

Online Learning Based Efficient Resource Allocation for LoRaWAN Network

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization

Implicit Neural Representations of Intramyocardial Motion and Strain

MVPBench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

AI Governance in Higher Education: A course design exploring regulatory, ethical and practical considerations

Benchmarking Gender and Political Bias in Large Language Models

BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models

TinyDef-DETR: A DETR-based Framework for Defect Detection in Transmission Lines from UAV Imagery

TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition

Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning

ICR: Iterative Clarification and Rewriting for Conversational Search

ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions

Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs

Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot

PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning

A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages

SIFThinker: Spatially-Aware Image Focus for Visual Reasoning

Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries

FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning

New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants

Analysis of Fourier Neural Operators via Effective Field Theory

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training

Memorization Sinks: Isolating Memorization during LLM Training

Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval

OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flows

AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN

Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning

Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models

MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing

Counterfactual Simulatability of LLM Explanations for Generation Tasks

PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims

HiLAB: A Hybrid Inverse-Design Framework

Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intent

WaterFlow: Learning Fast & Robust Watermarks using Stable Diffusion

Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding

Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching

Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation

Teaching Your Models to Understand Code via Focal Preference Alignment

Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts

SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning

Safe Learning Under Irreversible Dynamics via Asking for Help

Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection

How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the Wild

TokenSkip: Controllable Chain-of-Thought Compression in LLMs

Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

AI/ML Based Detection and Categorization of Covert Communication in IPv6 Network

Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN

Enhancing Automated Loop Invariant Generation for Complex Programs with Large Language Models

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation

Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints

Adversarial Prompt Distillation for Vision-Language Models

TrojanRobot: Physical-world Backdoor Attacks Against VLM-based Robotic Manipulation

The Belief State Transformer

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data

Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypes

T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design

TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation

Context-Aware Membership Inference Attacks against Pre-trained Large Language Models

RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning

Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning

Informed Correctors for Discrete Diffusion Models

EMOE: A Framework for Out-of-distribution Uncertainty Based Rejection via Model-Agnostic Expansive Matching of Experts

Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey

Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation

When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration

Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture

Explaining Tournament Solutions with Minimal Supports

Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence

TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems

Small Language Models are the Future of Agentic AI

Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D

Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

Robust Decision-Making Via Free Energy Minimization

CredID: Credible Multi-Bit Watermark for Large Language Models Identification

Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge

Overcoming classic challenges for artificial neural networks by providing incentives and practice

Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity

Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Language Models

Contrastive timbre representations for musical instrument and synthesizer retrieval

HARMONIC: A Content-Centric Cognitive Robotic Architecture

RadGame: An AI-Powered Platform for Radiology Education

JANUS: A Dual-Constraint Generative Framework for Stealthy Node Injection Attacks

Small Language Models are the Future of Agentic AI

Created by

Haebom

저자

Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov

개요

본 논문은 에이전트 시스템에서 대규모 언어 모델(LLM) 대신 소규모 언어 모델(SLM)을 사용하는 것이 더 적합하고 경제적이라는 주장을 제시합니다. LLM이 다양한 작업에서 인간과 유사한 성능을 보이는 반면, 에이전트 시스템은 특정 작업을 반복적으로 수행하는 경우가 많으므로 SLM이 충분한 성능을 제공하며, 경제적인 측면에서도 유리하다는 것입니다. 논문은 SLM의 현재 성능, 에이전트 시스템의 일반적인 아키텍처, 그리고 LM 배포의 경제성을 근거로 이 주장을 뒷받침하며, 일반적인 대화 능력이 필수적인 경우에는 여러 모델을 호출하는 이기종 에이전트 시스템을 사용하는 것이 자연스럽다고 주장합니다. 또한 SLM 채택의 잠재적 장벽을 논의하고 LLM에서 SLM 에이전트로 변환하는 일반적인 알고리즘을 개괄적으로 제시합니다. LLM에서 SLM으로의 부분적인 전환조차도 AI 에이전트 산업에 상당한 운영 및 경제적 영향을 미칠 것이라고 강조하며, AI 자원의 효율적인 사용에 대한 논의를 활성화하고 AI 비용 절감 노력을 진전시키는 것을 목표로 합니다.

시사점, 한계점

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시사점:

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에이전트 시스템에서 SLM의 효율성과 경제성을 강조하여 AI 자원의 효율적인 사용을 촉구합니다.

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LLM에서 SLM으로의 전환 알고리즘을 제시하여 실질적인 전환 방안을 모색합니다.

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이기종 에이전트 시스템을 제안하여 다양한 작업에 대한 효율적인 대응 방안을 제시합니다.

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AI 비용 절감에 기여할 수 있는 새로운 방향을 제시합니다.

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한계점:

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SLM의 성능이 모든 에이전트 시스템에서 LLM을 대체할 수 있을 만큼 충분한지에 대한 추가적인 실험적 검증이 필요합니다.

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LLM에서 SLM로의 전환 알고리즘의 일반성 및 실제 적용 가능성에 대한 추가적인 연구가 필요합니다.

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SLM 채택의 잠재적 장벽에 대한 더욱 심층적인 분석이 필요합니다.

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제시된 주장의 일반화 가능성과 다양한 에이전트 시스템에 대한 적용 가능성에 대한 추가적인 연구가 필요합니다.

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