Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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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?

Detecting Invariant Manifolds in ReLU-Based RNNs

Created by

Haebom

저자

Lukas Eisenmann, Alena Brandle, Zahra Monfared, Daniel Durstewitz

개요

본 논문은 시계열 예측 및 동적 시스템 재구성에 널리 사용되는 순환 신경망(RNN)의 동작 방식을 이해하는 데 초점을 맞춘다. 특히, ReLU 활성화 함수를 사용하는 조각별 선형 RNN(PLRNN)에서 안정 및 불안정 매니폴드를 감지하는 새로운 알고리즘을 소개한다. 이 알고리즘은 다중 안정성을 특성화하고 호모클리닉 포인트를 찾아 PLRNN에서 카오스 존재를 입증하는 데 활용된다. 마지막으로, 피질 뉴런의 전기 생리학적 기록에 대한 실제 사례를 통해 제안된 방법론의 통찰력을 보여준다.

시사점, 한계점

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시사점:

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PLRNN의 동작 방식을 이해하기 위한 새로운 알고리즘 개발.

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다중 안정성 및 카오스 특성 분석 가능.

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생물학적 시스템 (피질 뉴런)의 동적 특성 분석에 적용 가능.

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한계점:

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조각별 선형 RNN(PLRNN)에 초점을 맞춤.

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다른 유형의 RNN 아키텍처에 대한 일반화 부족 가능성.

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알고리즘의 계산 복잡성에 대한 언급 부재.

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