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.

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models

Scalable Evaluation and Neural Models for Compositional Generalization

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization

LA-MARRVEL: A Knowledge-Grounded and Language-Aware LLM Reranker for AI-MARRVEL in Rare Disease Diagnosis

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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

AI for Requirements Engineering: Industry adoption and Practitioner perspectives

HAFixAgent: History-Aware Automated Program Repair Agent

Quantifying truth and authenticity in AI-assisted candidate evaluation: A multi-domain pilot analysis

On Improvisation and Open-Endedness: Insights for Experiential AI

Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation

WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios

Using latent representations to link disjoint longitudinal data for mixed-effects regression

LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)

Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery

Enabling Robust In-Context Memory and Rapid Task Adaptation in Transformers with Hebbian and Gradient-Based Plasticity

The Mirror Loop: Recursive Non-Convergence in Generative Reasoning Systems

A Foundational Theory of Quantitative Abstraction: Adjunctions, Duality, and Logic for Probabilistic Systems

AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation

StutterZero and StutterFormer: End-to-End Speech Conversion for Stuttering Transcription and Correction

ADPO: Anchored Direct Preference Optimization

A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness

FaStfact: Faster, Stronger Long-Form Factuality Evaluations in LLMs

DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification

Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing

Training Optimal Large Diffusion Language Models

CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG

Automatic Discovery of One-Parameter Subgroups of Lie Groups: Compact and Non-Compact Cases of $\mathbf{SO(n)}$ and $\mathbf{SL(n)}$

A Unified Formal Theory on the Logical Limits of Symbol Grounding

Evaluating Large Language Models for Detecting Antisemitism

SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs

GDS Agent for Graph Algorithmic Reasoning

Activation Transport Operators

"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design

Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology

Geometry-Aware Global Feature Aggregation for Real-Time Indirect Illumination

Fast weight programming and linear transformers: from machine learning to neurobiology

Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

ViFP: A Framework for Visual False Positive Detection to Enhance Reasoning Reliability in VLMs

CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction

Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning

TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing

PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems

Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation

Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification

Flow matching for reaction pathway generation

DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models

Omni-Router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition

FedRef: Communication-Efficient Bayesian Fine-Tuning using a Reference Model

Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training

Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging

Dense SAE Latents Are Features, Not Bugs

AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs

Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing

Large Language Models Miss the Multi-Agent Mark

Distilling LLM Agent into Small Models with Retrieval and Code Tools

This Time is Different: An Observability Perspective on Time Series Foundation Models

RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation

Traversal Verification for Speculative Tree Decoding

Deep Learning Warm Starts for Trajectory Optimization on the International Space Station

A data-driven framework for team selection in Fantasy Premier League

SecRepoBench: Benchmarking Code Agents for Secure Code Completion in Real-World Repositories

Revisiting semi-supervised learning in the era of foundation models

Assessing the Macro and Micro Effects of Random Seeds on Fine-Tuning Large Language Models

A Survey on Text-Driven 360-Degree Panorama Generation

Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression

From Haystack to Needle: Label Space Reduction for Zero-shot Classification

REFA: Reference Free Alignment for multi-preference optimization

RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis

Do Automatic Factuality Metrics Measure Factuality? A Critical Evaluation

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs

Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs

Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models

Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization

MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping

Autonomous Robotic Drilling System for Mice Cranial Window Creation

A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics

Survey on AI Ethics: A Socio-technical Perspective

Transfer Learning-based Real-time Handgun Detection

Emotion Detection From Social Media Posts

Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything

Kosmos: An AI Scientist for Autonomous Discovery

The ORCA Benchmark: Evaluating Real-World Calculation Accuracy in Large Language Models

TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data

Mirror-Neuron Patterns in AI Alignment

Agentic Meta-Orchestrator for Multi-task Copilots

Misalignment Bounty: Crowdsourcing AI Agent Misbehavior

PlanU: Large Language Model Reasoning through Planning under Uncertainty

VoiceAgentBench: Are Voice Assistants ready for agentic tasks?

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse

Reinforcement Learning Foundations for Deep Research Systems: A Survey

Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning

Why Isn't Relational Learning Taking Over the World?

The Computational Complexity of Circuit Discovery for Inner Interpretability

Created by

Haebom

저자

Federico Adolfi, Martina G. Vilas, Todd Wareham

개요

본 논문은 신경망의 내적 해석 가능성을 위한 회로 발견의 실현 가능성에 대한 이론적, 실증적 탐구를 수행합니다. 기존 휴리스틱 기법의 확장성과 신뢰성에 대한 우려를 해결하기 위해, 고전적 및 매개변수화된 계산 복잡도 이론을 활용하여 회로 발견 문제를 분석합니다. 회로 발견 질의를 설명, 예측, 제어 측면에서 공식화하고, 기계적 설명을 위한 포괄적인 질의 집합을 정의하여 다층 퍼셉트론 상에서 다양한 질의 변형의 복잡도를 분석합니다. 많은 질의가 다루기 어려운 것으로 밝혀졌으며, 일부 문제는 더 잘 이해된 휴리스틱 기법을 활용하여 해결할 수 있는 변환이 존재함을 증명하고, 유용한 기능을 유지하는 보다 단순한 질의의 추적 가능성 또는 고정 매개변수 추적 가능성을 증명합니다. 이러한 프레임워크는 해석 가능성 질의의 범위와 한계를 이해하고, 실행 가능한 옵션을 탐색하며, 기존 및 미래 아키텍처에서의 자원 요구 사항을 비교할 수 있도록 합니다.

시사점, 한계점

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시사점:

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신경망의 내적 해석 가능성을 위한 회로 발견 문제에 대한 계산 복잡도 이론적 분석 프레임워크를 제시합니다.

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다양한 해석 가능성 질의의 계산 복잡도를 분석하여, 어떤 질의는 다루기 어렵다는 것을 밝힙니다.

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일부 어려운 문제를 해결하기 위한 변환 기법과, 유용한 기능을 유지하는 보다 단순한 질의의 추적 가능성을 제시합니다.

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해석 가능성 질의의 범위와 한계를 이해하고, 실행 가능한 옵션을 탐색하고 비교할 수 있는 기반을 마련합니다.

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한계점:

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분석 대상이 다층 퍼셉트론으로 제한됩니다. 다른 유형의 신경망 아키텍처에 대한 분석이 필요합니다.

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제시된 프레임워크의 실제 적용 가능성에 대한 추가적인 실험적 검증이 필요합니다.

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복잡도 분석 결과가 실제 응용에 미치는 영향에 대한 추가적인 연구가 필요합니다.

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