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.

CoRT: Code-integrated Reasoning within Thinking

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Policy-Based Trajectory Clustering in Offline Reinforcement Learning

Understanding Human-AI Trust in Education

ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO

Robotic Policy Learning via Human-assisted Action Preference Optimization

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

QuantMCP: Grounding Large Language Models in Verifiable Financial Reality

Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery

Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment

Sample Complexity and Representation Ability of Test-time Scaling Paradigms

Context Is Not Comprehension

High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset

SALAD: Systematic Assessment of Machine Unlearing on LLM-Aided Hardware Design

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning

Quantum AIXI: Universal Intelligence via Quantum Information

VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization

QuXAI: Explainers for Hybrid Quantum Machine Learning Models

Convert Language Model into a Value-based Strategic Planner

PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications

Token-Efficient RL for LLM Reasoning

MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark

Elucidating the Design Space of Multimodal Protein Language Models

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves

Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts

Content ARCs: Decentralized Content Rights in the Age of Generative AI

PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play

Computation Mechanism Behind LLM Position Generalization

CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting

Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges

Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations

An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem

Generative Uncertainty in Diffusion Models

EgoNormia: Benchmarking Physical Social Norm Understanding

Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

Object-Centric Latent Action Learning

Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

TransMLA: Multi-Head Latent Attention Is All You Need

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Prompt-based Depth Pruning of Large Language Models

Great Models Think Alike and this Undermines AI Oversight

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting

Latent Action Learning Requires Supervision in the Presence of Distractors

SR-Reward: Taking The Path More Traveled

Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs

SoK: Watermarking for AI-Generated Content

Engagement-Driven Content Generation with Large Language Models

PyGen: A Collaborative Human-AI Approach to Python Package Creation

DAWN: Designing Distributed Agents in a Worldwide Network

Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling

Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery

LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement

Deploying Open-Source Large Language Models: A performance Analysis

Neural Networks Generalize on Low Complexity Data

M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Predictive Embedding Architecture

Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs

The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation

TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation

Multi-group Uncertainty Quantification for Long-form Text Generation

Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique

Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things

IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language

Incentivizing Quality Text Generation via Statistical Contracts

Visually Descriptive Language Model for Vector Graphics Reasoning

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

Noise Balance and Stationary Distribution of Stochastic Gradient Descent

The Packing Chromatic Number of the Infinite Square Grid is 15

Reinforcing Multimodal Understanding and Generation with Dual Self-rewards

A Proposal to Extend the Common Model of Cognition with Metacognition

The Optimization Paradox in Clinical AI Multi-Agent Systems

CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDE

Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems

Evaluation of LLMs for mathematical problem solving

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets

A Heuristic Algorithm Based on Beam Search and Iterated Local Search for the Maritime Inventory Routing Problem

A Vision for Auto Research with LLM Agents

AssistanceZero: Scalably Solving Assistance Games

Don't Lag, RAG: Training-Free Adversarial Detection Using RAG

Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search

Training-Free Safe Denoisers for Safe Use of Diffusion Models

CollabLLM: From Passive Responders to Active Collaborators

Position: Theory of Mind Benchmarks are Broken for Large Language Models

InterFeat: An Automated Pipeline for Finding Interesting Hypotheses in Structured Biomedical Data

Created by

Haebom

저자

Dan Ofer, Michal Linial, Dafna Shahaf

개요

본 논문은 구조화된 생의학 데이터에서 흥미로운 단순 가설(효과 방향과 잠재적 기저 메커니즘을 갖는 특징-표적 관계)의 자동 발견을 위한 통합 파이프라인을 제시합니다. 이 파이프라인은 기계 학습, 지식 그래프, 문헌 검색 및 대규모 언어 모델을 결합하여 "흥미로움"을 참신성, 유용성, 타당성의 조합으로 공식화합니다. 영국 바이오뱅크의 8가지 주요 질병에 대한 실험에서, 본 파이프라인은 문헌에 나타나기 수년 전 위험 요소들을 일관되게 회복했습니다. 상위 후보 중 4053%가 흥미로운 것으로 검증되었으며, 이는 SHAP 기반 기준선의 07%와 비교됩니다. 전반적으로 109개 후보 중 28%가 의학 전문가에게 흥미로운 것으로 나타났습니다. 이 파이프라인은 "흥미로움"을 확장 가능하게 그리고 모든 표적에 대해 운영하는 과제를 해결합니다. 데이터와 코드는 https://github.com/LinialLab/InterFeat 에서 공개합니다.

GitHub - LinialLab/InterFeat: Automatically uncover Interesting (novel, plausible, and useful) features in biomedical data by combining statistical filtering, literature mining, knowledge graphs and LLMs on UK BioBank tabular features

Automatically uncover Interesting (novel, plausible, and useful) features in biomedical data by combining statistical filtering, literature mining, knowledge graphs and LLMs on UK BioBank tabular f...

시사점, 한계점

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시사점:

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생의학 데이터에서 흥미로운 가설을 자동으로 발견하는 통합 파이프라인을 제시합니다.

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"흥미로움"을 정량적으로 측정하고 평가하는 새로운 방법을 제시합니다.

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기존 방법보다 훨씬 높은 정확도로 잠재적 위험 요소를 발견합니다.

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의학 연구의 효율성과 속도를 향상시킬 수 있습니다.

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공개된 데이터와 코드를 통해 재현성과 확장성을 확보합니다.

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한계점:

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파이프라인의 성능은 사용된 데이터와 모델에 따라 달라질 수 있습니다.

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"흥미로움"의 정의는 주관적일 수 있으며, 다양한 분야와 상황에 따라 조정이 필요할 수 있습니다.

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대규모 데이터셋을 처리하기 위한 컴퓨팅 자원이 필요합니다.

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의학 전문가의 평가에 의존하는 부분이 존재하여 객관성 확보에 어려움이 있을 수 있습니다.

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