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Daily Arxiv
Daily Arxiv
전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Accurate and scalable exchange-correlation with deep learning
AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
GRAM: A Generative Foundation Reward Model for Reward Generalization
VideoMAR: Autoregressive Video Generatio with Continuous Tokens
FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models
No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!
Serving Large Language Models on Huawei CloudMatrix384
PLD: A Choice-Theoretic List-Wise Knowledge Distillation
TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy
Refactoring Codebases through Library Design
TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation
Multi-Task Reward Learning from Human Ratings
Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
Vision Transformers Don't Need Trained Registers
BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients
Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools
ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models
Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation
Efficient Long CoT Reasoning in Small Language Models
Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning
MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization
Fractured Chain-of-Thought Reasoning
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
Influential Bandits: Pulling an Arm May Change the Environment
SCAM: A Real-World Typographic Robustness Evaluation for Multimodal Foundation Models
Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos
A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do\~nana
KANITE: Kolmogorov-Arnold Networks for ITE estimation
Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation
EgoBlind: Towards Egocentric Visual Assistance for the Blind
PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice
Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
Perspective Transition of Large Language Models for Solving Subjective Tasks
Can LLMs Ask Good Questions?
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation
Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition
Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers
REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
FLARE: Towards Universal Dataset Purification against Backdoor Attacks
Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification
Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification
Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation
LL\"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes
The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers
Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs
Deep Graph Anomaly Detection: A Survey and New Perspectives
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Style-Preserving Lip Sync via Audio-Aware Style Reference
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Informed Correctors for Discrete Diffusion Models
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
A Systematic Survey of Natural Language Processing for the Greek Language
Predicting the Understandability of Computational Notebooks through Code Metrics Analysis
An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
The NordDRG AI Benchmark for Large Language Models
From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?
Entropy-based Exploration Conduction for Multi-step Reasoning
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization
Optimal Transport for Probabilistic Circuits
OM4OV: Leveraging Ontology Matching for Ontology Versioning
Behaviour Planning: A Toolkit for Diverse Planning
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
Dense SAE Latents Are Features, Not Bugs
Sekai: A Video Dataset towards World Exploration
Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning
Demystifying the Visual Quality Paradox in Multimodal Large Language Models
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
GFLC: Graph-based Fairness-aware Label Correction for Fair Classification
The Compositional Architecture of Regret in Large Language Models
LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
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VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
Created by
Haebom
저자
Yichen Feng, Zhangchen Xu, Fengqing Jiang, Yuetai Li, Bhaskar Ramasubramanian, Luyao Niu, Bill Yuchen Lin, Radha Poovendran
개요
VisualSphinx는 최초의 대규모 합성 시각 논리 추론 학습 데이터셋입니다. 기존 시각 언어 모델(VLMs)의 논리적 추론 능력 향상을 위해, 규칙 기반 이미지 합성 파이프라인을 통해 질문에서 규칙을 추출하고 확장하여 이미지를 생성합니다. 이 데이터셋으로 학습된 VLM은 논리적 일관성과 가독성이 향상되어, 다양한 논리 추론 작업(대수, 산술, 기하 추론 등)에서 성능이 개선됨을 실험을 통해 보여줍니다.
시사점, 한계점
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시사점:
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대규모 합성 시각 논리 추론 데이터셋 VisualSphinx 제시.
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규칙 기반 이미지 합성 파이프라인을 통한 효율적인 데이터 생성.
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VisualSphinx를 이용한 VLM 학습은 논리적 추론 능력 향상에 기여.
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다양한 논리 추론 작업(대수, 산술, 기하 추론 등)에서 성능 향상.
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한계점:
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합성 데이터의 현실 세계 데이터와의 차이.
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VisualSphinx 데이터셋의 일반화 성능에 대한 추가적인 연구 필요.
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제시된 규칙 기반 이미지 합성 파이프라인의 복잡성 및 확장성에 대한 고찰 필요.
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