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.

Teaching Models to Verbalize Reward Hacking in Chain-of-Thought Reasoning

Exploration Behavior of Untrained Policies

Ark: An Open-source Python-based Framework for Robot Learning

Hyperspherical Variational Autoencoders Using Efficient Spherical Cauchy Distribution

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

Towards Effective Complementary Security Analysis using Large Language Models

Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding

Learning from M-Tuple Dominant Positive and Unlabeled Data

BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementation for Treatment Response Prediction

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks

Subgroups Matter for Robust Bias Mitigation

Robust Stability Analysis of Positive Lure System with Neural Network Feedback

Next-token pretraining implies in-context learning

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents

Learning Flexible Forward Trajectories for Masked Molecular Diffusion

LEXam: Benchmarking Legal Reasoning on 340 Law Exams

Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement

Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks

Visual Test-time Scaling for GUI Agent Grounding

Consistency in Language Models: Current Landscape, Challenges, and Future Directions

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction

Adaptive Non-local Observable on Quantum Neural Networks

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems

Divergence of Empirical Neural Tangent Kernel in Classification Problems

Rethinking the Foundations for Continual Reinforcement Learning

Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs

Unfair Learning: GenAI Exceptionalism and Copyright Law

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents

LLMs' Leaning in European Elections

COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach

Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy

Learning-Order Autoregressive Models with Application to Molecular Graph Generation

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?

No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms

Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing

IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector

B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability

Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

Integrating Generative Artificial Intelligence in ADRD: A Roadmap for Streamlining Diagnosis and Care in Neurodegenerative Diseases

Logits are All We Need to Adapt Closed Models

Oracular Programming: A Modular Foundation for Building LLM-Enabled Software

Learning Traffic Anomalies from Generative Models on Real-Time Observations

BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction

Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection

Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation

An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge

DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential Equation

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models

Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction

Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces

TabDPT: Scaling Tabular Foundation Models on Real Data

Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration

UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification

EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration

Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models

Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner

Dataset Distillation-based Hybrid Federated Learning on Non-IID Data

TheraGen: Therapy for Every Generation

SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation

Insuring Uninsurable Risks from AI: Government as Insurer of Last Resort

DiPT: Enhancing LLM reasoning through diversified perspective-taking

Political Bias in LLMs: Unaligned Moral Values in Agent-centric Simulations

A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism

Causal Discovery-Driven Change Point Detection in Time Series

Learning Decentralized Multi-Biped Control for Payload Transport

Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

TKAN: Temporal Kolmogorov-Arnold Networks

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving

Integrated Gradient Correlation: a Dataset-wise Attribution Method

Continuous Spiking Graph Neural Networks

An In-depth Evaluation of Large Language Models in Sentence Simplification with Error-based Human Assessment

Pinning "Reflection" on the Agenda: Investigating Reflection in Human-LLM Co-Creation for Creative Coding

Dynamic Spiking Framework for Graph Neural Networks

Bounding the Worst-class Error: A Boosting Approach

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models

Capsule Networks Do Not Need to Model Everything

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

Continuous Classification Aggregation

MedGemma Technical Report

Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab

A Fuzzy Approach to the Specification, Verification and Validation of Risk-Based Ethical Decision Making Models

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact

Individual Causal Inference with Structural Causal Model

SymRAG: Efficient Neuro-Symbolic Retrieval Through Adaptive Query Routing

Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning

Reinforced Reasoning for Embodied Planning

Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning

Access Controls Will Solve the Dual-Use Dilemma

Arbitrarily Applicable Same/Opposite Relational Responding with NARS

Created by

Haebom

저자

Robert Johansson, Patrick Hammer, Tony Lofthouse

개요

본 논문은 비공리적 추론 시스템(NARS)이라는 계산적 인지 구조 내에서 임의로 적용 가능한 동일/반대 관계적 반응의 출현을 보여줍니다. NARS는 불확실성 하에서 적응적 추론을 위해 설계된 시스템이며, 본 연구에서는 '획득된 관계'를 구현하여 확장함으로써, 문맥적으로 제어된 매칭-투-샘플(MTS) 절차에서 최소한의 명시적 훈련으로 대칭적(상호 함축) 및 새로운 관계적 조합(조합적 함축)을 명시적으로 도출할 수 있도록 했습니다. 실험 결과, NARS는 명시적으로 훈련된 관계 규칙을 빠르게 내면화하고 임의의 문맥적 단서를 기반으로 도출된 관계적 일반화를 강력하게 보여줍니다. 중요한 점은, 중요한 테스트 단계에서 도출된 관계적 반응은 여러 개의 명시적으로 훈련된 반대 관계로부터 동일 관계를 도출하는 것과 같이 상호 및 조합적 함축을 본질적으로 결합합니다. 내부 신뢰도 측정은 이러한 관계 원칙의 강력한 내면화를 보여주며, 인간의 관계 학습 실험에서 관찰된 현상과 밀접하게 평행을 이룹니다.

시사점, 한계점

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시사점:

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NARS와 같은 인공지능 프레임워크에 심리학적 관계 학습 메커니즘을 통합하는 잠재력을 보여줍니다.

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임의적이고 문맥에 민감한 관계적 능력을 모델링하는 NARS의 가능성을 강조합니다.

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인간의 상징적 인지의 기본적인 측면인 동일/반대 관계적 반응을 계산적으로 구현하는 방법을 제시합니다.

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최소한의 훈련으로 복잡한 관계적 일반화를 도출하는 NARS의 능력을 실험적으로 증명합니다.

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한계점:

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본 연구는 NARS라는 특정 인지 아키텍처에 국한된 결과입니다. 다른 아키텍처에서도 동일한 결과가 나타날지는 추가 연구가 필요합니다.

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실험 환경이 문맥적으로 제어된 MTS 절차에 국한되어, 실제 세계의 복잡한 상황에서의 일반화 가능성은 추가 검증이 필요합니다.

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인간의 관계 학습과의 유사성을 강조하지만, 인간의 인지 과정과의 완벽한 일치는 아직 입증되지 않았습니다.

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