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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, 요약본 공유 시 출처만 명기하면 됩니다. This service is supported by Google Gemini.
SpecMAGIC: Specification-Guided Multi-Task Generalization in Multi-Agent Reinforcement Learning
Secure Agentic AI and Multimodal Intelligence in 6G Communication Systems
RoleFlow: Self-Evolving Multi-Role Agentic Collaboration via Structural Adaptation
Soft Ontology Priors over Latent Concept Posteriors for Weakly Supervised Neuro-Symbolic Learning
Route-and-Generate: KL-Regularized Router Improvement for Reasoning Routing in LLMs
Breaking the high-degree barrier in Lehmer's problem with a human-AI discovery loop
Feedback That Backfires in Games: Do LLM Players Repeat the Move They Just Watched Fail?
When Stable and Preferred Semantics Agree: A Path-Based Approach
Neural Networks with Differentiable Belief-Function Layers
Accelerating Wildfire Hazard Mapping Across Canada with an AI Surrogate Modelling Framework
MCP vs CLI for AI Agents: A Comparative Analysis of Tool Integration Strategies for Autonomous Systems
The Cost of Saying Hello: An Empirical Study of Handshake Overhead in AI Agent Communication Protocols
To Manipulate or Not: Opinion Dynamics in LLM Interactions
Speedrunning Relational Foundation Model Pretraining
Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs
A general-purpose System One decision model for clinical records
ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems
Physical AI: Concepts, Techniques, and Challenges
M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement
Teaching AI Agent to Play Poker Strategically Through Preference Optimization
Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
LightCRS: Lightweight Multi-Domain Conversational Recommendation System
MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning
Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 4
Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing
DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
Large language model and reinforcement learning based autonomous construction and progress updates
Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
Can Compact Open-Weight Large Language Models Recover Mobility Events from Incomplete Trajectories? A Controlled Evaluation Using Air Trips
AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment
Cortex: A Fixed-Point Theory of Governed Coding Agents - Technical Report
Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL
Training-Free Prompt Selection for Efficient Reinforcement Learning With Partial Rollouts
Distillation, Self-Training, and the Fragility of Learned Chemical Reasoning
Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using Agents
Self-Improving Video-to-Action Models
TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU
A Conditional No-Hair Theorem for Transformers: How Language Models Forget Their Prompt
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Cache2LoRA: Understanding and Improving Context Internalization
Persistent Memory and Iterative Improvement in an LLM Agent for Chinese-Character Drawing
World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds
LogicVal:A Logic-Formalized Benchmark for Constraint Satisfaction in Text-to-Image Generation
Unified Multiparadigm Fusion Intelligence for Rare Disease Diagnosis
Beyond Explicit Intent: Discovering and Shaping Artistic Intent through Human-AI Co-Creation
What Social Media Teaches Us About Accumulative AI Risks
A Controlled LLM Workflow for Evidence-Grounded Diagnosis of Risk Score Differences
Doing Less with More: A First-Principles Exploration of the Suitability of Agentic Computing over Alternative Architectural Choices
GardenBench: A lightweight, daily evaluation of LLM capabilities
FlexUMM: Flexible Representation Learning for Unified Multimodal Models
Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation
From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents
At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier
Repairing LLM-Generated Specifications with Deterministic Diagnosis
FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds
Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks
Testing the Planning Claims of LLM-Based Agents against Formal Semantics
Harness-Delta Attribution: Dissecting Gains From Harness Evolution
Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference
Functional Welfare for AI Safety
Learnable Splines for Unstructured Neural Operators
Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability
From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems
PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents
Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation
From Events to Reasoning: Hierarchical Rule Composition over Explicit States
Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation
Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides
Defining and Generating Compositional Structure
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety
Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
Temporal Specification Guidance for Text-to-Video Generation
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
Open-world AI: a Tale of Three Requirements
ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
L2G-SR: Learning to Guide LLM-based Symbolic Regression
IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models
When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
Evaluating Model Capabilities After Benchmark Saturation
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
추가

Evaluating the Fitness of Ontologies for the Task of Question Generation

작성자
Haebom
카테고리
비어 있음

저자

Samah Alkhuzaey, Floriana Grasso, Terry R. Payne, Valentina Tamma

개요

본 논문은 교육적 환경에서 자동 질문 생성(AQG)을 위한 온톨로지 적합성 평가에 초점을 맞추고 있다. 기존 연구에서 온톨로지의 질이 AQG 시스템의 효과에 중요한 영향을 미친다는 점을 간과했기에, 본 연구는 ROMEO 방법론을 이용하여 AQG에 특화된 온톨로지 평가 지표를 제시하고, 다양한 온톨로지에 대한 평가를 수행한다. 연구 결과는 온톨로지의 특성이 AQG의 효과에 상당한 영향을 미치며, 온톨로지 간 성능 차이가 존재함을 보여준다. 따라서 AQG 작업에 대한 온톨로지 품질 평가의 중요성을 강조한다.

시사점, 한계점

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시사점:
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AQG를 위한 온톨로지 평가 기준 및 지표를 제시하여 온톨로지의 질적 향상 및 AQG 시스템 성능 개선에 기여할 수 있다.
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다양한 온톨로지의 AQG 성능 비교 분석을 통해 온톨로지 설계 및 선택에 대한 실질적인 가이드라인을 제공한다.
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교육용 질문 생성 시스템 개발에 중요한 시사점을 제공한다.
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한계점:
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제시된 지표 및 평가 방법론의 일반화 가능성에 대한 추가적인 검증이 필요하다.
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다양한 유형의 질문 생성에 대한 평가가 아닌 특정 유형의 질문 생성에 국한될 수 있다.
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평가에 사용된 온톨로지의 수와 다양성이 제한적일 수 있다.
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