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.

A Theoretical Framework for Environmental Similarity and Vessel Mobility as Coupled Predictors of Marine Invasive Species Pathways

CareMedEval dataset: Evaluating Critical Appraisal and Reasoning in the Biomedical Field

Node-Based Editing for Multimodal Generation of Text, Audio, Image, and Video

Control Barrier Function for Aligning Large Language Models

A Criminology of Machines

FATE: A Formal Benchmark Series for Frontier Algebra of Multiple Difficulty Levels

PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts

Causal Graph Neural Networks for Healthcare

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

OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights

Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers

Artificial Intelligence in Elementary STEM Education: A Systematic Review of Current Applications and Future Challenges

Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off

TraceTrans: Translation and Spatial Tracing for Surgical Prediction

TowerVision: Understanding and Improving Multilinguality in Vision-Language Models

Gestura: A LVLM-Powered System Bridging Motion and Semantics for Real-Time Free-Form Gesture Understanding

ADPO: Anchored Direct Preference Optimization

Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning

RealDPO: Real or Not Real, that is the Preference

Deep Edge Filter: Return of the Human-Crafted Layer in Deep Learning

Mathematics with large language models as provers and verifiers

Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks

A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG

Learning to Navigate Socially Through Proactive Risk Perception

Training Large Language Models To Reason In Parallel With Global Forking Tokens

ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models

HyperAdapt: Simple High-Rank Adaptation

Comparing Computational Pathology Foundation Models using Representational Similarity Analysis

Back to Ear: Perceptually Driven High Fidelity Music Reconstruction

Test-Time Warmup for Multimodal Large Language Models

Zero-Shot Referring Expression Comprehension via Vison-Language True/False Verification

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

Balancing Quality and Variation: Spam Filtering Distorts Data Label Distributions

A Multi-target Bayesian Transformer Framework for Predicting Cardiovascular Disease Biomarkers during Pandemics

Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System

XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation

Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda

GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units

MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection

Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact

Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation

Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization

HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence

HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts

Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models

Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data

Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents

Two Causally Related Needles in a Video Haystack

Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics

Autocomp: A Powerful and Portable Code Optimizer for Tensor Accelerators

But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectors

Learning Dynamics of RNNs in Closed-Loop Environments

Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks

Multimodal Cancer Modeling in the Age of Foundation Model Embeddings

Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards

TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models

Efficient Model Development through Fine-tuning Transfer

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

Pragmatic Reasoning improves LLM Code Generation

KGGen: Extracting Knowledge Graphs from Plain Text with Language Models

How Memory in Optimization Algorithms Implicitly Modifies the Loss

GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs

Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models

Understanding Adam Requires Better Rotation Dependent Assumptions

Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction

Residual Kolmogorov-Arnold Network for Enhanced Deep Learning

Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction

LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users

Projection Methods for Operator Learning and Universal Approximation

Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting

A Unified Kernel for Neural Network Learning

Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework

SnapStream: Efficient Long Sequence Decoding on Dataflow Accelerators

Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning

BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuning

Toward Clinically Grounded Foundation Models in Pathology

Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation

A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning

Structured Debate Improves Corporate Credit Reasoning in Financial AI

Cross-modal Causal Intervention for Alzheimer's Disease Prediction

Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth

Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation Learning

Building Altruistic and Moral AI Agent with Brain-inspired Emotional Empathy Mechanisms

"Let's Agree to Disagree": Investigating the Disagreement Problem in Explainable AI for Text Summarization

Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysis

Discussion Graph Semantics of First-Order Logic with Equality for Reasoning about Discussion and Argumentation

X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations

Addressing divergent representations from causal interventions on neural networks

Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning

LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems

Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics

Alternative Fairness and Accuracy Optimization in Criminal Justice

RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG

Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering

OUNLP at TSAR 2025 Shared Task: Multi-Round Text Simplifier via Code Generation

RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tables

Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training

Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs

Created by

Haebom

저자

Zichao Hu, Junyi Jessy Li, Arjun Guha, Joydeep Biswas

개요

ROBO-INSTRUCT는 서비스 로봇을 위한 소규모 LLM의 파인튜닝을 위해, 자연어 작업에서 로봇 실행 가능 프로그램으로의 변환을 수행한다. 특정 로봇에 대한 작업-프로그램 쌍 데이터셋 구축의 어려움을 해결하기 위해, ROBO-INSTRUCT는 런타임에 작업별 시뮬레이션 환경을 합성하고 LLM 보조 후처리 절차를 통합하여 로봇 프로그램과의 정렬을 개선한다. 이를 통해 여러 LLM에서 ROBO-INSTRUCT의 효과를 입증하고, 파인튜닝된 모델이 모든 기준선 방법보다 우수하며, 더 크고 독점적인 모델의 성능과 일치하거나 능가함을 보여준다.

시사점, 한계점

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시사점:

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ROBO-INSTRUCT는 특정 로봇에 대한 데이터셋 구축 없이 소규모 LLM을 파인튜닝하여 자연어 작업을 로봇 실행 프로그램으로 변환하는 데 성공적인 접근 방식을 제시한다.

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실시간 시뮬레이션 환경 합성을 통해 물리적 제약 조건 및 로봇 제약 조건을 준수하는 프로그램 생성을 가능하게 한다.

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LLM 보조 후처리 절차를 통해 프로그램의 정확성을 향상시킨다.

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소규모 모델로도 대규모 모델의 성능을 능가하거나 동등한 수준을 달성할 수 있음을 입증했다.

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한계점:

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논문에서 구체적인 한계점은 직접적으로 언급되지 않음. (하지만, 시뮬레이션 환경 구축의 어려움 및 LLM의 한계점을 고려해야 함)

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