Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
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MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI

Soft Tokens, Hard Truths

Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning

When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models

COLT: Enhancing Video Large Language Models with Continual Tool Usage

Do You Need Proprioceptive States in Visuomotor Policies?

CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection

Self-Evolving LLMs via Continual Instruction Tuning

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework

Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning

Equip Pre-ranking with Target Attention by Residual Quantization

Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source

Synthetic bootstrapped pretraining

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models

UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

Structure Matters: Brain Graph Augmentation via Learnable Edge Masking for Data-efficient Psychiatric Diagnosis

Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting

HumAine-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning

EAI-Avatar: Emotion-Aware Interactive Talking Head Generation

SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs

Do AI Companies Make Good on Voluntary Commitments to the White House?

Embedding Alignment in Code Generation for Audio

Kron-LoRA: Hybrid Kronecker-LoRA Adapters for Scalable, Sustainable Fine-tuning

From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs

Measuring Harmfulness of Computer-Using Agents

Enhancing RAG Efficiency with Adaptive Context Compression

CANDLE: A Cross-Modal Agentic Knowledge Distillation Framework for Interpretable Sarcopenia Diagnosis

Assay2Mol: large language model-based drug design using BioAssay context

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

Energy Management for Renewable-Collocated Artificial Intelligence Data Centers

VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation

LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization

Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization

HAZEMATCHING: Dehazing Light Microscopy Images with Guided Conditional Flow Matching

Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

CUPID: Curating Data your Robot Loves with Influence Functions

Quantum-Classical Hybrid Quantized Neural Network

SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model

Why Do Some Inputs Break Low-Bit LLM Quantization?

A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification

CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning

Urania: Differentially Private Insights into AI Use

RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing

OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models

Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning

PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

To Trust Or Not To Trust Your Vision-Language Model's Prediction

SEM: Enhancing Spatial Understanding for Robust Robot Manipulation

Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data

From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora

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

Stepwise Guided Policy Optimization: Coloring your Incorrect Reasoning in GRPO

GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation

EDBench: Large-Scale Electron Density Data for Molecular Modeling

Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare

LEMUR Neural Network Dataset: Towards Seamless AutoML

Towards Visual Text Grounding of Multimodal Large Language Model

Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches

DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework

Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment

Challenges and Trends in Egocentric Vision: A Survey

Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models

Language Models Fail to Introspect About Their Knowledge of Language

Learning to Drive by Imitating Surrounding Vehicles

A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation

Anomaly Detection in Complex Dynamical Systems: A Systematic Framework Using Embedding Theory and Physics-Inspired Consistency

Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions

HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks

SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation

Compact Rule-Based Classifier Learning via Gradient Descent

BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues

Representation Convergence: Mutual Distillation is Secretly a Form of Regularization

Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets

Stylus: Repurposing Stable Diffusion for Training-Free Music Style Transfer on Mel-Spectrograms

Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion

VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model

A GEN AI Framework for Medical Note Generation

Evading Toxicity Detection with ASCII-art: A Benchmark of Spatial Attacks on Moderation Systems

Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation

Robust Training of Neural Networks at Arbitrary Precision and Sparsity

On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting

DeNOTS: Stable Deep Neural ODEs for Time Series

TALEC: Teach Your LLM to Evaluate in Specific Domain with In-house Criteria by Criteria Division and Zero-shot Plus Few-shot

RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models

CLIP Can Understand Depth

CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning

Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity

Markov Decision Processes under External Temporal Processes

MAPO: Mixed Advantage Policy Optimization

CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models

Plan Verification for LLM-Based Embodied Task Completion Agents

GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoning

Compression Strategies for Efficient Multimodal LLMs in Medical Contexts

Emergent Risk Awareness in Rational Agents under Resource Constraints

MI9 -- Agent Intelligence Protocol: Runtime Governance for Agentic AI Systems

Created by

Haebom

Author

Charles L. Wang, Trisha Singhal, Ameya Kelkar, Jason Tuo

Outline

This paper proposes MI9, a runtime governance framework for the safe and responsible deployment of agent-based AI systems, which exhibit unpredictable behavior during execution, unlike existing AI models. MI9 provides real-time control through six integrated components: an agency risk index, agent semantic telemetry collection, continuous permission monitoring, an FSM-based compliance engine, target-conditional deviation detection, and a progressive isolation strategy. Through analysis of various scenarios, we demonstrate the systematic application of MI9 to governance challenges that existing approaches fail to address.

Takeaways, Limitations

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Takeaways:

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We present a novel approach to runtime governance of agent-based AI systems.

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The MI9 framework provides real-time control for the safety and alignment of running agents.

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It provides a foundation for systematically managing agent-related risks that existing approaches fail to address.

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Provides a transparent and unified governance framework that works across diverse agent architectures.

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Provides infrastructure for securely deploying agent-based AI systems at scale.

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Limitations:

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There is a lack of empirical research on the practical application and performance of MI9.

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Further research is needed to explore the applicability and generalizability to different types of agents and environments.

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The complexity of the framework can lead to high implementation and maintenance costs.

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We may not be able to guarantee perfect response to unexpected agent behavior.

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