Daily Arxiv

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CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics

Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks

Unlearning as Ablation: Toward a Falsifiable Benchmark for Generative Scientific Discovery

Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games

Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes

Agentic AI for Software: thoughts from Software Engineering community

Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs

Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling

A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

Generative Artificial Intelligence and Agents in Research and Teaching

CALR: Corrective Adaptive Low-Rank Decomposition for Efficient Large Language Model Layer Compression

Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments

Retrieval Enhanced Feedback via In-context Neural Error-book

From Confidence to Collapse in LLM Factual Robustness

On Task Vectors and Gradients

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

DLLMQuant: Quantizing Diffusion-based Large Language Models

LLM - Enhanced Linear Autoencoders for Recommendations

Leveraging GNN to Enhance MEF Method in Predicting ENSO

Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation

New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs

Apple Intelligence Foundation Language Models: Tech Report 2025

SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models

Demographic-aware fine-grained classification of pediatric wrist fractures

Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing

Solar Altitude Guided Scene Illumination

An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table

UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation

Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models

EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion

RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation for Explainable QA Hallucination Detection

Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing

Concept-Guided Interpretability via Neural Chunking

Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study

An Ontology-Driven Graph RAG for Legal Norms: A Hierarchical, Temporal, and Deterministic Approach

Prefill-level Jailbreak: A Black-Box Risk Analysis of Large Language Models

Video CLIP Model for Multi-View Echocardiography Interpretation

A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images

M$^2$IV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering

Noise-based reward-modulated learning

Faster Parameter-Efficient Tuning with Token Redundancy Reduction

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials

Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems

Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements

TableTalk: Scaffolding Spreadsheet Development with a Language Agent

StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel

Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis

Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot

Safe Multiagent Coordination via Entropic Exploration

TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use

Cultural Dimensions of AI Perception: Charting Expectations, Risks, Benefits, Tradeoffs, and Value in Germany and China

CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers

Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

Hierarchical Object-Oriented POMDP Planning for Object Rearrangement

From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification

Secure Reinforcement Learning via Shuffle Privacy Model

Overcoming label shift with target-aware federated learning

Benchmarking XAI Explanations with Human-Aligned Evaluations

HonestCyberEval: An AI Cyber Risk Benchmark for Automated Software Exploitation

Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning

GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration

ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context

Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL

Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis

Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning

Memory augment is All You Need for image restoration

Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data

DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

ST-Raptor: LLM-Powered Semi-Structured Table Question Answering

Route-and-Execute: Auditable Model-Card Matching and Specialty-Level Deployment

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence

Response and Prompt Evaluation to Prevent Parasocial Relationships with Chatbots

Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld

Multi-Agent LLMs as Ethics Advocates for AI-Based Systems

Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions

Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA

MRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation

Jigsaw-Puzzles: From Seeing to Understanding to Reasoning in Vision-Language Models

The Influence of Human-inspired Agentic Sophistication in LLM-driven Strategic Reasoners

YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models

Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation

Can Large Language Models Act as Ensembler for Multi-GNNs?

Pessimistic Iterative Planning with RNNs for Robust POMDPs

Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding

Integrating Large Language Model for Improved Causal Discovery

A Survey on Causal Discovery: Theory and Practice

Generative Interfaces for Language Models

Interpolating Speaker Identities in Embedding Space for Data Expansion

VibeVoice Technical Report

LSD-3D: Large-Scale 3D Driving Scene Generation with Geometry Grounding

Understanding Tool-Integrated Reasoning

Emotions as Ambiguity-aware Ordinal Representations

Real-Time Model Checking for Closed-Loop Robot Reactive Planning

Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld

Created by

Haebom

Author

Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu

Outline

This paper proposes a dynamic multi-agent system (MAS) within the AWorld framework, consisting of executive and supervisory agents, to address the reliability issues that arise when intelligent agents based on large-scale language models (LLMs) utilize external tools to solve complex real-world problems. Specifically, we use a methodology inspired by system identification in control theory to generate performance profiles of executive agents. Based on these profiles, the supervisory agent performs goal-oriented interventions tailored to the agent's unique weaknesses, thereby enhancing the system's robustness. Experimental results using the GAIA dataset demonstrate that the proposed profile-aware MAS significantly outperforms single-agent systems and general supervisory systems in terms of efficiency and stability, achieving the top ranking among open-source projects on the GAIA leaderboard. This demonstrates that building trustworthy intelligent systems requires not only inter-agent collaboration but also an empirical understanding of each agent's unique capabilities and limitations.

Takeaways, Limitations

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Takeaways:

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Presenting the possibility of solving the reliability problem of LLM-based intelligent agents through a multi-agent system.

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Profile-aware surveillance utilizing a control theory-based system identification methodology contributes to efficient and stable system operation.

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The excellence of the proposed system was verified by achieving first place on the GAIA leaderboard.

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Presenting a new paradigm for building trustworthy intelligent systems (emphasizing the need for a deep understanding of the capabilities and limitations of agents).

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Limitations:

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The performance of the proposed system is based on a specific dataset (GAIA), and its generalizability to other datasets or tasks requires further study.

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Dependence on the size and quality of offline training data required for the system identification process.

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Potential increase in computational costs due to increased complexity of profile-aware surveillance.

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Further validation is needed for applicability and generalizability to complex and diverse real-world situations.

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