# KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems

arXiv: https://arxiv.org/abs/2502.07350 · HTML: https://mlsanity.com/paper/2502.07350/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 35 of its 51 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 35)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [Large Language Model Routing with Benchmark Datasets](https://mlsanity.com/paper/2309.15789/) (2023)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models](https://mlsanity.com/paper/2311.08692/) (2023)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration](https://mlsanity.com/paper/2404.12715/) (2024)
- [Fusing Models with Complementary Expertise](https://mlsanity.com/paper/2310.01542/) (2023)
- [Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models](https://mlsanity.com/paper/2407.06089/) (2024)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity](https://mlsanity.com/paper/2412.01572/) (2024)
- [Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding](https://mlsanity.com/paper/2502.06020/) (2025)
- [A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health](https://mlsanity.com/paper/2402.14807/) (2024)
- [Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach](https://mlsanity.com/paper/2007.13151/) (2020)
- [Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs](https://mlsanity.com/paper/2209.08767/) (2022)

## Built on by (most cited first, 30 of 37)

- [CF-VLM:CounterFactual Vision-Language Fine-tuning](https://mlsanity.com/paper/2506.17267/) (2025)
- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [MAT-Agent: Adaptive Multi-Agent Training Optimization](https://mlsanity.com/paper/2510.17845/) (2025)
- [Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control](https://mlsanity.com/paper/2508.08134/) (2025)
- [ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment](https://mlsanity.com/paper/2509.17818/) (2025)
- [FastVMT: Eliminating Redundancy in Video Motion Transfer](https://mlsanity.com/paper/2602.05551/) (2026)
- [Failure-Driven Workflow Refinement](https://mlsanity.com/paper/2510.10035/) (2025)
- [Learning Dynamics of VLM Finetuning](https://mlsanity.com/paper/2510.11978/) (2025)
- [FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models](https://mlsanity.com/paper/2512.20561/) (2025)
- [Group Editing: Edit Multiple Images in One Go](https://mlsanity.com/paper/2603.22883/) (2026)
- [STORM: Search-Guided Generative World Models for Robotic Manipulation](https://mlsanity.com/paper/2512.18477/) (2025)
- [CoAgent: Collaborative Planning and Consistency Agent for Coherent Video Generation](https://mlsanity.com/paper/2512.22536/) (2025)
- [Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems](https://mlsanity.com/paper/2601.18735/) (2026)
- [GlyphBanana: Advancing Precise Text Rendering Through Agentic Workflows](https://mlsanity.com/paper/2603.12155/) (2026)
- [PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence](https://mlsanity.com/paper/2605.18067/) (2026)
- [Large Language Models as Discounted Bayesian Filters](https://mlsanity.com/paper/2512.18489/) (2025)
- [Adaptive-VoCo: Complexity-Aware Visual Token Compression for Vision-Language Models](https://mlsanity.com/paper/2512.18496/) (2025)
- [ResWM: Residual-Action World Model for Visual RL](https://mlsanity.com/paper/2603.11110/) (2026)
- [Backward-Friendly Optimization: Training Large Language Models with Approximate Gradients under Memory Constraints](https://mlsanity.com/paper/2510.22467/) (2025)
- [Reflective Confidence: Correcting Reasoning Flaws via Online Self-Correction](https://mlsanity.com/paper/2512.18605/) (2025)
- [InstanceAnimator: Multi-Instance Sketch Video Colorization](https://mlsanity.com/paper/2603.25357/) (2026)
- [From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control](https://mlsanity.com/paper/2506.00043/) (2025)
- [DynaPose4D: High-Quality 4D Dynamic Content Generation via Pose Alignment Loss](https://mlsanity.com/paper/2510.22473/) (2025)
- [Guardian: Decoupling Exploration from Safety in Reinforcement Learning](https://mlsanity.com/paper/2510.22859/) (2025)
- [GTMA: Dynamic Representation Optimization for OOD Vision-Language Models](https://mlsanity.com/paper/2512.18504/) (2025)
- [PTTA: A Pure Text-to-Animation Framework for High-Quality Creation](https://mlsanity.com/paper/2512.18614/) (2025)
- [SirenPose: Dynamic Scene Reconstruction via Geometric Supervision](https://mlsanity.com/paper/2512.20531/) (2025)
- [RevFFN: Memory-Efficient Full-Parameter Fine-Tuning of Mixture-of-Experts LLMs with Reversible Blocks](https://mlsanity.com/paper/2512.20920/) (2025)
- [A Three-Level Alignment Framework for Large-Scale 3D Retrieval and Controlled 4D Generation](https://mlsanity.com/paper/2512.22294/) (2025)
- [Learning Dynamic Scene Reconstruction with Sinusoidal Geometric Priors](https://mlsanity.com/paper/2512.22295/) (2025)

## Abstract (arXiv)

As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduces Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination through semantic understanding and dynamic adaptation. The framework features three key innovations: a three-dimensional knowledge distance model for deep semantic understanding, a dual-adaptation mechanism for continuous expert optimization, and a knowledge-aware Thompson Sampling strategy for efficient expert selection. Extensive evaluation demonstrates KABB achieves an optimal cost-performance balance, maintaining high performance while keeping computational demands relatively low in multi-agent coordination.