AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraints

• URL: http://arxiv.org/abs/2512.11426v1
• Date: Fri, 12 Dec 2025 10:08:03 GMT
• Title: AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraints
• Authors: Shuowei Cai, Yansong Ning, Hao Liu,
• Abstract summary: Large Language Model (LLM)-based multi-agent systems (MAS) are becoming indispensable building blocks for web-scale applications.<n>We present AgentBalance, a framework for constructing cost-effective MAS under explicit token-cost and latency budgets.
• Score: 7.38359558170225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM)-based multi-agent systems (MAS) are becoming indispensable building blocks for web-scale applications such as web search, social network analytics, and online customer support, where cost-effectiveness is increasingly the primary constraint for large-scale deployment. While recent work improves MAS cost-effectiveness by shaping inter-agent communication topologies and selecting agent backbones, it rarely models and optimizes under explicit token-cost and latency budgets that reflect deployment constraints. This often leads to topology-first designs and suboptimal cost-effectiveness when budgets are binding. We present AgentBalance, a framework for constructing cost-effective MAS under explicit token-cost and latency budgets via a backbone-then-topology design. AgentBalance first performs backbone-oriented agent generation, constructing agents with heterogeneous backbones through LLM pool construction, pool selection, and role-backbone matching. It then performs adaptive MAS topology generation, guiding inter-agent communication via agent representation learning, gating, and latency-aware topology synthesis. Experiments on benchmarks with 14 candidate LLM backbones show that AgentBalance achieves up to 10% and 22% performance gains under matched token-cost and latency budgets, respectively, and yields strong AUC on performance-versus-budget curves across benchmarks. AgentBalance also functions as a plug-in for existing MAS, improving performance under the same token-cost and latency constraints, and it generalizes well to unseen LLMs for practical, budget-aware deployment. Code: https://github.com/usail-hkust/AgentBalance

Related papers

• Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory [56.0946692457838]
  BudgetMem is a runtime agent memory framework for explicit, query-aware performance-cost control.<n>A lightweight router performs budget-tier routing across modules to balance task performance and memory construction cost.<n>Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized.
  arXiv  Detail & Related papers  (2026-02-05T18:57:09Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• BAMAS: Structuring Budget-Aware Multi-Agent Systems [18.99441110805831]
  Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks.<n>We propose BAMAS, a novel approach for building multi-agent systems with budget awareness.<n>Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.
  arXiv  Detail & Related papers  (2025-11-26T16:48:18Z)
• Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models [97.55009021098554]
  This work aims to identify the key determinants of SLMs' real-device latency and offer generalizable principles and methodologies for SLM design and training.<n>We introduce a new family of hybrid SLMs, called Nemotron-Flash, which significantly advances the accuracy-efficiency frontier of state-of-the-art SLMs.
  arXiv  Detail & Related papers  (2025-11-24T08:46:36Z)
• Budget-Aware Tool-Use Enables Effective Agent Scaling [82.6942342482552]
  Scaling test-time computation improves performance across different tasks on large language models (LLMs)<n>We study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents.<n>We introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness.
  arXiv  Detail & Related papers  (2025-11-21T07:18:55Z)
• Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning [53.57360296655208]
  Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs.<n>Existing approaches rely on decentralized frameworks, which invoke multiple LLMs for every input and thus lead to substantial and uncontrolled inference costs.<n>We introduce a centralized multi-LLM framework, where a controller LLM selectively coordinates a pool of expert models in a cost-efficient and cost-controllable manner.
  arXiv  Detail & Related papers  (2025-11-04T17:35:17Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems [40.44248136759827]
  We introduce MAS$2$, a multi-agent system that autonomously architects bespoke multi-agent systems.<n> MAS$2$ achieves performance gains of up to $19.6%$ over state-of-the-art MAS.
  arXiv  Detail & Related papers  (2025-09-29T06:20:10Z)
• syftr: Pareto-Optimal Generative AI [40.80352098169579]
  syftr is a framework that performs efficient multi-objective search over a broad space of agentic and non-agentic RAG configurations.<n>Syftr finds flows which are on average approximately 9 times cheaper while preserving most of the accuracy of the most accurate flows.
  arXiv  Detail & Related papers  (2025-05-26T17:43:13Z)
• Scalable Chain of Thoughts via Elastic Reasoning [61.75753924952059]
  Elastic Reasoning is a novel framework for scalable chain of thoughts.<n>It separates reasoning into two phases--thinking and solution--with independently allocated budgets.<n>Our approach produces more concise and efficient reasoning even in unconstrained settings.
  arXiv  Detail & Related papers  (2025-05-08T15:01:06Z)
• Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems [42.137278756052595]
  $texttAgentPrune$ can seamlessly integrate into mainstream multi-agent systems. textbf(I) integrates seamlessly into existing multi-agent frameworks with $28.1%sim72.8%downarrow$ token reduction. textbf(III) successfully defend against two types of agent-based adversarial attacks with $3.5%sim10.8%uparrow$ performance boost.
  arXiv  Detail & Related papers  (2024-10-03T14:14:31Z)
• A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning [34.85351481228439]
  We propose a unified coordinating framework to efficiently utilize existing ML-based agents. We show that it can effectively utilize different ML-based agents and find better configurations with 1.414.1X speedups on the workload execution time.
  arXiv  Detail & Related papers  (2023-03-10T05:27:23Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.