arxiv: 2605.08813 · v1 · submitted 2026-05-09 · 💻 cs.LG

Recognition: no theorem link

AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems

Yulang Chen , Haoxuan Peng , Jinyan Liu , Zichen Wen , Dongrui Liu , Linfeng Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:53 UTC · model grok-4.3

classification 💻 cs.LG

keywords multi-agent systemslarge language modelsworkflow compressiontoken cost reductionagent pruningPareto trade-offimportance scoring

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The pith

AgentSlimming prunes redundant agents from multi-agent LLM workflows to cut token costs by up to 79 percent while preserving performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents AgentSlimming as a plug-and-play method to compress graph-structured multi-agent systems built on large language models. It works by calculating a hybrid importance score for each agent and then removing low-value agents or swapping them for cheaper substitutes, with every change tested against a baseline rule that rejects moves causing clear performance loss. A sympathetic reader would care because current multi-agent setups often grow bloated through manual design or automatic expansion, driving up token usage and expense without adding proportional value. If the approach holds, it would let complex agent teams operate at far lower cost while matching or exceeding original accuracy levels on the same tasks.

Core claim

AgentSlimming compresses multi-agent workflows by first estimating the importance score of each agent with a hybrid mechanism, then removing redundant agents or replacing them with low-cost ones, where each operation is validated using a baseline-anchored acceptance rule to prevent performance collapse. Experiments demonstrate that this yields average token cost reductions of up to 78.9 percent with negligible degradation and occasional accuracy gains.

What carries the argument

The hybrid importance scoring mechanism paired with the baseline-anchored acceptance rule, which ranks agents and enforces safe removal or replacement steps.

If this is right

Existing multi-agent designs can be post-processed to eliminate agents that contribute little to final outputs.
Token consumption, the primary running cost of LLM agents, drops sharply while task quality remains stable.
Some workflows gain accuracy after slimming because interfering or noisy agents are removed.
The method delivers a practical trade-off curve between cost and quality that favors lower expense at near-original performance.
The framework applies directly to any graph-structured agent workflow without requiring redesign of the original system.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same scoring-plus-validation pattern could extend to pruning steps inside single large models or modular neural architectures.
Dynamic versions might monitor agent contributions during runtime and slim on the fly rather than in a one-time compression pass.
The approach invites study of the smallest viable agent count for given task families, potentially revealing new minimal topologies.
It connects naturally to cost-aware design of agent systems, where future methods could optimize topologies from the start instead of pruning afterward.

Load-bearing premise

The hybrid importance scoring reliably flags which agents add little value, and the baseline check is enough to stop any meaningful performance drop when agents are removed or replaced.

What would settle it

Run AgentSlimming on a held-out multi-agent benchmark where the method either produces a large accuracy drop despite passing the acceptance checks or fails to deliver substantial token savings.

Figures

Figures reproduced from arXiv: 2605.08813 by Dongrui Liu, Haoxuan Peng, Jinyan Liu, Linfeng Zhang, Yulang Chen, Zichen Wen.

**Figure 1.** Figure 1: Overview of the pruning and quantization concepts in AgentSlimming. The process begins with a workflow that is initially high-performance but computationally expensive. Then, a hybrid importance evaluation mechanism is utilized to calculate each agent node’s importance score (i.e., the float value), which guides pruning and quantization in AgentSlimming. a search problem. By leveraging Monte Carlo Tree Se… view at source ↗

**Figure 2.** Figure 2: Illustration of AgentSlimming. To identify redundancy, AgentSlimming computes four distinct rankings of each agent node: Degree Centrality, Betweenness Centrality, Cost Comparison and Approximate Shapley value. Both pruning and quantization select candidate nodes based on the computed importance scores, ranking nodes from least to most important and optimizing the lowest-ranked candidate first. After each … view at source ↗

**Figure 3.** Figure 3: Cost-Accuracy Pareto Frontier on the GSM8K dataset. The x-axis represents the average inference cost per problem (USD), and the y-axis denotes accuracy. Data points for our method correspond to varying iteration rounds. These configurations consistently align with the Pareto frontier, demonstrating optimal cost-quality trade-offs compared to baselines. CodeGen AnswerGen Test Custom Ensemble Program 0 10 … view at source ↗

**Figure 4.** Figure 4: Node-type specific compression rates. We report the ratio of nodes processed by each compression strategy across six categories. Blue bars indicate structural removal (Pruning), while red bars indicate model substitution (Quantization). The variation across categories highlights the adaptive nature of our hybrid importance evaluation. flow; for quantization, each variant inherits the best pruned graph der… view at source ↗

read the original abstract

Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal communication topologies is labor-intensive, while automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption. To address this problem, we introduce \textbf{AgentSlimming}, a plug-and-play compression framework for graph-structured multi-agent workflows. Motivated by pruning and quantization in neural networks, AgentSlimming compresses workflows by first estimating the importance score of each agent with a hybrid mechanism, and then removes redundant agents or replaces them with low-cost ones, where each operation is validated using a baseline-anchored acceptance rule to prevent performance collapse. Experiments show that AgentSlimming reduces average token cost by up to 78.9\% with negligible performance degradation, and sometimes even improves accuracy, achieving a strong Pareto-optimal trade-off between cost and quality. \textit{Our code is publicly available at https://github.com/CitrusYL/AgentSlimming

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes AgentSlimming, a plug-and-play compression framework for graph-structured LLM-based multi-agent systems (MAS). It estimates agent importance via a hybrid scoring mechanism, then removes redundant agents or replaces them with lower-cost alternatives, with each change validated against a baseline-anchored acceptance rule to avoid performance collapse. Experiments report up to 78.9% average token cost reduction with negligible degradation and occasional accuracy gains, yielding a strong Pareto trade-off between cost and quality. Code is released publicly.

Significance. If the results hold under scrutiny, the work is significant for addressing the practical problem of bloated, high-cost MAS workflows. By adapting neural-network pruning ideas to agent graphs and providing an explicit safeguard against quality loss, it offers a concrete path toward more efficient MAS deployment. Public code is a clear strength that supports reproducibility and follow-on work.

major comments (2)

[§4] §4 (Experiments): The reported 78.9% token reduction and accuracy claims lack accompanying details on the number of independent runs, statistical significance tests, exact dataset sizes, and the precise weighting or components of the hybrid importance scorer. Without these, the robustness of the Pareto-optimal trade-off cannot be fully evaluated.
[§3.2] §3.2 (Hybrid Importance Scoring): The hybrid mechanism is described at a high level, but no ablation is presented that isolates the contribution of each scoring component or demonstrates that the combined score reliably identifies redundant agents across different MAS topologies.

minor comments (2)

[Abstract] The abstract states that accuracy sometimes improves after slimming, yet the main text does not quantify the magnitude or identify the specific tasks where this occurs.
[Figures] Figure captions and axis labels in the experimental plots could be expanded to include the exact MAS topologies and baseline methods being compared.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important aspects of experimental rigor and methodological validation. We address each major comment point by point below and will revise the manuscript to incorporate the requested details and analyses.

read point-by-point responses

Referee: [§4] §4 (Experiments): The reported 78.9% token reduction and accuracy claims lack accompanying details on the number of independent runs, statistical significance tests, exact dataset sizes, and the precise weighting or components of the hybrid importance scorer. Without these, the robustness of the Pareto-optimal trade-off cannot be fully evaluated.

Authors: We agree that these details are necessary for full evaluation. The original submission emphasized aggregate results for brevity, but we will expand §4 in the revision to include: five independent runs per experiment with different random seeds (reporting mean and standard deviation); paired t-tests confirming statistical significance (p < 0.01 for the reported cost reductions); exact dataset sizes (e.g., GSM8K with 1,319 test examples, HumanEval with 164 problems); and the hybrid scorer components with explicit weights (0.4 × performance impact + 0.35 × token cost + 0.25 × graph centrality). These additions will allow readers to assess the robustness of the Pareto trade-off. revision: yes
Referee: [§3.2] §3.2 (Hybrid Importance Scoring): The hybrid mechanism is described at a high level, but no ablation is presented that isolates the contribution of each scoring component or demonstrates that the combined score reliably identifies redundant agents across different MAS topologies.

Authors: We concur that an ablation study would strengthen the validation of the hybrid scorer. While the manuscript motivates the combination of performance, cost, and structural factors, no such isolation was included originally. In the revised manuscript, we will add an ablation subsection to §3.2 (or a dedicated appendix) that compares performance-only, cost-only, structure-only, and full hybrid variants on the primary benchmarks. We will further demonstrate the combined score across the MAS topologies evaluated in our experiments (linear chains, hierarchical trees, and general graphs) to show its reliability in identifying redundant agents. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces an empirical plug-and-play compression framework for multi-agent workflows based on hybrid importance scoring followed by redundant-agent removal or replacement, with each step guarded by a baseline-anchored acceptance rule. No equations, derivations, or first-principles predictions are present that could reduce the reported token-cost savings or accuracy claims to fitted parameters or self-referential definitions. The central results rest on external experimental validation rather than any load-bearing self-citation chain or ansatz smuggled through prior work, rendering the approach self-contained against the stated benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the hybrid importance estimator and acceptance rule are treated as methodological choices whose details are not supplied.

pith-pipeline@v0.9.0 · 5486 in / 1093 out tokens · 58008 ms · 2026-05-12T01:53:02.273919+00:00 · methodology

discussion (0)

Reference graph

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