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arxiv: 2605.27850 · v1 · pith:Z4YMPVAAnew · submitted 2026-05-27 · 💻 cs.AI

TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems

Yi Ding , Zijie Xuan , Haowei Zhou , Zhenyu Ju , Xiaoxiao Dong , Jingwen Zhang , Xingyu Zhu , Leixin Sun
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Haochi Zhang
This is my paper

Pith reviewed 2026-06-29 12:52 UTC · model grok-4.3

classification 💻 cs.AI
keywords multi-agent systemsprompt evolutioncommunication topologyco-evolutioncost-aware designPareto optimization
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The pith

Jointly evolving prompts and communication topologies provides a route to cost-aware multi-agent system design.

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

The paper argues that prompts and communication graphs cannot be chosen separately because what an agent does depends on the information it receives and the value of any communication edge depends on how the receiving agent interprets it. It therefore treats prompts and topologies as one unified genome and evolves them together under three objectives: task performance, token cost, and structural complexity. An initialization-time landscape probe sets the starting search direction and Pareto-front diagnostics adjust exploration during the run. On controlled evaluations with a fixed model backbone the resulting systems reach high accuracy while using substantially fewer tokens than systems that fix either prompts or graphs in advance.

Core claim

TCP-MCP searches agent prompts and communication topologies as a single genome. It begins with an initialization-time landscape probe to calibrate early behavior, then uses Pareto-front diagnostics to balance three objectives during evolution. The resulting systems reach 82.66 percent accuracy on MMLU-Pro, 89.96 percent on MMLU, and 96.61 percent on GSM8K while using up to 5.69 times fewer tokens than debate-style systems at the reported points and outperforming automated graph-generation baselines.

What carries the argument

The unified genome that represents both prompts and communication topologies together, evolved with an initialization-time landscape probe and Pareto-front adaptation under performance, cost, and complexity objectives.

If this is right

  • Communication topologies become task-specific rather than fixed in advance.
  • Token budgets can be treated as an explicit, optimizable objective alongside accuracy.
  • Manual tuning of either prompts or graphs in isolation is expected to be suboptimal.
  • The same backbone model can support multiple operating points on the performance-cost frontier.

Where Pith is reading between the lines

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

  • The approach suggests that existing single-agent prompt libraries could be extended by jointly searching over small communication graphs.
  • If the landscape probe generalizes, similar calibration steps might reduce the number of generations needed in other evolutionary prompt methods.
  • The three-objective Pareto framing could be tested on domains where communication cost is measured in latency rather than tokens.

Load-bearing premise

The landscape probe at initialization and the Pareto-front diagnostics guide the search without introducing post-hoc bias or overfitting to the chosen benchmarks.

What would settle it

Apply the same co-evolution procedure to a fourth benchmark outside the three used in the evaluations and check whether the reported accuracy and token-cost advantages are preserved.

Figures

Figures reproduced from arXiv: 2605.27850 by Haochi Zhang, Haowei Zhou, Jingwen Zhang, Leixin Sun, Xiaoxiao Dong, Xingyu Zhu, Yi Ding, Zhenyu Ju, Zijie Xuan.

Figure 1
Figure 1. Figure 1: Overall framework of TCP-MCP for joint prompt–topology co-evolution. Each candidate [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of structural crossover, radical mutation, and role mutation in TCP-MCP. The [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Development-set Pareto fronts. Stars mark the representative TCP-MCP points selected for [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evolutionary trends (Q2) showing token cost and structural complexity across generations. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Task-adaptive structural prototypes evolved for different reasoning requirements. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Supplementary visualization of accuracy and token cost for the main results in Table 1. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
read the original abstract

Effective multi-agent systems cannot be designed by selecting prompts or communication graphs in isolation. Agent behavior depends on the information an agent receives, while the usefulness of a communication edge depends on how the receiving agent interprets and uses that information. We propose \textbf{TCP-MCP} (Topology-Coupled Prompting for Multi-Agent Collaborative Problem-Solving), a co-evolution framework that searches agent prompts and communication topologies as a unified genome. TCP-MCP uses an initialization-time landscape probe to calibrate early search behavior, and then relies on Pareto-front diagnostics to adapt exploration under three objectives: task performance, token cost, and structural complexity. Using the same DeepSeek-V3.2 backbone across all methods, TCP-MCP achieves 82.66\%, 89.96\%, and 96.61\% accuracy on MMLU-Pro, MMLU, and GSM8K, respectively. Across the three benchmarks, it consistently outperforms automated graph-generation baselines and achieves competitive accuracy relative to debate-style systems, while using up to 5.69$\times$ fewer tokens than those systems at the reported operating points. These results show that jointly evolving prompts and communication structure provides a practical route to cost-aware and task-adaptive multi-agent system design in controlled evaluations.

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 / 1 minor

Summary. The paper proposes TCP-MCP, a co-evolution framework that jointly searches agent prompts and communication topologies as a unified genome. It employs an initialization-time landscape probe to calibrate search and Pareto-front diagnostics to balance three objectives (task performance, token cost, structural complexity). Using a fixed DeepSeek-V3.2 backbone, the method reports 82.66% accuracy on MMLU-Pro, 89.96% on MMLU, and 96.61% on GSM8K, outperforming automated graph-generation baselines while using up to 5.69× fewer tokens than debate-style systems at the reported points.

Significance. If the empirical results prove reproducible, the work demonstrates a practical route to cost-aware multi-agent design by treating prompts and topologies as coupled variables rather than independent choices. The consistent backbone across comparisons and the explicit multi-objective Pareto handling are positive features that support fair evaluation of the joint-evolution claim.

major comments (2)
  1. [Abstract] Abstract: the reported accuracy figures (82.66%, 89.96%, 96.61%) and token-reduction claims are presented without error bars, statistical tests, baseline implementation details, data-exclusion rules, or the full experimental protocol, leaving the central empirical claims without verifiable support from the provided text.
  2. [Framework] Framework description (implied in §3): the initialization-time landscape probe and Pareto-front diagnostics are positioned as the mechanisms that prevent post-hoc bias and overfitting to the three benchmarks, yet no concrete implementation parameters, pseudocode, or cross-benchmark validation procedure is supplied, so it is impossible to assess whether these components reliably calibrate the search as claimed.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'up to 5.69× fewer tokens than those systems at the reported operating points' should explicitly name the comparison systems and the exact operating points for each benchmark.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need for greater transparency in our empirical claims and framework details. We address each point below and will revise the manuscript to improve reproducibility while preserving the core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported accuracy figures (82.66%, 89.96%, 96.61%) and token-reduction claims are presented without error bars, statistical tests, baseline implementation details, data-exclusion rules, or the full experimental protocol, leaving the central empirical claims without verifiable support from the provided text.

    Authors: We agree the abstract is too terse to support the claims on its own. In revision we will expand it to note that accuracies are means over 5 independent runs, with standard deviations and full protocol (including identical DeepSeek-V3.2 backbone, no data exclusion beyond benchmark defaults, and token counting procedure) provided in Section 4 and Appendix B. Baseline implementation details will be cross-referenced to the same section. This addresses the verifiability concern without altering the reported operating points. revision: yes

  2. Referee: [Framework] Framework description (implied in §3): the initialization-time landscape probe and Pareto-front diagnostics are positioned as the mechanisms that prevent post-hoc bias and overfitting to the three benchmarks, yet no concrete implementation parameters, pseudocode, or cross-benchmark validation procedure is supplied, so it is impossible to assess whether these components reliably calibrate the search as claimed.

    Authors: Section 3 currently gives a high-level description. We will add (i) explicit parameter values (probe size = 50 genomes, Pareto selection threshold = 0.15 hypervolume improvement), (ii) pseudocode for both the landscape probe and the diagnostic update rule, and (iii) a new cross-benchmark validation subsection in Section 4.3 that reports probe calibration statistics and Pareto stability across all three tasks. These additions will allow direct assessment of the claimed bias-prevention behavior. revision: yes

Circularity Check

0 steps flagged

No significant circularity in claimed derivation chain

full rationale

The paper describes an empirical co-evolution framework (TCP-MCP) that jointly searches prompts and communication topologies using an initialization-time landscape probe and Pareto-front diagnostics under three objectives. Results are presented as direct empirical measurements (accuracies and token reductions on MMLU-Pro, MMLU, GSM8K) against baselines, with no equations, derivations, or self-referential definitions that reduce outputs to fitted inputs by construction. The central claims rest on controlled evaluations rather than load-bearing mathematical steps or self-citation chains that collapse to the target result. This is the expected non-finding for an applied empirical methods paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal ledger entries; the framework rests on the domain assumption that agent behavior and edge utility are interdependent, with no free parameters or invented entities explicitly quantified.

axioms (1)
  • domain assumption Agent behavior depends on received information while communication edge usefulness depends on how the receiving agent interprets that information
    Opening premise of the abstract that motivates the co-evolution approach.

pith-pipeline@v0.9.1-grok · 5787 in / 1326 out tokens · 43085 ms · 2026-06-29T12:52:23.231223+00:00 · methodology

discussion (0)

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