arxiv: 2604.09297 · v1 · submitted 2026-04-10 · 💻 cs.SE · cs.AI

Recognition: unknown

SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering

Jingzhi Gong , Ruizhen Gu , Zhiwei Fei , Yazhuo Cao , Lukas Twist , Alina Geiger , Shuo Han , Dominik Sobania

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Federica Sarro Jie M. Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:21 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords multi-objective optimizationLLM coding agentsskill bundlessoftware engineeringNSGA-IIautomated prompt evolutionagent skillsfailure-driven editing

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The pith

SkillMOO evolves skill bundles for LLM coding agents by combining LLM-proposed edits with NSGA-II selection to raise pass rates while lowering cost.

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

The paper presents SkillMOO as a way to replace manual tuning of agent skills with an automated loop. A solver agent tests each candidate bundle on coding tasks while an optimizer agent suggests changes based on where the bundle failed. NSGA-II keeps the best bundles across generations by balancing success, cost, and runtime. On three SkillsBench tasks the resulting bundles reach higher pass rates than the strongest baseline for each task and do so at reduced cost. Analysis of the changes shows that removing or replacing instructions accounts for most of the gains.

Core claim

SkillMOO frames skill-bundle design as a multi-objective search problem. An LLM solver runs each bundle on the target coding tasks and records pass rate, token cost, and runtime. An LLM optimizer then examines the failures and proposes concrete edits such as pruning, substitution, or addition of instructions. NSGA-II maintains a population of bundles and selects survivors according to non-dominated trade-offs. On three SkillsBench software engineering tasks the evolved bundles improve pass rate by up to 131 percent and reduce cost by up to 32 percent relative to the best baseline per task, while the total optimization overhead stays low. Post-hoc pattern analysis indicates that the winning,

What carries the argument

The central mechanism is the closed loop of LLM-proposed edits to skill bundles, guided by failure analysis from the solver, followed by NSGA-II non-dominated sorting to retain bundles that improve the success-cost-runtime trade-off.

Load-bearing premise

LLM-proposed edits based on failure analysis will keep producing better bundles that generalize beyond the three benchmark tasks without introducing unmeasured costs.

What would settle it

Apply the same optimization process to a fresh set of coding tasks never seen during evolution and check whether the reported gains in pass rate and cost reduction remain or shrink.

Figures

Figures reproduced from arXiv: 2604.09297 by Alina Geiger, Dominik Sobania, Federica Sarro, Jie M. Zhang, Jingzhi Gong, Lukas Twist, Ruizhen Gu, Shuo Han, Yazhuo Cao, Zhiwei Fei.

**Figure 1.** Figure 1: SkillMOO workflow: solver-optimizer loop with evolving skill bundles. This motivates search-based, data-driven skill optimization, and thereby we propose SkillMOO, a multi-objective optimization (MOO) framework that automatically evolves task-specific skill bundles for SE tasks using LLM-proposed edits and NSGA-II survivor selection on pass rate and cost: a task solver agent evaluates candidate skill bund… view at source ↗

read the original abstract

Agent skills provide modular, task-specific guidance for LLM- based coding agents, but manually tuning skill bundles to balance success rate, cost, and runtime is expensive and fragile. We present SkillMOO, a multi-objective optimization framework that automatically evolves skill bundles using LLM-proposed edits and NSGA-II survivor selection: a solver agent evaluates candidate skill bundles on coding tasks and an optimizer agent proposes bundle edits based on failure analysis. On three SkillsBench software engineering tasks, SkillMOO improves pass rate by up to 131% while reducing cost up to 32% relative to the best baseline per task at low optimization overhead. Pattern analysis reveals pruning and substitution as primary drivers of improvement, suggesting effective bundles favor minimal, focused content over accumulated instructions.

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.

Desk Editor's Note private letter to a colleague

SkillMOO shows a workable LLM-plus-NSGA-II loop for tuning agent skill bundles and reports clear gains on the benchmark tasks, but the gains come from optimizing directly on the same three tasks used for final reporting.

read the letter

SkillMOO automates the tuning of skill bundles for LLM coding agents. One agent proposes edits after looking at failures, NSGA-II keeps the better bundles across the three objectives, and the result is higher pass rates and lower cost on the reported tasks. The main new piece is applying this evolutionary loop with LLM-guided edits to the specific problem of agent skill configuration in software engineering. The pattern analysis that pruning and substitution drive most of the improvement is a concrete, usable observation that lines up with what many practitioners already suspect about prompt bloat.

Referee Report

3 major / 2 minor

Summary. The paper introduces SkillMOO, a multi-objective optimization framework for evolving skill bundles in LLM-based coding agents. An optimizer agent proposes edits to bundles based on failure analysis from a solver agent, with NSGA-II used for survivor selection across objectives of pass rate, cost, and runtime. On three SkillsBench software engineering tasks, SkillMOO reports pass-rate gains of up to 131% and cost reductions of up to 32% versus the best per-task baseline, at low optimization overhead. Pattern analysis attributes gains primarily to pruning and substitution of skills, favoring minimal focused bundles over accumulated instructions.

Significance. If the reported gains hold under proper controls for generalization, the work offers a practical method to automate skill tuning that is otherwise manual and brittle, potentially improving efficiency in LLM agent systems for software engineering. The combination of LLM-guided editing with NSGA-II provides an empirical template for multi-objective agent optimization that could be extended beyond the evaluated tasks.

major comments (3)

[Experimental methodology] Experimental methodology (likely §4): Skill bundle optimization, including solver evaluations and optimizer edits guided by failure analysis, occurs on the identical three SkillsBench tasks used for final performance reporting. No held-out validation tasks, cross-task transfer tests, or separation between optimization and evaluation sets are described. This setup allows discovered bundles to exploit task-specific patterns, directly undermining the claim that the bundles are generally superior rather than overfit.
[Results section] Results section (likely §5): Quantitative claims of up to 131% pass-rate improvement and 32% cost reduction are presented without reported details on number of independent runs, statistical significance tests, standard deviations, or precise definitions and measurement protocols for cost and runtime. This absence prevents verification that the gains exceed noise or baseline variability and are not due to post-hoc selection.
[Baselines and comparison] Baselines and comparison (likely §5.1): The paper compares against 'the best baseline per task' but does not clarify whether those baselines received equivalent optimization effort or were static; if baselines were not similarly tuned, the relative gains may be inflated by unequal treatment rather than intrinsic superiority of the SkillMOO bundles.

minor comments (2)

[Abstract] The abstract states 'low optimization overhead' without a quantitative metric (e.g., number of LLM calls or wall-clock time) in the summary; a table or figure reporting overhead would improve clarity.
[Method] Notation for the three objectives (pass rate, cost, runtime) and the NSGA-II crowding distance or dominance criteria could be formalized with equations to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below with clarifications and indicate planned revisions to the manuscript.

read point-by-point responses

Referee: [Experimental methodology] Experimental methodology (likely §4): Skill bundle optimization, including solver evaluations and optimizer edits guided by failure analysis, occurs on the identical three SkillsBench tasks used for final performance reporting. No held-out validation tasks, cross-task transfer tests, or separation between optimization and evaluation sets are described. This setup allows discovered bundles to exploit task-specific patterns, directly undermining the claim that the bundles are generally superior rather than overfit.

Authors: We agree that the optimization and final evaluation were performed on the same three tasks, which constrains strong claims of general superiority. This design was chosen to isolate the effects of the multi-objective optimization process on representative SkillsBench tasks. In the revised manuscript we will add an explicit limitations subsection that discusses the risk of task-specific overfitting and will outline future work on held-out tasks and cross-task transfer. We will also moderate language in the abstract and conclusion to emphasize the framework rather than universal bundle superiority. New held-out experiments are not feasible in the current revision cycle. revision: partial
Referee: [Results section] Results section (likely §5): Quantitative claims of up to 131% pass-rate improvement and 32% cost reduction are presented without reported details on number of independent runs, statistical significance tests, standard deviations, or precise definitions and measurement protocols for cost and runtime. This absence prevents verification that the gains exceed noise or baseline variability and are not due to post-hoc selection.

Authors: The referee correctly notes that these experimental details were omitted. All reported results were obtained from five independent runs per configuration. In the revised results section we will state the number of runs, report standard deviations alongside all metrics, provide precise operational definitions (cost measured as aggregate token usage of both solver and optimizer agents; runtime as mean wall-clock seconds per task), and include statistical significance via Wilcoxon signed-rank tests comparing SkillMOO bundles against baselines. These additions will allow readers to evaluate whether the gains exceed variability. revision: yes
Referee: [Baselines and comparison] Baselines and comparison (likely §5.1): The paper compares against 'the best baseline per task' but does not clarify whether those baselines received equivalent optimization effort or were static; if baselines were not similarly tuned, the relative gains may be inflated by unequal treatment rather than intrinsic superiority of the SkillMOO bundles.

Authors: The baselines are the static, manually authored skill bundles supplied with the SkillsBench tasks; they were not subjected to any automated optimization. The comparison is therefore between typical manual practice and our automated framework. We will revise §5.1 to state this explicitly. To strengthen the comparison we will also add results against a random-search baseline that receives the same number of bundle evaluations as SkillMOO, thereby controlling for optimization effort. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; results are empirical measurements

full rationale

The paper introduces SkillMOO as a procedural framework that applies LLM-proposed edits and NSGA-II selection to evolve skill bundles, then directly measures pass-rate and cost outcomes on three SkillsBench tasks. No equations, fitted parameters, or predictions are described that reduce to the input data or benchmark results by construction. The central claims rest on reported empirical deltas rather than any self-definitional, self-citation load-bearing, or ansatz-smuggled steps. The method and evaluation are self-contained against the external benchmark without invoking uniqueness theorems or renaming known results as derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond the high-level description of the two-agent architecture; the central claim rests on unstated assumptions about benchmark representativeness and LLM edit quality.

pith-pipeline@v0.9.0 · 5450 in / 1188 out tokens · 36640 ms · 2026-05-10T17:21:55.619579+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

11 extracted references · 8 canonical work pages · 3 internal anchors

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work page arXiv 2025
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