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arxiv: 2606.18051 · v1 · pith:UHT322ERnew · submitted 2026-06-16 · 💻 cs.CL

Compositional Skill Routing for LLM Agents: Decompose, Retrieve, and Compose

Xueping Gao This is my paper

Pith reviewed 2026-06-27 00:34 UTC · model grok-4.3

classification 💻 cs.CL
keywords compositional skill routingtask decompositionskill retrievalLLM agentsCompSkillBenchSkillWeaverIterative Skill-Aware Decomposition
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The pith

Iterative skill-aware decomposition raises accuracy from 51% to 67.7% for routing complex queries to multiple LLM skills.

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

The paper formalizes compositional skill routing as the need to break a user query into sub-tasks, retrieve matching skills from a large library, and assemble an executable plan. Standard LLM decomposition proves the main limit, reaching only 34.2 percent category recall at the step level. Iterative Skill-Aware Decomposition addresses this by feeding retrieval results back into the decomposer to refine sub-task granularity. The resulting SkillWeaver pipeline, tested on a new benchmark of 300 queries over 2,209 real skills, cuts context consumption by over 99 percent and still improves performance on categories absent from the retrieval pool.

Core claim

Task decomposition quality is the primary bottleneck in compositional skill routing, and Iterative Skill-Aware Decomposition improves decomposition accuracy from 51.0 percent to 67.7 percent in a single iteration by iteratively aligning sub-tasks with the skills present in the library.

What carries the argument

Iterative Skill-Aware Decomposition (SAD), a retrieval-augmented feedback loop that uses retrieved skills to refine the LLM's task decomposition.

If this is right

  • When decomposition reaches correct granularity, category recall at retrieval rises from 34 percent to 41 percent.
  • SkillWeaver reduces context window consumption by over 99 percent compared with direct prompting.
  • The gains transfer to skill categories absent from the retrieval pool, yielding a 35.6 percent relative improvement in decomposition accuracy.

Where Pith is reading between the lines

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

  • The same feedback loop could be applied to other agent frameworks that already maintain large tool libraries.
  • Running more than one iteration of SAD might produce further gains if the marginal improvement curve is measured.
  • Replacing the static benchmark with live, changing skill sets would test whether the reported generalization holds under distribution shift.

Load-bearing premise

Correct task granularity is required before retrieval can succeed, and the benchmark queries and skill library reflect the compositional demands that arise in practice.

What would settle it

An end-to-end evaluation in which high decomposition accuracy fails to produce better executable plans on tasks drawn from a different skill ecosystem would show that decomposition quality is not the primary bottleneck.

Figures

Figures reproduced from arXiv: 2606.18051 by Xueping Gao.

Figure 1
Figure 1. Figure 1: Overview of SKILLWEAVER. A query is decomposed into sub-tasks, each matched to skills via bi￾encoder retrieval, then composed into a DAG. Dashed arrows: SAD feedback loop (§4.4). curates large-scale tool-calling datasets for fine￾tuning. Code-first agent frameworks such as TaskWeaver (Qiao et al., 2024) address execution orchestration but not skill retrieval. These efforts are complementary: they address h… view at source ↗
Figure 2
Figure 2. Figure 2: SAD convergence. DA (left axis) converges [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
read the original abstract

LLM agents increasingly rely on external skills -- reusable tool specifications -- but real-world tasks often require composing multiple skills, not just selecting one. We formalize this as the Compositional Skill Routing problem: given a complex user query and a large skill library, decompose the query into atomic sub-tasks, retrieve the appropriate skill for each sub-task, and compose an executable plan. We present SkillWeaver, a decompose-retrieve-compose framework combining an LLM task decomposer, a bi-encoder skill retriever with FAISS indexing, and a dependency-aware DAG planner. To support evaluation, we introduce CompSkillBench, a benchmark of 300 compositional queries over 2,209 real MCP server skills spanning 24 functional categories, sourced from the public MCP ecosystem. Our experiments reveal that task decomposition quality is the primary bottleneck: standard LLM decomposition reaches only 34.2% category recall at the step level. To address this, we propose Iterative Skill-Aware Decomposition (SAD), a retrieval-augmented feedback loop that iteratively aligns decomposition with available skills. SAD improves decomposition accuracy from 51.0% to 67.7% (+32.7%, Wilcoxon p < 10^-6) in a single iteration; DA-conditioned analysis confirms that correct granularity is the prerequisite for effective retrieval (CatR@1 rises from 34% to 41% when DA=1). SkillWeaver reduces context window consumption by over 99%, and transfer experiments confirm generalization (+35.6% relative DA gain even when target categories are absent from the retrieval pool).

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

3 major / 1 minor

Summary. The paper formalizes the Compositional Skill Routing problem for LLM agents and introduces SkillWeaver, a decompose-retrieve-compose framework using an LLM task decomposer, bi-encoder retriever with FAISS, and dependency-aware DAG planner. It presents CompSkillBench (300 queries over 2,209 MCP skills in 24 categories) and proposes Iterative Skill-Aware Decomposition (SAD) to address decomposition bottlenecks. Key claims include SAD raising decomposition accuracy from 51.0% to 67.7% (+32.7%, Wilcoxon p < 10^-6), >99% context reduction, and +35.6% relative DA gain on unseen categories, with DA=1 shown as prerequisite via conditioned analysis (CatR@1 34%→41%).

Significance. If the empirical claims hold after addressing gaps in experimental detail, the work would provide a practical framework and benchmark for compositional tool use in agents, highlighting decomposition as the primary bottleneck and demonstrating substantial efficiency gains. The introduction of a benchmark grounded in real MCP skills and the use of a statistical test are positive elements that could support follow-on research.

major comments (3)
  1. [Abstract] Abstract: The central claim of SAD improving decomposition accuracy from 51.0% to 67.7% lacks any definition of the accuracy metric, description of how the 51.0% baseline was obtained, or details on error bars/variance; this directly undermines evaluation of the reported +32.7% lift and Wilcoxon test.
  2. [Abstract] Abstract (DA-conditioned analysis): The statement that correct granularity (DA=1) is a prerequisite for effective retrieval rests on a correlational observation (CatR@1 rising from 34% to 41%) without controls for confounders such as query phrasing or skill-description overlap, nor any causal isolation experiment.
  3. [Abstract] Abstract (CompSkillBench): No information is supplied on sampling method for the 300 queries, how ground-truth decompositions were created, or inter-annotator agreement; these omissions make it impossible to assess whether the benchmark supports the representativeness assumption underlying all transfer and generalization claims.
minor comments (1)
  1. [Abstract] Abstract: The abbreviation 'DA' appears in 'DA-conditioned analysis' and 'DA gain' without an explicit first-use definition or expansion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for your detailed and constructive review. We appreciate the identification of omissions in the abstract that affect clarity and interpretability. We will revise the abstract to incorporate definitions, baseline descriptions, benchmark construction details, and more cautious phrasing for the analysis. Our point-by-point responses are below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of SAD improving decomposition accuracy from 51.0% to 67.7% lacks any definition of the accuracy metric, description of how the 51.0% baseline was obtained, or details on error bars/variance; this directly undermines evaluation of the reported +32.7% lift and Wilcoxon test.

    Authors: We agree the abstract requires additional context. Decomposition accuracy is the fraction of steps where both granularity (DA=1) and category match the ground truth. The 51.0% baseline is the performance of a standard LLM decomposer without SAD's retrieval-augmented iteration. Error bars appear in the main results (Table 2), and the Wilcoxon test was applied to paired per-query differences. We will add a concise definition and baseline description to the revised abstract. revision: yes

  2. Referee: [Abstract] Abstract (DA-conditioned analysis): The statement that correct granularity (DA=1) is a prerequisite for effective retrieval rests on a correlational observation (CatR@1 rising from 34% to 41%) without controls for confounders such as query phrasing or skill-description overlap, nor any causal isolation experiment.

    Authors: We acknowledge the analysis is correlational and does not include explicit controls or causal experiments. The conditioned results demonstrate a clear association between DA=1 and improved CatR@1. We will revise the abstract to use more cautious language such as 'empirical evidence indicates' and add a limitations discussion in the main text noting potential confounders like query phrasing. A dedicated causal isolation study is beyond the current scope but noted as future work. revision: partial

  3. Referee: [Abstract] Abstract (CompSkillBench): No information is supplied on sampling method for the 300 queries, how ground-truth decompositions were created, or inter-annotator agreement; these omissions make it impossible to assess whether the benchmark supports the representativeness assumption underlying all transfer and generalization claims.

    Authors: We agree these details are necessary. The queries were sampled for compositional diversity across the 24 categories from the public MCP ecosystem; ground-truth decompositions were produced by experts with MCP skill knowledge; and inter-annotator agreement was assessed via Cohen's kappa. We will add a brief summary of the benchmark construction process to the revised abstract. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on external benchmark measurements

full rationale

The paper's central claims (SAD accuracy lift 51.0%→67.7%, SkillWeaver context reduction and generalization gains) are empirical measurements on the introduced CompSkillBench (300 queries, 2209 skills) with reported Wilcoxon p-values. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described framework. The derivation chain consists of standard decompose-retrieve-compose steps evaluated against an external benchmark rather than reducing to inputs by construction. This matches the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The framework relies on standard LLM capabilities and existing retrieval libraries (FAISS) without introducing new postulated entities.

pith-pipeline@v0.9.1-grok · 5809 in / 1174 out tokens · 41244 ms · 2026-06-27T00:34:22.766751+00:00 · methodology

discussion (0)

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Reference graph

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