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arxiv: 2512.01099 · v2 · submitted 2025-11-30 · 💻 cs.AI

Cost-Aware Model Orchestration for LLM-based Systems

Daria Smirnova , Hamid Nasiri , Marta Adamska , Zhengxin Yu , Peter Garraghan This is my paper

Pith reviewed 2026-05-17 02:18 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM orchestrationcost-aware selectionquantitative performance characteristicsenergy efficiencytask accuracymodel selection latencyAI systems
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The pith

LLM orchestrators reach better accuracy and efficiency by folding quantitative performance numbers into their model choices.

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

The paper claims that existing LLM-based orchestrators rely on qualitative descriptions of available models, which often fail to match actual capabilities, costs, or energy use and therefore produce suboptimal selections. The proposed cost-aware method corrects this by feeding quantitative performance characteristics directly into the decision process so the orchestrator can weigh accuracy against resource trade-offs. A sympathetic reader would care because modern AI systems increasingly combine many models and tools, and poor choices waste compute while lowering output quality. Experiments on multiple tasks show the change raises accuracy, improves energy efficiency, and speeds up selection. The work therefore points toward orchestration that treats measurable traits as first-class inputs rather than afterthoughts.

Core claim

The paper proposes a cost-aware model selection method that accounts for performance-cost trade-offs by incorporating quantitative model performance characteristics within decision-making. Initial experimental results demonstrate that this method increases accuracy by 0.90%-11.92% across various evaluated tasks, achieves up to a 54% energy efficiency improvement, and reduces orchestrator model selection latency from 4.51 s to 7.2 ms.

What carries the argument

Cost-aware model selection method that incorporates quantitative performance characteristics into the LLM decision-making process.

If this is right

  • Accuracy on the evaluated tasks rises between 0.90% and 11.92%.
  • Energy efficiency improves by as much as 54%.
  • Model selection latency drops from 4.51 seconds to 7.2 milliseconds.
  • Selections more closely reflect true model capabilities, lowering the rate of suboptimal choices.

Where Pith is reading between the lines

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

  • The same quantitative integration could let orchestrators handle growing catalogs of models without decision time growing proportionally.
  • Periodic refresh of the measured characteristics would keep gains intact as models are updated or replaced.
  • Hybrid systems could combine the LLM's reasoning with direct metric lookup tables to further reduce reliance on descriptive text alone.

Load-bearing premise

Quantitative performance characteristics of models are readily available, accurate, and can be incorporated into LLM decision-making without introducing new selection biases or significant additional overhead.

What would settle it

A controlled test that runs identical tasks with and without the quantitative characteristics and finds no measurable gain in accuracy or efficiency, or that finds the added data collection increases overall latency.

Figures

Figures reproduced from arXiv: 2512.01099 by Daria Smirnova, Hamid Nasiri, Marta Adamska, Peter Garraghan, Zhengxin Yu.

Figure 1
Figure 1. Figure 1: Overview of an LLM-orchestrated AI system. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the energy-aware LLM-orchestrator model selection framework. The Energy Budget Tracker (right) estimates the current per-slot energy [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy per Joule, calculated on weighted energy and accuracy [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Model selection performance on the VQA dataset. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Per-slot energy usage. The dotted red line indicates the user-defined [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

As modern artificial intelligence (AI) systems become more advanced and capable, they can leverage a wide range of tools and models to perform complex tasks. The task of orchestrating these models is increasingly performed by Large Language Models (LLMs) that rely on qualitative descriptions of models for decision-making. However, the descriptions provided to existing LLM-based orchestrators frequently do not reflect true model capabilities and performance characteristics, leading to suboptimal model selection, reduced task accuracy, and increased cost. In this paper, we conduct an empirical analysis of LLM-based orchestration limitations and propose a cost-aware model selection method that accounts for performance-cost trade-offs by incorporating quantitative model performance characteristics within decision-making. Initial experimental results demonstrate that our proposed method increases accuracy by 0.90%-11.92% across various evaluated tasks, achieves up to a 54% energy efficiency improvement, and reduces orchestrator model selection latency from 4.51 s to 7.2 ms.

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

1 major / 2 minor

Summary. The manuscript analyzes limitations of LLM-based model orchestration that relies on qualitative model descriptions, which can lead to suboptimal selections. It proposes a cost-aware orchestration method that incorporates quantitative performance metrics (accuracy, energy, latency) into LLM decision-making for model selection. Initial experiments claim accuracy gains of 0.90%-11.92% across tasks, up to 54% energy efficiency improvement, and reduction of selection latency from 4.51 s to 7.2 ms.

Significance. If the net gains hold after including costs of acquiring the quantitative characteristics, the approach could meaningfully improve efficiency and accuracy in multi-model LLM systems by enabling explicit performance-cost trade-offs. The work identifies a practical gap in current qualitative-only orchestration and offers an empirical demonstration of potential benefits.

major comments (1)
  1. §5 (Experimental Evaluation): The reported accuracy, energy (up to 54%), and latency (4.51 s to 7.2 ms) improvements treat quantitative performance characteristics as available inputs without measuring or subtracting the overhead of obtaining them (via offline profiling or online estimation). This is load-bearing for the central claim, as unaccounted benchmarking costs could negate the net efficiency and latency gains, especially for new tasks where profiling does not transfer.
minor comments (2)
  1. Abstract: Provide at least high-level information on the number of tasks, models evaluated, and comparison baselines to allow readers to contextualize the 0.90%-11.92% accuracy range.
  2. §3 (Method): Clarify the exact integration mechanism (e.g., how quantitative metrics are encoded in the LLM prompt or used in a separate selector) with pseudocode or a diagram for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below and will revise the paper to strengthen the experimental evaluation.

read point-by-point responses

  1. Referee: §5 (Experimental Evaluation): The reported accuracy, energy (up to 54%), and latency (4.51 s to 7.2 ms) improvements treat quantitative performance characteristics as available inputs without measuring or subtracting the overhead of obtaining them (via offline profiling or online estimation). This is load-bearing for the central claim, as unaccounted benchmarking costs could negate the net efficiency and latency gains, especially for new tasks where profiling does not transfer.

    Authors: We agree this is a valid concern and a limitation in the current presentation of results. The reported gains focus on the online orchestration and execution phase, under the assumption that quantitative metrics (accuracy, energy, latency) have been pre-computed via offline profiling, which is a realistic setup for production multi-model systems where profiling occurs once and is reused. However, we acknowledge that the one-time cost of profiling could reduce net benefits for infrequent or entirely new tasks where metrics do not transfer. In the revised manuscript we will add a dedicated analysis in §5 that (1) reports the measured time and energy overhead of our offline profiling procedure for the evaluated models, (2) discusses amortization over repeated task invocations, and (3) identifies conditions under which the net gains remain positive. This will make the efficiency claims more precise without altering the core experimental findings. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method relies on external measurements with no internal derivation chain

full rationale

The paper conducts an empirical analysis of LLM orchestration limitations and proposes a cost-aware selection method that incorporates quantitative performance characteristics (accuracy, energy, latency) as inputs to decision-making. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation load-bearing uniqueness theorems appear in the abstract or described approach. The reported gains (+0.90–11.92% accuracy, up to 54% energy improvement, latency reduction to 7.2 ms) are presented as experimental outcomes using externally obtained characteristics, rendering the work self-contained against external benchmarks rather than reducing any claim to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no explicit free parameters, axioms, or invented entities can be extracted. The approach implicitly assumes that quantitative model metrics exist and are unbiased inputs.

pith-pipeline@v0.9.0 · 5468 in / 1062 out tokens · 60987 ms · 2026-05-17T02:18:47.335956+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    We use Pareto optimization to balance accuracy and average energy usage when selecting models... the Selector performs Pareto-Efficient filtering to obtain a subset of models on the Pareto Frontier of (Acc, Eavg).

  • IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    GUIDE achieves significant accuracy improvements... up to 54% in Accuracy-per-Joule... while operating at 7.2 ms latency per request (vs 4.51 s for LLM-based methods).

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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