OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

Chunming Wu; Dong Zhang; Hongyan Liu; Jiajie Su; Jiashuo Yu; Longlong Zhu; Shaopeng Zhou; Xiang Chen; Xingyuan Li; Xuan Liu

arxiv: 2606.18105 · v2 · pith:GUAWR5B2new · submitted 2026-06-16 · 💻 cs.NI · cs.LG

OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

Longlong Zhu , Jiashuo Yu , Zedi Chen , Yuhan Wu , Zhifan Jiang , Yuchen Xian , Yimeng Liu , Jiajie Su

show 7 more authors

Shaopeng Zhou Xingyuan Li Hongyan Liu Xuan Liu Dong Zhang Chunming Wu Xiang Chen

This is my paper

Pith reviewed 2026-06-26 21:56 UTC · model grok-4.3

classification 💻 cs.NI cs.LG

keywords network planning optimizationlarge language modelsmixture of expertsmachine learning offloadingmixed integer programmingdeep reinforcement learningadaptive optimizationuser intent interpretation

0 comments

The pith

OmniPlan translates natural-language user goals into a preference vector that selects and tunes a mix of solvers for fast near-optimal network plans.

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

Network planning optimization must balance competing objectives under constraints while adapting to shifting user preferences expressed in natural language. Prior approaches using MIP solvers, heuristics, or DRL models force a choice between execution speed and solution quality when intents change. OmniPlan addresses this by first using an LLM to turn heterogeneous intents into a single quantifiable preference vector. It then routes each planning task to a suitable expert from a mixture of MIP solvers, heuristics, and DRL models, and applies a separate DRL module to adjust objective weights. Evaluation on offloading diverse ML inference tasks across real hardware shows the framework produces decisions that are both low-latency and near-optimal.

Core claim

OmniPlan achieves near-optimal and low-execution-time offloading for real-world ML inference tasks by converting natural-language intents into a unified preference vector via an LLM interpreter, dynamically selecting among MIP solvers, heuristics, and DRL experts, and fine-tuning weights with a DRL configuration module, yielding latency reductions of up to 97.8 percent and network device resource reductions of up to 11.5 percent.

What carries the argument

Mixture-of-experts architecture that dynamically selects and configures MIP solvers, heuristics, and DRL models according to an LLM-derived user-preference vector.

If this is right

Planning decisions for distributed ML inference can be generated in low time while staying close to optimal across decision trees, SVMs, naive Bayes, XGBoost, and random forests.
The same framework can be applied to any network planning task whose objectives can be expressed as weighted combinations of latency and resource metrics.
User preferences stated in ordinary language become directly usable inputs without requiring manual translation into solver parameters.
Dynamic intent changes no longer require restarting the entire optimization pipeline.

Where Pith is reading between the lines

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

The approach could be tested on transportation or power-grid planning problems that also involve competing objectives and natural-language stakeholder goals.
If the preference vector generalizes across domains, the framework might reduce the engineering effort needed to adapt optimization tools to new application areas.
Real-time scenarios with rapidly changing intents become feasible once the LLM interpreter and expert selector run with low overhead.

Load-bearing premise

The LLM interpreter converts natural-language intents into a preference vector that accurately guides expert choice and weight tuning without introducing errors that degrade planning quality.

What would settle it

A test case in which the LLM misreads a user intent, selects a mismatched expert, and produces a plan whose latency or resource use exceeds that of a fixed baseline solver.

Figures

Figures reproduced from arXiv: 2606.18105 by Chunming Wu, Dong Zhang, Hongyan Liu, Jiajie Su, Jiashuo Yu, Longlong Zhu, Shaopeng Zhou, Xiang Chen, Xingyuan Li, Xuan Liu, Yimeng Liu, Yuchen Xian, Yuhan Wu, Zedi Chen, Zhifan Jiang.

**Figure 1.** Figure 1: The architecture of OmniPlan. 4 Intent Translation Challenges. The network planning task needs to handle diverse optimization objectives arising from heterogeneous task requirements, such as minimizing latency for time-sensitive tasks and maximizing throughput for data-intensive tasks. Here, user intents may contain multiple objectives. For example, the intent of “offloading more tasks in a green data cen… view at source ↗

**Figure 2.** Figure 2: (Exp#12) Number of successfully offloaded tasks [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

read the original abstract

Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids. It requires simultaneous optimization of multiple competing objectives under complex constraints. Existing network planning optimization frameworks rely on mixed integer programming (MIP) solvers, heuristics, and deep reinforcement learning (DRL) models to compute planning decisions. However, they lack effective adaptability to diverse and dynamic user intents, thus leading to the trade-off between execution time and optimality. In this paper, we propose OmniPlan, an adaptive framework that achieves both timeliness and near-optimality in network planning optimization. To achieve the adaptability lacking in existing solutions, OmniPlan employs a large language model (LLM)-based interpreter to convert heterogeneous natural-language intents into a unified and quantifiable user-preference vector. Then it employs a mixture-of-experts architecture that integrates MIP solvers, heuristics, and DRL models as specialized experts, where OmniPlan adapts to diverse intents by dynamically selecting timely and near-optimal experts. Finally, it incorporates a DRL-based expert configuration module that fine-tunes optimization objective weights to align planning decisions with user-specific preferences. We evaluate OmniPlan with a representative real-world workload, i.e., distributed machine learning (ML), where we leverage OmniPlan to offload a wide spectrum of ML inference tasks, e.g., decision trees, SVM, naive Bayes, XGBoost, and random forests, onto a network of hardware devices. Our experiments on a real-world testbed indicate that OmniPlan achieves near-optimal and low-execution-time offloading for real-world ML inference tasks, reducing latency by up to 97.8\% and network device resource consumption by up to 11.5\%.

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

OmniPlan integrates an LLM interpreter with a mixture-of-experts setup for intent-driven network planning, but the abstract gives no validation that the interpreter produces accurate preference vectors.

read the letter

The main takeaway is that OmniPlan tries to make network planning respond to natural-language intents by routing through MIP solvers, heuristics, and DRL experts, then tuning weights with another DRL module. The reported gains come from a testbed run on ML inference offloading.

The new piece is the end-to-end pipeline that starts from heterogeneous user text and ends with dynamic expert choice plus weight adjustment. The evaluation uses a real testbed and concrete workloads (decision trees, SVM, XGBoost, random forests), which is more grounded than pure simulation work.

The experiments claim up to 97.8% latency drop and 11.5% resource savings. Those numbers are specific enough to be checked once the full tables and setup details are available.

The clear gap is the LLM interpreter. The abstract describes turning intents into a preference vector but supplies no accuracy metrics, error rates across intent types, or checks against ground-truth optimality. If that mapping is noisy or biased, the expert selection and weight tuning rest on shaky inputs, and the performance numbers would not hold up outside the tested cases.

The DRL configuration module could also be fitting to the same data used for reporting gains, but that cannot be judged from the abstract alone.

This paper is for researchers who build adaptive optimizers for communication networks or similar constrained planning problems. Someone already working on MoE or LLM-augmented solvers might pick up the architecture as a concrete example.

I would send it to peer review. The testbed evaluation and the multi-expert design are concrete enough to deserve referee time, even though the LLM validation step needs to be added or strengthened.

Referee Report

2 major / 2 minor

Summary. The paper proposes OmniPlan, an adaptive framework for network planning optimization. It converts heterogeneous natural-language user intents into a unified preference vector via an LLM-based interpreter, then uses a mixture-of-experts architecture (MIP solvers, heuristics, DRL models) dynamically selected for timeliness and near-optimality, plus a DRL expert-configuration module to tune objective weights. Evaluated on distributed ML inference offloading (decision trees, SVM, naive Bayes, XGBoost, random forests) to hardware devices on a real-world testbed, it claims near-optimal low-execution-time decisions with up to 97.8% latency reduction and 11.5% resource savings.

Significance. If the central claims hold, the work could meaningfully advance adaptive multi-objective network optimization by linking natural-language intents to quantitative planning via LLM interpretation and expert selection. The integration of MIP, heuristics, and DRL with dynamic weighting addresses a recognized gap in existing solvers. The real-world ML workload evaluation on a testbed adds practical value, though the absence of supporting data or validation metrics limits assessment of the reported gains.

major comments (2)

[Abstract] Abstract: the performance claims (97.8% latency reduction, 11.5% resource savings) are stated without any equations, data tables, error bars, derivation steps, or baseline comparisons, preventing verification that the gains arise from the proposed LLM interpreter, expert selection, and DRL tuning rather than experimental artifacts or post-hoc selection.
[Evaluation] Evaluation section: no quantitative metrics (e.g., vector error rates, intent-to-preference correlation, or optimality-gap analysis across intent types) are supplied for the LLM interpreter's fidelity in mapping natural-language intents to the preference vector; this mapping is load-bearing for the adaptability claim and the reported gains, as biased or erroneous vectors would misalign expert choice and weight tuning.

minor comments (2)

The abstract lists ML models (decision trees, SVM, naive Bayes, XGBoost, random forests) but provides no details on how these tasks were mapped to network offloading decisions or the specific constraints used.
No discussion of potential LLM hallucinations or bias in the interpreter, nor any fallback mechanism if the preference vector is unreliable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and evaluation. We address each major comment below with proposed revisions where the manuscript can be strengthened without misrepresenting the work.

read point-by-point responses

Referee: [Abstract] Abstract: the performance claims (97.8% latency reduction, 11.5% resource savings) are stated without any equations, data tables, error bars, derivation steps, or baseline comparisons, preventing verification that the gains arise from the proposed LLM interpreter, expert selection, and DRL tuning rather than experimental artifacts or post-hoc selection.

Authors: Abstracts are space-constrained and conventionally omit equations, tables, and full derivations; the manuscript supplies these details in the Evaluation section via baseline comparisons (standalone MIP, heuristics, DRL) and reported figures/tables. We will partially revise the abstract to name the primary baselines and add a sentence directing readers to the evaluation results for verification of the gains. revision: partial
Referee: [Evaluation] Evaluation section: no quantitative metrics (e.g., vector error rates, intent-to-preference correlation, or optimality-gap analysis across intent types) are supplied for the LLM interpreter's fidelity in mapping natural-language intents to the preference vector; this mapping is load-bearing for the adaptability claim and the reported gains, as biased or erroneous vectors would misalign expert choice and weight tuning.

Authors: The current evaluation validates the interpreter indirectly via end-to-end ML offloading performance. We agree that direct metrics would better support the adaptability claim. We will revise the Evaluation section to add quantitative analysis, including intent-to-preference correlation and error rates on a held-out intent set, to quantify mapping fidelity. revision: yes

Circularity Check

0 steps flagged

No circularity detected; framework claims rest on described components without self-referential reductions.

full rationale

The provided abstract describes OmniPlan's components (LLM interpreter, mixture-of-experts, DRL configuration) and reports empirical results on a testbed (latency and resource reductions), but contains no equations, fitting procedures, or self-citations that reduce any claimed prediction or optimality to its own inputs by construction. No derivation chain is exhibited that would allow identification of self-definitional, fitted-input, or self-citation load-bearing steps. The central claims are presented as outcomes of evaluation rather than mathematical derivations forced by prior steps within the paper. Without internal equations or load-bearing citations in the given text, the derivation is self-contained against external 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 user-preference vector and expert-selection policy are mentioned but not formalized.

pith-pipeline@v0.9.1-grok · 5893 in / 1231 out tokens · 22877 ms · 2026-06-26T21:56:03.191521+00:00 · methodology

discussion (0)

Reference graph

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