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arxiv: 2604.02927 · v2 · submitted 2026-04-03 · 💻 cs.LG · cs.NI

Recognition: 1 theorem link

· Lean Theorem

Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

Andreas Boltres , Niklas Freymuth , Benjamin Schichtholz , Michael K\"onig , Gerhard Neumann
Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:35 UTC · model grok-4.3

classification 💻 cs.LG cs.NI
keywords routing algorithmsgraph neural networkstelemetry-aware routingreinforcement learningdelay-aware controlnetwork optimizationneural routing
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The pith

A graph neural network called LOGGIA outperforms shortest-path routing when communication and inference delays are modeled realistically.

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

The paper frames telemetry-aware routing as a closed-loop control problem that must account for delays in gathering network state and computing decisions. It introduces a training and evaluation framework that incorporates these delays explicitly. On top of this, it develops LOGGIA, which uses graph neural networks to predict log-space link weights from local topology and telemetry data, pre-trained then refined with reinforcement learning. Experiments on synthetic and real topologies with mixed TCP/UDP traffic show LOGGIA beating baselines, while other neural methods break down under delays, and local deployment works best.

Core claim

By explicitly modeling communication and inference delays in a delay-aware closed-loop control setup, the LOGGIA algorithm, which predicts log-space link weights via a graph neural network on attributed topology-and-telemetry graphs after data-driven pre-training and on-policy reinforcement learning, achieves consistent outperformance over shortest-path baselines across various network topologies and unseen traffic sequences, whereas prior neural routing approaches fail when realistic delays are enforced.

What carries the argument

LOGGIA, a graph neural network that predicts log-space link weights from attributed topology-and-telemetry graphs, trained via pre-training followed by on-policy reinforcement learning in a delay-modeled framework.

If this is right

  • Neural routing algorithms perform best when deployed fully locally at each router rather than centralized.
  • Telemetry-aware neural routers can react to traffic bursts within milliseconds when delays are accounted for in training.
  • LOGGIA generalizes to unseen mixed TCP/UDP traffic sequences on both synthetic and real topologies.
  • Explicit delay modeling in the training framework is necessary for neural methods to remain effective in realistic settings.

Where Pith is reading between the lines

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

  • If the local deployment advantage holds, production networks could run independent neural routers at each node without central coordination overhead.
  • Testing LOGGIA on larger-scale or dynamic topologies would verify scalability beyond the evaluated cases.
  • Integrating this with other network control problems like congestion control could create end-to-end neural network management systems.

Load-bearing premise

The modeled communication and inference delays accurately represent conditions in actual production networks.

What would settle it

Deploying LOGGIA in a real-world production network and measuring whether it still outperforms shortest-path routing under actual observed delays and traffic.

Figures

Figures reproduced from arXiv: 2604.02927 by Andreas Boltres, Benjamin Schichtholz, Gerhard Neumann, Michael K\"onig, Niklas Freymuth.

Figure 1
Figure 1. Figure 1: We view telemetry-aware near-real time routing as a closed-loop control problem, illustrated here [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Observation graph assembly for the mini5 topology, shown in a) with link delays in ms. b): The "birds-eye" view of network state St contains all current node and edge states xt. c): We designate node 1 as the central node vc, having the lowest maximum delay of 8 ms to all other nodes. Colored edges show the minimum-delay spanning tree used for communicating state, observation, and action information. d): O… view at source ↗
Figure 3
Figure 3. Figure 3: The different possible deployment options within our framework. Each interaction step requires [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Network topologies used in our experiments. From left to right: [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance of baseline algorithms and our algorithm [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance of LOGGIA trained with PPO/MAPPO in delay-aware/delay-oblivious configuration, and evaluated in different deployment modes on the B4 topology. Solid lines show the inter-quartile mean across 8 random seeds per approach. Dashed lines denote the best SP baseline per evaluation. All runs include IL pretraining and are evaluated for varying inference delay scalings λac ∈ [0, 1] (x-axis per plot). I… view at source ↗
Figure 7
Figure 7. Figure 7: Performance and generalization capabilities of [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Our implementation of node and edge snapshots. [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Our implementation of observed node, edge and global attribute vectors. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Illustration of local reward assignment for two packets sent from node [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Each of the architectural choices matter in [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Our policy training improvements help LOGGIA attain higher routing performance, while their effects on M-Slim are inconclusive. Adding principled value learning mechanisms does not improve LOGGIA’s performance, indicating that value learning is not the main performance bottleneck in the evaluated scenarios. C.2 Policy and Value Function Training Policy Updates. We ablate the following two policy training … view at source ↗
Figure 13
Figure 13. Figure 13: Ablation studies on LOGGIA-path show sharply degrading routing performance, including the variants with added random paths or trust-region regularization. This indicates that LOGGIA’s edge-level exploration may be sufficient for our routing problem despite exploration happening on a purely local basis. connectedness). For each node pair, the normalized path costs c˜i = ci − minj∈[0,K−1] cj then form logit… view at source ↗
Figure 14
Figure 14. Figure 14: Performance of LOGGIA for varying topology presets and training algorithm combinations, evaluated in Local-Multi deployment. Dashed lines denote the best SP baseline per evaluated topology preset. IL is inferior to PPO as a standalone trainer, but improves subsequent PPO training consistently. While BC outperforms DAgger-style IL as a standalone trainer, it is inferior to IL as a pretraining phase. C.5 Mu… view at source ↗
Figure 15
Figure 15. Figure 15: Performance of LOGGIA for various topology presets, multi-agent PPO algorithms, central vs. local training observers (the latter marked by (L)), and reward settings. All approaches are trained in the delay-aware setting with IL pretraining and evaluated in Local-Multi deployment. Dashed lines denote the best SP baseline per evaluation. All multi-agent algorithms show similar performance, both with central… view at source ↗
Figure 16
Figure 16. Figure 16: Performance of LOGGIA trained and evaluated in single-/multi-agent mode on the B4 topol￾ogy, with network setting variations as described in the diagram headers (τ = 5 ms corresponds to the default setting). All approaches are trained in the delay-aware setting with IL pretraining and evaluated in Local-Multi deployment. Dashed lines denote the best SP baseline per evaluation. We find that LOGGIA’s perfor… view at source ↗
Figure 17
Figure 17. Figure 17: Performance of LOGGIA trained with PPO/MAPPO in delay-aware/delay-oblivious configura￾tion, and evaluated in different deployment modes on the GEANT topology. Solid lines show the inter￾quartile mean across 8 random seeds per approach. Dashed lines denote the best SP baseline per evaluation. All runs include IL pretraining and are evaluated for varying inference delay scalings λac ∈ [0, 1] (x-axis per plo… view at source ↗
Figure 18
Figure 18. Figure 18: Visualizations of the mini5, B4 and an example nx-XS topology with link datarates and delays [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Additional visualizations of two topologies of the [PITH_FULL_IMAGE:figures/full_fig_p032_19.png] view at source ↗
read the original abstract

Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data can provide informative signals to routing algorithms, and recent work has trained neural networks to exploit such signals for traffic-aware routing. Yet, aggregating network-wide information is subject to communication delays, and existing neural approaches either assume unrealistic delay-free global states, or restrict routers to purely local telemetry. This leaves their deployability in real-world environments unclear. We cast telemetry-aware routing as a delay-aware closed-loop control problem and introduce a framework that trains and evaluates neural routing algorithms, while explicitly modeling communication and inference delays. On top of this framework, we propose LOGGIA, a scalable graph neural routing algorithm that predicts log-space link weights from attributed topology-and-telemetry graphs. It utilizes a data-driven pre-training stage, followed by on-policy Reinforcement Learning. Across synthetic and real network topologies, and unseen mixed TCP/UDP traffic sequences, LOGGIA consistently outperforms shortest-path baselines, whereas neural baselines fail once realistic delays are enforced. Our experiments further suggest that neural routing algorithms like LOGGIA perform best when deployed fully locally, i.e., observing network states and inferring actions at every router individually, as opposed to centralized decision making.

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 / 2 minor

Summary. The paper frames telemetry-aware routing as a delay-aware closed-loop control problem and introduces a framework for training and evaluating neural routing algorithms that explicitly model communication and inference delays. It proposes LOGGIA, a scalable graph neural network that predicts log-space link weights from attributed topology-and-telemetry graphs via data-driven pre-training followed by on-policy reinforcement learning. Experiments on synthetic and real network topologies with unseen mixed TCP/UDP traffic sequences show LOGGIA outperforming shortest-path baselines, while other neural baselines degrade under realistic delays; the work further suggests fully local deployment is preferable to centralized decision-making.

Significance. If the delay modeling and performance rankings hold under real conditions, the framework and LOGGIA could advance deployable neural routing for millisecond-scale traffic adaptation in production networks, addressing a key limitation of prior neural approaches that ignore delays or restrict to local views only.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'neural baselines fail once realistic delays are enforced' while LOGGIA succeeds is load-bearing for the deployability conclusions, yet the abstract (and by extension the evaluation) provides no calibration procedure, no comparison to measured latencies on the same topologies, and no sensitivity analysis on delay parameters.
  2. [Evaluation] Evaluation (assumed §4-5): the reported outperformance uses unseen traffic sequences and standard baselines, but lacks statistical significance tests, confidence intervals, or details on exact experimental setups and potential confounding factors in the simulations, undermining the 'consistently outperforms' assertion.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'log-space link weights' is introduced without a brief definition or pointer to the precise formulation used in the GNN output layer.
  2. [Framework] The manuscript would benefit from an explicit statement of the communication/inference delay model equations in the framework section to allow reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their valuable comments on our manuscript. We address each of the major points raised below, providing clarifications and indicating revisions where necessary to improve the presentation of our delay modeling and experimental results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'neural baselines fail once realistic delays are enforced' while LOGGIA succeeds is load-bearing for the deployability conclusions, yet the abstract (and by extension the evaluation) provides no calibration procedure, no comparison to measured latencies on the same topologies, and no sensitivity analysis on delay parameters.

    Authors: We agree that the abstract should better contextualize the delay modeling to support the central claim. In the revised version, we will update the abstract to explicitly mention that delays are modeled based on realistic network parameters drawn from established literature on communication latencies. Furthermore, we will include a new subsection in the evaluation detailing a sensitivity analysis on the delay parameters (communication and inference delays), showing that LOGGIA maintains its performance advantage across a range of realistic delay values. Regarding calibration and direct comparison to measured latencies on the same topologies, our work is simulation-based and uses representative delay values; obtaining and matching exact real-world latency measurements for the specific synthetic and real topologies would require proprietary data not available to us. We believe the current modeling is sufficient for the framework's purpose, but we will clarify this limitation in the discussion. revision: partial

  2. Referee: [Evaluation] Evaluation (assumed §4-5): the reported outperformance uses unseen traffic sequences and standard baselines, but lacks statistical significance tests, confidence intervals, or details on exact experimental setups and potential confounding factors in the simulations, undermining the 'consistently outperforms' assertion.

    Authors: We appreciate this feedback on strengthening the statistical rigor of our evaluation. We will revise the evaluation section to include: (1) details on the exact experimental setups, including the number of independent runs (e.g., 10 seeds), simulation parameters, and traffic generation procedures; (2) statistical significance tests such as paired t-tests with p-values reported for comparisons between LOGGIA and baselines; and (3) 95% confidence intervals for key metrics like average delay and throughput. We will also discuss potential confounding factors, such as variations in traffic mix and topology scale, and how they were controlled. These additions will substantiate the claim of consistent outperformance with quantitative evidence. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation or claims

full rationale

The paper frames telemetry-aware routing as a delay-aware control problem, introduces an explicit framework for modeling communication and inference delays, and evaluates LOGGIA (a GNN predicting log-space weights via pre-training plus on-policy RL) against shortest-path and other neural baselines on synthetic/real topologies with held-out mixed TCP/UDP sequences. No load-bearing step reduces by construction to its own inputs: performance rankings are reported as empirical outcomes of the delay model and RL training rather than tautological re-statements of fitted parameters; no self-citation chain is invoked to justify uniqueness or ansatz choices; and the central claim (local deployment outperforming centralized under realistic delays) rests on direct experimental comparison rather than renaming or self-definition. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions in machine learning for control and network simulation fidelity.

free parameters (1)
  • neural network hyperparameters
    Standard parameters such as learning rates and architecture sizes are likely fitted or chosen during training, though not specified.
axioms (1)
  • domain assumption Network simulations with synthetic and real topologies accurately model real-world routing scenarios
    Invoked when claiming generalization from experiments to deployability.

pith-pipeline@v0.9.0 · 5543 in / 1238 out tokens · 51094 ms · 2026-05-13T20:35:00.802713+00:00 · methodology

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Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages · 1 internal anchor

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    to compute up toK top shortest paths per node pair (at least one due to our assumption of graph 26 150 200 Mean delivered (MB) mini5 150 200 250 300 B4 200 300 400 nx-XS LOGGIA LOGGIA-path +Krand +Krand +TRPL SPEIGRP SPRIP Figure 13: Ablation studies onLOGGIA-pathshow sharply degrading routing performance, including the variants with added random paths or...

    work page 2020