Title: Hephaestus: Mixture Generative Modeling with Energy Guidance for Large-scale QoS Degradation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Combinatorial Optimization

Reinforcement learning

Generative AI
Abstract: We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, where communication quality, not just connectivity, determines functionality. While classical methods rely on combinatorial optimization, and recent ML approaches address only restricted linear variants with small-size networks, no prior model directly tackles the QoSD problem under nonlinear edge-weight functions. This work proposes Hephaestus, a self-reinforcing generative framework that synthesizes feasible solutions in latent space, to fill this gap. Our method includes three phases: (1) Forge: a Predictive Path-Stressing (PPS) algorithm that uses graph learning and approximation to produce feasible solutions with performance guarantee, (2) Morph: a new theoretically grounded training paradigm for Mixture of Conditional VAEs guided by an energy-based model to capture solution feature distributions, and (3) Refine: a reinforcement learning agent that explores this space to generate progressively near-optimal solutions using our designed differentiable reward function. Experiments on both synthetic and real-world networks show that our approach consistently outperforms classical and ML baselines, particularly in scenarios with nonlinear cost functions where traditional methods fail to generalize.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a self-reinforcing generative framework called Hephaestus to address the QoSD problem under both linear and nonlinear edge-weight functions.  Specifically, the proposed method uses graph learning to produce feasible solutions with a performance guarantee, then trains a Mixture of Conditional VAEs guided by an energy-based model to capture solution feature distributions, and finally uses a reinforcement learning agent in the latent space to fine-tune the solutions. Experiments show the superior performance of the proposed method, especially in nonlinear settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed Hephaestus framework is a novel framework to address QoSD, filling the gap that previous methods can't tackle nonlinear edge-weight functions.\n2. Experiments show the superior performance of Hephaestus in nonlinear settings and various datasets.\n3. Each step in this framework has a theoretical guarantee.\n\nWeaknesses:\nIt seems that using a Mixture of Conditional VAEs guided by an energy-based model is not reasonable. For VAEs, it minimizes KL(p|q) for q distribution, which is known as mode coverage, and is always applied to alleviate mode collapse[1]. Conversely, for energy-based models, the minimax training will cause the VAEs to optimize KL(q|p) w.r.t. q, which can lead to mode collapse[2]. Therefore, the motivation for designing a Mixture of Conditional VAEs guided by an energy-based model is not reasonable. At least, it seems this design can't alleviate mode collapse with the paper's explanation.\n\n[1] Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inferential Model. CVPR 2019\n\n[2] Exploring bidirectional bounds for minimax training of Energy-based models. IJCV 2025\n\nQUESTIONS:\nI suggest the authors provide a more reasonable explanation of why using the Mixture of Conditional VAEs guided by an energy-based model can alleviate mode collapse.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the Quality-of-Service Degradation (QoSD) problem, where an adversary perturbs edge weights in a network to degrade end-to-end performance while adhering to budget constraints. It introduces Hephaestus, a novel three-phase generative framework comprising: (1) Forge, which uses a Predictive Path-Stressing (PPS) algorithm guided by a shortest-path attention model (SPAGAN) to generate feasible perturbations; (2) Morph, an energy-based, expert-augmented generative model (Mix-CVAE) designed to capture multimodal solution distributions; and (3) Refine, a reinforcement learning agent that explores the latent space to produce optimized solutions using a smooth, differentiable reward.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1. The paper offers a well-integrated approach to the QoSD problem by combining graph learning, generative modeling, and reinforcement learning, and demonstrates strong empirical performance on large-scale real-world networks.\n2. The paper provides solid theoretical guarantees (e.g., for PPS, latent RL convergence, and normalization-free energy modeling).\n3. Its ability to handle nonlinear edge cost functions (e.g., quadratic and log-concave) distinguishes it from prior work.\n4. The modular structure of Hephaestus (Forge-Morph-Refine) is clearly presented with detailed ablations.\n\n**Weaknesses:**\n\n1. The framework relies on SPAGAN's accuracy—robustness to prediction errors in large, noisy graphs is not extensively discussed.\n2. The expert expansion mechanism in Mix-CVAE adds computational overhead; resource usage and efficiency trade-offs are not evaluated.\n3. The reinforcement learning component (Refine) is theoretically elegant, but lacks empirical discussion on convergence behavior and sample efficiency.\n4. The paper does not compare with recent powerful generative optimization methods (e.g., diffusion-based solvers or large-scale neural MIP solvers).\n\nQUESTIONS:\n1. How robust is the PPS algorithm when SPAGAN is trained on distributions that differ from the test graph topology?\n\n2. Has the RL refinement phase been compared with alternative latent optimization strategies like Bayesian optimization or ES?\n\n3. How is the number of CVAE experts in Morph determined? Is there a risk of redundancy or overfitting?\n\nLIMITATIONS:\nYES", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nHephaestus is a generative framework for Quality of Service Degradation that aims to identify minimal, stealthy perturbations to network edge weights to degrade system performance. It combines predictive path approximation, energy-guided mixture generative modeling, and latent-space reinforcement learning to efficiently generate high-quality solutions under nonlinear cost functions and large-scale constraints.\n\nSTRENGTHS AND WEAKNESSES:\nstrengths:\n1.Unlike prior methods that treat feasibility search, modeling, and optimization as separate tasks, Hephaestus introduces a unified, generative pipeline that handles them jointly. This end-to-end design addresses the inefficiency and fragmentation of previous methods, especially under large-scale nonlinear constraints where classical ILP or heuristic-based solvers fail to generalize.\n2.Earlier approximation algorithms like Adaptive Trading or Iterative Greedy handle each source-target pair independently, leading to budget inefficiencies. Hephaestus’s PPS algorithm uses shortest-path estimation and a global potential function to coordinate updates across all pairs, providing better resource allocation with theoretical approximation guarantees.\n\nweakness:\n1.The Forge phase depends on a well-trained SPAGAN model to estimate shortest paths, but the paper lacks a thorough sensitivity analysis on how inaccuracies in SPAGAN affect overall performance. If SPAGAN generalizes poorly to unseen graph structures, the entire pipeline could suffer significantly.\n2.Although the RL phase improves solution quality, training policies in latent space still requires iterative rollouts and reward evaluation, which can be computationally intensive. The paper does not provide detailed analysis of RL convergence behavior or resource cost under different problem scales.\n\nQUESTIONS:\n1.Since PPS relies on estimated shortest path lengths from SPAGAN rather than exact values, it’s unclear how errors in this approximation affect the feasibility or optimality of generated solutions. Has the robustness of the pipeline been evaluated under noisy or miscalibrated SPAGAN outputs?\n2.The paper claims generalization to unseen graphs with similar structure, but it is not clear how well the RL-based refinement policy performs when topology or distribution shifts occur. Would retraining be needed for significantly different domains?\n\nLIMITATIONS:\n1.The quality and diversity of the pretrained solution dataset (Dsol) is critical for the Morph phase, yet the paper does not examine the failure modes when Dsol is sparse or biased. If the initial PPS-based solutions are suboptimal, the entire pipeline may converge to poor regions of the solution space.\n2.The current setup assumes static graph topologies and fixed critical pairs. In practical scenarios (e.g., networks with dynamic routing), it's unclear whether Hephaestus can adapt online or if it requires full retraining from scratch.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Hephaestus, a novel generative modelling framework for solving the Quality of Service Degradation (QoSD) problem, which is formulated as perturbing network edge weights minimally yet sufficiently to degrade service quality. The paper proposes a three-stage sequential solution for the problem:\n\n1. Forge, employing a Predictive Path-Stressing (PPS) algorithm to generate feasible initial solutions with theoretical guarantees.\n2. Morph, a generative modelling approach using Mixture of Conditional VAEs (Mix-CVAEs) guided by an Energy-Based Model (EBM) to model multimodal solution distributions using the output from step 1. as input.\n3. Refine, a reinforcement learning method for fine-tuning latent-space solutions to achieve near-optimality using a differentiable reward function.\n\nThe paper provides rigorous experimental evaluations across synthetic and real-world networks, demonstrating Hephaestus's effectiveness over classical optimization and ML-based baselines, especially under nonlinear conditions. The effectiveness of the algorithm and theoretical guarantees are provided for the algorithm stages. Notably, it can handle both linear and non-linear edge cost functions and a large number of path constraints (that prevents the problem being stated as ILP), which past methods could not.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Tackles a highly relevant real-world challenge: QoS degradation via stealthy perturbations in networked systems (e.g., blockchain, traffic control, distributed ML). Although the problem is relevant, it is not broadly relevant but more relevant for the networking community.\n- The paper is well executed and it offers strong technical depth, including theoretical guarantees (i) approximation bounds for PPS i.e. they prove an approximation ratio for the PPS in Forge, ensuring the generated initial solutions are within a bounded factor of optimal – Forge part; (ii) convergence analysis and proofs of Mix-CVAEs with EBM guidance – Morph part; (iii) design of the RL reward and the gradient-based latent refinement (Theorem 4) demonstrate deep technical insight into stabilizing and accelerating training – Refine part.\n- Comprehensive Evaluation and Analysis. The experimental section is extensive. The authors evaluate different graph types and sizes (beyond million nodes size networks) in the main part and in more details in the appendix. They also investigate why Hephaestus performs well: for instance, the discussion notes that the learned model tends to concentrate budget on edges that benefit multiple source–target pairs, whereas baselines waste budget on pair-specific edges.\n- Strong Empirical Performance: Hephaestus shows clear performance gains over a wide array of baselines. On synthetic graphs, it achieved the lowest costs across all tested densities, essentially matching the optimal in linear cases. In more complex cost settings (quadratic, log-concave) where learning-based ILP solvers cannot be applied, it still outperformed the best classical approximations.\n- The paper is generally well-written and flows well, considering the somewhat complex orchestration of the whole proposal.\n\nWeaknesses:\n- High Complexity, Implementation Overhead & feasibility to be deployed in practice: The Hephaestus framework is quite complex orchestration, involving multiple models and training stages (a GNN predictor, a heuristic search, an EBM, several VAE “experts”, and an RL agent). Implementing and tuning this entire pipeline might be challenging. The approach trades off runtime per instance for a heavy upfront training cost on a distribution of graphs. In scenarios where such extensive training is not feasible or when one needs a quick solution for a single new network, this could be a drawback. The practical impact in real deployment scenarios remains partly speculative without a deeper discussion of implementation challenges (e.g., noise, model drift in real environments) – which are not discussed as limitations. Due to the orchestration nature of the approach (3 stages) having a bad outcome of the first (Forge) i.e. having a bad data will likely have influence on the other phases.\n- Clarity. Though generally well written, given the complexity and number of innovations (PPS, Mix-CVAE, EBM, latent RL), the paper’s readability occasionally suffers from density.\n- Focus on Static Scenario: The QoSD problem is treated as a one-shot perturbation planning problem on a static weighted graph. In practice, network conditions and attack strategies might evolve over time. The paper does not address dynamic or adaptive adversarial scenarios (which is beyond its scope, but still a limitation of the current model).\n\nSuitability:\n- The paper is relevant to machine learning and representation community but to a limited extend, there are more suitable venues for this work such as SIGCOMM, ICNP, INFOCOM.\n\nMinor typos:\n- In the supplemental material, the section is named: DETAILS EXPERIMENTS AND ABLATION STUDIES. \nShould it be named: \nDETAILS OF THE EXPERIMENTS AND ABLATION STUDIES or \nDETAILS, EXPERIMENTS AND ABLATION STUDIES\n\nQUESTIONS:\n1. The authors should discuss the analysis of failure modes. It would improve the paper to discuss how errors in the learned models (SPAGAN or the generative model) affect the outcome. A suggestion is to include a brief sensitivity analysis or worst-case check: e.g., verify a few final solutions with an exact shortest-path computation to confirm no constraints are violated. Reporting the predictor’s accuracy on shortest path estimates for test graphs would also reassure that the learned model is reliable. If certain graph topologies or weight patterns cause predictor mistakes, acknowledging that and perhaps integrating a safeguard (such as occasional exact path re-checks for critical pairs) could be beneficial.\n2. The authors should consider simplifying the explanations for critical, complex components (particularly Morph phase and latent RL refinement) to improve the presentation.\n3. Dynamic/Adaptive Scenario as Future Work: While not necessary for this paper’s scope, mentioning possible extensions to handle dynamic attacks or repeated games could strengthen the impact. For example, one could imagine an online version of Hephaestus that updates its strategy as network conditions or defences change.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-07"}