{"id":"085f32e3-a342-4b01-ac8b-d5548131ee51","arxiv_id":"2507.05722","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Simulations show the proposed two-layer drone offloading algorithm beats simpler baselines on task completion rate and system utility.","lead":"This paper designs a two-layer drone-assisted system for vehicle computing tasks, where a high-altitude drone relays traffic and low-altitude drones compute, and a reinforcement learning agent decides how much work to offload and where drones should fly. The authors report simulations showing higher task completion and lower energy use than three comparison schemes.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The claimed performance gains are not yet established: all comparisons rest on single-seed curves against self-ablation baselines, with no code, hyperparameters, or convergence plot.","rationale":"The reader's verdict is CONDITIONAL with moderate confidence, and my stress-test concurs that the paper should not be accepted as-is. The reader's weakest-assumption focus was the idealized simulation environment (perfect global state, simplified physical layer). I agree that is a limitation, but the single most load-bearing concern for the central claim is more fundamental: the experimental section provides no statistical evidence. Figures 3-5 report single-run outcomes with no variance, seed counts, or hyperparameter settings, which makes it impossible to determine whether the reported margins over the baselines are reproducible or merely noise. The convergence-speed claim, highlighted in the abstract and conclusion, is entirely missing from the results section. The baselines are also self-ablation variants of the proposed architecture rather than existing state-of-the-art VEC offloading methods, so even a clean single-run comparison would only show that the components help, not that the full method is competitive with prior art. The concrete test I propose would settle this by requiring multi-seed runs and confidence intervals, and by checking whether the learning-curve advantage actually materializes. If the intervals overlap or the convergence plot shows no advantage, the central performance claim collapses. If they do not, the paper's core empirical assertion is substantiated, and the remaining perfect-information caveat becomes a secondary realism concern rather than a correctness issue. I therefore recommend keeping the reader's CONDITIONAL verdict rather than moving to reject, because the proposed architecture and SAC formulation are internally coherent and the direction of the claimed improvement is plausible, but the evidence is insufficient for unconditional acceptance.","tokens_in":10173,"tokens_out":4181,"duration_ms":54203,"concrete_test":"Request the authors' simulation code or reimplement Section IV, then run all experiments with at least 10 independent random seeds and report mean and 95% confidence intervals for final completion rate, system utility, and episode reward. Concretely, recompute Fig. 4 at 50 vehicles and Fig. 5 at 100 MHz: if the proposed method's confidence interval overlaps the DQN baseline's interval, or if the reward-versus-episode curve does not saturate earlier than DQN, the claimed superiority and convergence-speed advantage are not supported. Additionally, inject 10% Gaussian noise into the observed states in Eq. 24 (positions and residual resources); if completion rate drops by more than a few percent, the 'strong robustness in dynamic vehicular environments' claim is environment-specific.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim in Sections IV and V is that the hierarchical SAC method outperforms NoPriority, FixedUAV, and DQN in task completion rate, system utility, and convergence speed. The only quantitative evidence is Figs. 3-5, which show single learning curves and bar-style comparisons without error bars, seed counts, or tabulated final values. The 'convergence speed' assertion appears in the abstract and conclusion but is never demonstrated in the results: no figure or metric compares learning curves of the proposed method against DQN. Moreover, the three baselines are ablations of the proposed system (removing priority scheduling, disabling trajectory optimization, or discretizing the action space), not established VEC offloading benchmarks, so outperforming them does not establish state-of-the-art performance. Separately, the MDP in Eq. 24 assumes perfect global state information and the communication model in Eqs. 1-8 omits interference and control overhead, but the more immediate blocker is that the empirical evidence is too thin to support the comparative claim even under the idealized model.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes a dual-layer UAV-assisted vehicular edge computing architecture in which a high-altitude UAV (HUAV) relays traffic to RSUs or the BS while low-altitude UAVs (LUAVs) provide edge computation, with partial task offloading from vehicles. It formulates a mixed-integer nonlinear program P0 (Eq. 23a) that maximizes a weighted sum of task completion rate, negative delay, and negative energy consumption, then reformulates the problem as an MDP. A hierarchical SAC-based algorithm is introduced: the SAC agent outputs offloading ratios and LUAV trajectories, while a priority-based scheduler selects nodes and allocates computing resources. Simulation results in Sections IV and V claim that the proposed method outperforms NoPriority, FixedUAV, and DQN-based baselines in task completion rate, system utility, and convergence speed, with robustness to vehicle density and bandwidth variations.","tokens_in":10442,"tokens_out":4346,"duration_ms":51118,"significance":"If validated, the hierarchical decomposition of global continuous actions (offloading ratios and trajectories) plus a local priority scheduler would be a useful tool for managing the large action space in UAV-assisted VEC. The system model is coherent and includes propulsion energy for LUAVs and hovering energy for the HUAV, which is a positive feature. However, the performance claims are not currently established. The empirical evidence consists of single-seed, ablation-style baselines, the reward function duplicates the optimization objective, and the simulation environment assumes perfect global state and interference-free communication. No code, hyperparameters, or convergence plots are provided. The paper would make a modest contribution after a major revision that adds multi-seed experiments, stronger baselines or appropriately tempered claims, and full reproducibility details.","major_comments":[{"comment":"The central comparative claim rests on learning curves and bar-style comparisons without error bars, seed counts, or tabulated final values. The abstract and conclusion claim improved 'convergence speed,' but no figure or table compares the learning curve of the proposed SAC method against DQN-based or other baselines. Please provide multi-seed means with confidence intervals, tabulated steady-state metrics, and an explicit convergence-speed comparison (e.g., episodes to reach a target utility or reward).","section":"Section IV, Figs. 3-5"},{"comment":"NoPriority, FixedUAV, and DQN-based are ablations of the proposed system or a discretized version of its own SAC logic; they are not established VEC offloading benchmarks. Therefore, the results can only show the contribution of each component, not that the method 'outperforms existing approaches' as stated in Section V. Please add comparisons with at least one or two published UAV-assisted or vehicular edge offloading algorithms (e.g., [9]-[13], [18]), or explicitly restrict the claim to outperforming the considered ablations.","section":"Section IV, baseline definitions"},{"comment":"The immediate reward r(t) is identical to the optimization objective P0 in Eq. (23a), with the task completion rate weighted by ω1 = 0.6. Because Rsucc is part of the training signal, the reported task completion-rate advantage partly reflects the chosen reward weights rather than an independent evaluation metric. This circularity is common in RL, but it should be stated explicitly, and the paper should report raw delay and energy components separately as well as the completion rate. In addition, robustness of the conclusions to ω1, ω2, and ω3 should be demonstrated.","section":"Section III-A, Eq. (26) and Section II-E, Eq. (23a)"},{"comment":"The simulation environment assumes perfect global state information (Eq. 24), quasi-static node positions within each time slot, and distance-based LoS/NLoS rates without interference, control overhead, or feedback delay. Consequently, the claim of 'strong robustness and adaptability in dynamic vehicular environments' in the abstract and Section IV is not actually demonstrated. Please include sensitivity analysis to imperfect state information, channel fading/shadowing dynamics, or mobility within a slot, or soften the robustness claim to the specific simulation conditions.","section":"Section II-C and Section III-A, Eq. (24)"},{"comment":"Key algorithmic parameters are missing: the normalization constants β_T and β_E in Eqs. (23a) and (26), the scoring weights α_s and β_s in Eq. (28), the 30% resource-availability threshold, and the SAC hyperparameters (learning rates, network sizes, batch sizes, entropy coefficient schedule). Without these values, the simulation results cannot be reproduced or meaningfully compared with other algorithms. Please provide a complete parameter table.","section":"Section IV and Section III-B, Eqs. (23a), (26), (28)"}],"minor_comments":[{"comment":"There is a typo in the last term of Eq. (16): 'f_i(t)x' should read 'f_i^x(t)' to match the notation used elsewhere.","section":"Section II-D, Eq. (16)"},{"comment":"The sentence 'The simulations are implemented on the PyCharm platform' is imprecise, since PyCharm is an integrated development environment, not a simulation platform. Please rephrase as, for example, 'simulations are implemented in Python 3.8 and PyTorch.'","section":"Section IV, first paragraph"},{"comment":"The caption says 'System utility versus episode' while the text describes both task completion rate and system utility. The figure should be split into two clearly labeled subplots (one for completion rate, one for utility) with axis labels and units.","section":"Fig. 3 caption and text"},{"comment":"The simulation setup is described in prose without a consolidated parameter table. A table listing all default values (β0, α1, α2, Pn, bandwidths, transmit powers, node counts, energy model constants) would greatly improve readability and reproducibility.","section":"Section IV, simulation setup"},{"comment":"The notation 'P0 : max_{λ,F,Q} = ...' is incomplete; it should be 'P0 : max_{λ,F,Q} ...' or a utility function should be defined on the right-hand side, such as 'maximize U(λ,F,Q) = ...'.","section":"Eq. (23a)"}],"recommendation":"major_revision","confidential_remarks":"The paper's system model and hierarchical control idea are coherent, and the energy formulations for UAV propulsion and hovering are a positive feature. The main weakness is validation: single-seed experiments, ablation-only baselines, and a reward function that is exactly the optimization objective. These issues are fixable within the manuscript's scope, so I recommend major revision rather than rejection. In the revision, the authors should add multi-seed statistics, convergence comparisons, at least one published baseline or clearly scope their claims, and provide a full set of hyperparameters. I would also ask them to acknowledge the reward-objective coupling explicitly and to test robustness to the stated assumptions."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Hi [Colleague], quick take on arXiv:2507.05722.\n\nThis is a straightforward, competently assembled paper on UAV-assisted vehicular edge computing. What's new is the specific combination: a high-altitude UAV relay with low-altitude computing UAVs, partial offloading, and a hierarchical SAC controller where the global policy outputs offloading ratios and trajectories while a priority-based scheduling layer does node selection and resource allocation. That exact package isn't in the cited literature, and the system model and problem formulation (Section II) are coherent, with consistent delay/energy equations and clear constraints. The SAC reformulation is standard but sensible, and the separation of global decisions from local scheduling is a reasonable way to handle the large action space.\n\nThe soft spot is the evaluation, not the model. All the performance claims rest on Figures 3-5, which show single learning curves with no error bars or seed counts. The baselines are ablations of the proposed system (NoPriority, FixedUAV, DQN), not established VEC benchmarks, so beating them doesn't establish state-of-the-art. The 'convergence speed' claim in the abstract and conclusion is never shown; there's no convergence curve comparing the proposed method against DQN or any other algorithm. Key parameters (normalization coefficients, the 30% resource threshold, scoring weights) are not reported, and no code or hyperparameters are provided. Even within the idealized model—perfect global state, distance-based LoS/NLoS channels, no interference—the evidence doesn't support the comparative claims as written. The stress-test note is on target.\n\nOne thing the reader's report flagged that I'd push back on: the reward being the same as the objective is standard practice in RL for constrained problems; that's not a flaw. The load-bearing issue is purely empirical.\n\nWho is this for? Researchers working on UAV-assisted VEC who want a reference for a hierarchical control decomposition. The formulation has value. But as submitted, the performance claims should be treated with caution.\n\nRecommendation: send to review. A serious referee can push the authors to add multiple seeds, error bars, hyperparameters, a convergence plot, and at least one non-ablation baseline. If that holds up, the paper would be a reasonable contribution. Without it, this is a conditional accept at best.","headline":"A coherent hierarchical SAC offloading design whose performance claims are not yet backed by the experiments as reported.","tokens_in":10941,"tokens_out":2152,"would_cite":false,"duration_ms":23786,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A dual-layer UAV architecture—high-altitude relay plus low-altitude computing drones—steered by hierarchical soft actor-critic and priority-based scheduling, achieves higher task completion and system utility than fixed-trajectory…","keywords":["UAV-assisted vehicular edge computing","task offloading","deep reinforcement learning","soft actor-critic","trajectory planning","priority-based scheduling","partial offloading","hierarchical decision making"],"falsifier":"Run the identical algorithm with delayed or partial state feedback and packet-level interference; if task completion at 40 MHz relay bandwidth falls below the 0.820 reported for the fixed-trajectory baseline, the claimed benefit of dynamic trajectories and ratio control is contradicted.","tokens_in":9986,"feed_emoji":"🚁","tokens_out":6420,"duration_ms":67931,"temperature":0.7,"pith_summary":"The paper aims to establish that a two-tier aerial computing network can keep vehicular tasks on schedule under heavy, fast-changing traffic. A high-altitude UAV relays delay-tolerant tasks to idle roadside units or a base station, while low-altitude UAVs supply nearby computing capacity, and the two layers coordinate through a mixed continuous-and-discrete control scheme. The authors reformulate the joint delay-and-energy optimization as a Markov decision process and solve it with a hierarchical soft actor-critic algorithm that decides offloading ratios and drone trajectories, leaving node selection and CPU allocation to a priority-based rule. Reported simulations show the scheme keeping task completion near 95 percent at full relay bandwidth and about 87.5 percent when bandwidth drops to 40 MHz, while fixed-trajectory, no-priority, and DQN baselines decline more steeply.","feed_headline":"Two-layer drone network keeps 95% of vehicle tasks on time","feed_subtitle":"A high-altitude relay plus low-altitude compute drones, steered by soft actor-critic, beats fixed and DQN baselines.","key_machinery":"The load-bearing mechanism is a two-level action decomposition. The upper level is a soft actor-critic agent that observes a global state—vehicle positions, tasks, speeds, node locations and remaining compute, and LUA V motion—and outputs continuous actions: the per-vehicle offloading-ratio vector and LUA V headings and speeds. The lower level is a priority-based scheduler that orders tasks by priority and deadline, scores candidate nodes by distance and remaining resources, and requires a node to hold more than 30 percent free capacity before it can be selected. This decomposition turns a mixed-integer nonlinear program into a Markov decision process with a small continuous action space, which is what makes SAC training stable enough to compare favorably with the baselines.","core_discovery":"The central claim, stated on the paper's own terms, is that decoupling global from local decisions is what makes UAV-assisted vehicular offloading both tractable and effective. The proposed dual-layer architecture has a high-altitude UAV (HUA V) act as relay and coordinator and low-altitude UAVs (LUA Vs) act as nearby compute nodes; the algorithm then splits the decision: a soft actor-critic agent outputs continuous offloading ratios and LUA V trajectories, and a priority-based scheduler picks target nodes and allocates their remaining CPUs. Against baselines that drop priority scheduling, freeze trajectories, or use a discrete-action deep Q-network, the reported experiments show the proposed method sustaining higher task completion rates as vehicle density scales from 5 to 50 and as relay bandwidth narrows, at a reported 0.950 completion rate at 100 MHz and 0.875 at 40 MHz.","pith_inferences":["Beyond the paper, the obvious stress test is to replace the perfect global state assumption with noisy, delayed, or partial feedback; if completion gains shrink towards the baselines, the real-world value rests on the HUA V's sensing and reporting quality, not on the algorithm alone.","A second extension is to sweep the fixed 30-percent free-resource threshold in the scheduling rule; the reported gains could depend on that threshold matching the simulated load.","The model omits interference, control overhead, and sub-slot mobility, so a packet-level or ray-tracing simulator with concurrent transmissions would show how much of the reported 0.950 completion rate survives realistic radio conditions.","The same two-level decomposition—continuous global policy plus a rule-based local scheduler—could transfer to other heterogeneous edge settings with large mixed action spaces, such as ground base stations plus drone relays."],"forward_implications":["If the central claim holds, operators can absorb rising vehicle density by adding low-altitude UAVs and letting the high-altitude relay re-dispatch them, since completion rate stays high from 5 to 50 vehicles in the reported runs.","Urgent tasks benefit most from the priority ordering, because deadlines and priority values determine scheduling order before resource allocation.","Deploying more LUA Vs raises completion rate but eventually stops raising system utility, since propulsion energy grows; the reported utility curves identify a deployment sweet spot rather than a monotone benefit.","The continuous SAC action space converges faster than the discrete DQN baseline in the reported episodes, suggesting that quantizing offloading ratios into discrete actions was a bottleneck for the baseline.","Under scarce relay bandwidth the dynamic trajectory and ratio control matter most: at 40 MHz the proposed method reports 0.875 completion while the fixed-trajectory baseline reports 0.820."],"supporting_citations":[{"why":"Supplies the quasi-static position model within each time slot on which the MDP state transitions rest.","marker":"[15]"},{"why":"Provides the non-line-of-sight ground-to-ground channel measurements used for vehicle-to-RSU rates.","marker":"[16]"},{"why":"Provides the log-normal shadowing and 3-D radio channel construction used in the communication model.","marker":"[17]"},{"why":"Supplies the hierarchical aerial computing idea that the dual-layer HUA V relay and LUA V compute architecture extends.","marker":"[18]"},{"why":"Defines the UAV-assisted task offloading problem class and serves as the setting the proposed scheme is designed to improve.","marker":"[13]"},{"why":"Provides the deep-reinforcement-learning resource management baseline context that motivates the SAC-based approach and the DQN comparison.","marker":"[14]"}],"fun_headline_variants":["Hierarchical deep RL optimizes dual-drone relay and compute offload","Soft actor-critic steers UAV relay and compute for vehicle tasks","Global-local split lifts task completion in UAV edge offloading","Drone duo: relay and compute, trained via hierarchical RL"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The plan assumes the high-altitude UAV receives complete, accurate, immediate knowledge of every vehicle's position, task, speed, and every node's remaining resources, and that radio links are only distance-based line-of-sight/non-line-of-sight rates with no interference or control overhead.","fun_headline_variants_meta":{"raw":{"variants":["Hierarchical deep RL optimizes dual-drone relay and compute offload","Soft actor-critic steers UAV relay and compute for vehicle tasks","Global-local split lifts task completion in UAV edge offloading","Drone duo: relay and compute, trained via hierarchical RL"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00088,"raw_usage":{"total_tokens":3810,"prompt_tokens":957,"completion_tokens":2853,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":573,"completion_tokens_details":{"reasoning_tokens":2780}},"tokens_in":573,"tokens_out":2853,"duration_ms":26467,"temperature":1.0,"reasoning_tokens":2780,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T19:19:40.587146+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the identical algorithm with delayed or partial state feedback and packet-level interference; if task completion at 40 MHz relay bandwidth falls below the 0.820 reported for the fixed-trajectory baseline, the claimed benefit of dynamic trajectories and ratio control is contradicted.","supporting_citations":[{"cited_title":"UA V-relaying-assisted multi- access edge computing with multi-antenna base station: Offl oading and scheduling optimization,","cited_arxiv_id":null,"evidence_quote":"Supplies the quasi-static position model within each time slot on which the MDP state transitions rest."},{"cited_title":"A survey on channel soundin g technologies and measurements for UA V-assisted communica tions,","cited_arxiv_id":null,"evidence_quote":"Provides the non-line-of-sight ground-to-ground channel measurements used for vehicle-to-RSU rates."},{"cited_title":"Sparse bayesian learning-based hierarchical cons truction for 3d radio environment maps incorporating channel shadowing ,","cited_arxiv_id":null,"evidence_quote":"Provides the log-normal shadowing and 3-D radio channel construction used in the communication model."},{"cited_title":"Hierarchical aerial computing for Internet of things via cooperation of HAPs and UA Vs,","cited_arxiv_id":null,"evidence_quote":"Supplies the hierarchical aerial computing idea that the dual-layer HUA V relay and LUA V compute architecture extends."},{"cited_title":"UA V-assisted task offloading in vehicular edge computing networks,","cited_arxiv_id":null,"evidence_quote":"Defines the UAV-assisted task offloading problem class and serves as the setting the proposed scheme is designed to improve."},{"cited_title":"Deep reinforcement learning based resource management for multi-access edge computing in vehicular ne tworks,","cited_arxiv_id":null,"evidence_quote":"Provides the deep-reinforcement-learning resource management baseline context that motivates the SAC-based approach and the DQN comparison."}],"review_version":1}