{"id":"54b2ed35-5fe4-4ec9-83bd-fa165364e805","arxiv_id":"2504.13554","paper_version":2,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":7,"one_line_summary":"A Hungarian-plus-diffusion multi-agent RL controller with a Lyapunov energy queue assigns UAV rescue tasks to ground robots and airships, reporting lower latency and steadier energy use in simulation.","lead":"This paper proposes a rescue system where low-flying drones share heavy computing work with ground robots and an airship, using a learning algorithm to choose tasks and exploration areas. It reports lower task delays and energy use than two standard multi-agent reinforcement learning baselines.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 4.2's claim that solving P3 yields the optimal solution of P1 is unsupported: P3 minimizes an upper bound over F only, while P1 minimizes over F, M, and W, and the paper never shows how M and W are optimized inside P3.","rationale":"The paper's central claim is that P3, solved by HG-MADDPG, yields the optimal solution of P1, which is a joint optimization over task offloading F, computation-resource allocation M, and trajectory control W. The most load-bearing weakness is that this reduction is not a valid equivalence: P3 minimizes an upper bound of the drift-plus-penalty term and optimizes only F, while the objective and constraints still involve M and W. Even if the per-subarea parameters required by Eq. (30) were magically available, the formal gap would remain. The reader's rationale flags this P3 issue, but the reader's stated weakest_assumption is instead the availability of D_b, C_b, and f_b in Eq. (30); that is a real operational problem, but it is secondary to the variable-mismatch issue. I therefore mark agreement as partial: the same family of concerns is present, but the load-bearing point is the unproven and internally inconsistent P1-to-P3 reduction. The paper also lacks code, error bars, an actor update rule, and a clear treatment of M and W in the MARL action space, all of which reinforce the rejection but are not the single decisive flaw. Because the central algorithmic claim is unsupported as written, the REJECT verdict should stand unchanged; a corrected derivation and a properly specified action set could make the approach salvageable.","tokens_in":29373,"tokens_out":4650,"duration_ms":46189,"concrete_test":"Re-derive Section 4.2 by substituting Eqs. (8), (10), (11), and (13) into P3. Fix an arbitrary offloading ratio ς and show that the P3 objective is still nonconstant in f_j,u and du,j(ti). Then solve the minimal instance with one UAV, one GER, one subarea, two candidate trajectories with distances d1 ≠ d2, and two candidate computation allocations f_a ≠ f_b: compute the P3 objective at (ς, f_a, d1) versus (ς, f_b, d2). If the ranking changes, the argmin over F in Eq. (29) is not well-defined without first choosing M and W, so P3 is not a well-posed optimization over F alone and the claimed equivalence to P1 fails. This check can be done by hand or with a short script.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is the reduction from P1 to P3 in Section 4.2. P1 is min over F, M, W of the sum of task completion latencies Ttota(ti) under constraints (20a)-(20k). P2 repeats this per slot, but P3 changes the optimization variables to F only while keeping constraints (28f)-(28j), which explicitly involve M (computation resource allocations f_j,u) and W (trajectory variables du(ti), Lu(ti)). The P3 objective still contains Ttota(ti), which by Eqs. (8), (10), and (11) depends on f_j,u through Tcomp_j(ti) and on du,j(ti) through Ru,j(ti) in Ttran_u,j(ti). Minimizing this expression over F alone is not equivalent to minimizing over (F, M, W), and no derivation shows that the M and W chosen by the Hungarian-based exploration or by any other rule are the optimizers of P3. Standard Lyapunov drift-plus-penalty theory only provides an O(1/V) bound on the time-average objective under boundedness or Slater-type conditions; it does not produce the exact optimal solution of the original MINLP. Equation (29) is also not evaluable from F alone: Ttota is unparameterized unless M and W are first selected, and the HG-MADDPG action set in Eq. (32), A_i = {m_u,j, p_u,j}, does not include either the computation-allocation variables M or the trajectory variables W. Thus the assertion, 'By solving P3, the optimal solution of P1 can be obtained,' is internally inconsistent and unsupported as stated.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript studies joint task assignment and exploration optimization for a low-altitude UAV rescue system in which UAVs, ground-embedded robots (GERs), and airships cooperate. It formulates a long-term mixed-integer nonlinear program P1 that minimizes total task completion latency under an energy budget and mobility/resource constraints, then applies Lyapunov optimization to obtain a per-slot problem P3. The proposed HG-MADDPG algorithm combines a Hungarian-algorithm-based subarea selection with a diffusion-model-enhanced multi-agent deep deterministic policy gradient for task offloading decisions. Simulations compare HG-MADDPG against MADDPG and MAPPO and report reductions in task completion latency of 20.35% and 12.56%, together with more stable queue behavior and exploration trajectories.","tokens_in":29738,"tokens_out":7141,"duration_ms":66486,"significance":"If the claimed derivation were correct, the paper would offer a useful engineering combination of Lyapunov decomposition, Hungarian assignment, and generative-diffusion MARL for UAV-assisted rescue. The manuscript is clearly organized, includes a complexity analysis, and reports parameter sensitivity experiments, which are helpful for reproducibility. However, the central equivalence between P3 and P1 is asserted but not established, the proposed action space omits two of the three variable classes of the original problem, and the Hungarian exploration cost requires information that the unknown-environment scenario does not provide. These are load-bearing gaps, so the stated significance cannot be assessed from the current manuscript. No code or proofs are provided, and the experimental curves lack error bars or statistical tests, further limiting the strength of the empirical claims.","major_comments":[{"comment":"P3 is written as a minimization over F only, whereas P1 and P2 minimize over (F, M, W). The P3 objective still contains Ttota(ti), which by Eqs. (8), (10), and (11) depends on the computation allocation f_{j,u}(ti) and on the trajectory-dependent distances d_{u,j}(ti), and constraints (28f)-(28j) explicitly involve M and W. The paper does not show how M and W are selected inside P3, nor that the choices made by the Hungarian area selection or by the HG-MADDPG actions are optimal for P3. Therefore the statement \"By solving P3, the optimal solution of P1 can be obtained\" is unsupported. Standard Lyapunov drift-plus-penalty theory provides, under boundedness and Slater-type conditions, an O(1/V) suboptimality bound on the time-average objective, not an exact solution of the original MINLP. At best, Eq. (29) defines a heuristic per-slot policy.","section":"Section 4.2, Eq. (28)-(29)"},{"comment":"The action of each agent is defined as A_u^i = {m_{u,j}(ti), p_{u,j}(ti)}, where m is the selected GER and p is the offloading ratio. This action set does not include the computation-resource allocation variables M = {f_{j,u}(ti)} or the trajectory variables W = {d_u(ti), L_u(ti)}, even though the conclusion states that HG-MADDPG optimizes \"the computation resource allocation, task offloading ratio, and GER selection.\" Since P3 is supposed to be solved over these variables, the algorithm cannot be the solver of P3 unless the missing variables are set by an external rule, and no such rule is given. The constraints (28f)-(28j) involving M and W are therefore not enforced by any optimization step.","section":"Section 5.3.2, Eq. (32)"},{"comment":"The Hungarian area-selection cost requires per-subarea values of data size D_b, computation intensity C_b, and average GER computing power f_b. In the stated scenario of an unknown disaster area with no prior maps or load information, these quantities are not available before exploration. The paper even claims in Section 5.2.1 that the Hungarian approach \"eliminates the need for the agent to acquire the status of all GERs or the task data size across different areas,\" which contradicts the need for D_b, C_b, and f_b in Eq. (30). Without a clearly stated source for this information, Algorithm 2 cannot be executed, and the exploration half of the claimed joint optimization is not well defined.","section":"Section 5.2.2, Eq. (30)"},{"comment":"The reward is defined as r_u^i = E[V * Ttota + Q(ti) * yk(ti) | Q(ti)], which is a positive cost-like quantity. In a multi-agent deep deterministic policy gradient framework, the agent maximizes the expected discounted sum of rewards, so a positive reward of this form would encourage larger latency and larger queue backlog. The paper does not state that the reward is negated, that the actor minimizes the Q-value, or that Eq. (33) is a cost to be minimized. This sign inconsistency affects the entire RL training update and is not a presentation detail.","section":"Section 5.3.2, Eq. (33)"},{"comment":"The introduction and contributions promise airship-assisted computing: \"An airship is engaged to handle offloaded tasks only when GERs lack sufficient computation resources.\" However, the system model only defines a U2G channel model in Eqs. (3)-(8); there is no U2A communication model, no airship computation latency model, and no airship resource constraints in P1 or P3. The task latency model in Eqs. (9)-(13) only considers UAV-local and GER execution. The claimed airship offloading capability is therefore absent from the mathematical formulation and from the experiments, so the framework's stated contribution is not supported by the model.","section":"Sections 3.1-3.4 and Contributions"}],"minor_comments":[{"comment":"Several UAV constraints are indexed with \"∀j∈J\" instead of \"∀u∈U\", e.g., constraints (20i), (20j), and (20k); the same typo appears in (26h)-(26j) and (28h)-(28j).","section":"Section 3.6, constraints (20i)-(20k)"},{"comment":"Table 3 contains corrupted placeholder glyphs (e.g., \"/uni00000013/uni00000015...\") in several rows, making some parameter values unreadable.","section":"Section 6.1, Table 3"},{"comment":"The reward in Eq. (33) omits the constant term Θ from the drift upper bound in Eq. (28); if the intent is to use the per-slot upper-bound objective, the expression should be Θ + V*Ttota + Q*yk, and the sign convention relative to reward maximization should be clarified.","section":"Section 5.3.2"},{"comment":"The term \"queuing energy\" is described verbally as the average difference between the UAV's energy consumption in the current round and the previous episode, but it is not defined by an equation; this makes Fig. 6 difficult to interpret.","section":"Section 6.2.2"},{"comment":"The text states that one objective is to maximize the task offloading rate, but P1 in Eq. (20) only minimizes task completion latency with an energy budget; no offloading-rate term appears in the objective or in the reported metrics.","section":"Section 3.6"},{"comment":"The initial label setting α_u = min(C[u][1],...,C[u][B]) with the matching condition C[u][b] ≤ α_u + β_b is nonstandard for the minimization form of the Hungarian algorithm; the feasibility of the labeling and the augmenting-path adjustment rule should be stated precisely.","section":"Section 5.2.2, Algorithm 2"}],"recommendation":"reject","confidential_remarks":"The manuscript is within the scope of the journal, but the technical core is not yet in a defensible state. The largest concerns are the unsupported equivalence between P3 and P1, the mismatch between the optimization variables and the proposed action space, and the unresolved source of the per-subarea information required by the Hungarian area selection. These are not local fixes; they require reworking the problem decomposition, the algorithm's variable coverage, and the experimental validation. I also note substantial methodological overlap with the authors' related works cited as [9], [21], and the present manuscript would benefit from a much sharper statement of what is actually optimized versus what is a heuristic."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know about this paper. First, the engineering idea is reasonable: combining Hungarian area selection with a diffusion-based MADDPG for rescue UAV task offloading is a sensible way to cut observation dimensionality and improve action generation. Second, the theoretical framing does not hold up. The paper claims that solving P3—a per-slot drift-plus-penalty problem over F only—gives the optimal solution to P1, which is a MINLP over F, M, and W. That is not true, and the paper never shows how M and W are optimized.\n\nWhat the paper does well: the system model is thorough (LoS/NLoS fading, Nakagami, energy queue, task latency), and the simulations show 20% and 12% latency improvements over MADDPG and MAPPO. The Hungarian area selection is a clever preprocessing step, and the diffusion actor is a natural fit for continuous offloading ratios.\n\nThe load-bearing problem is the Lyapunov step. P3 still contains Ttota, which depends on M and W through Eqs. (8)–(11), so minimizing over F alone is not equivalent to minimizing the original objective. Eq. (29) is an assertion, not a proof; standard drift-plus-penalty gives an O(1/V) bound on the time average, not exact optimality. Also, the action set in Eq. (32) only includes GER selection and offloading ratio, so the algorithm never actually sets the computation allocations f_j,u or the trajectory variables that appear in the constraints. That mismatch means the paper is solving a different problem than the one it formulates.\n\nLess severe but real: the reward in Eq. (33) is a positive drift-plus-penalty cost while the text says agents maximize rewards; the Hungarian cost in Eq. (30) needs per-subarea D_b, C_b, f_b that a UAV cannot know in a disaster zone; and the experiments have no error bars, seeds, or ablations, nor code, so the reported gains cannot be independently checked. The closest prior works [21] and [9] are mentioned in the text but not run as baselines.\n\nWho is this paper for? Someone working on UAV offloading with MARL will read it for the system model and the Hungarian+diffusion combination. The optimality claims should be ignored. It deserves a serious referee—the ideas are salvageable—but as written it should not be accepted.","headline":"A plausible engineering combo (Hungarian + diffusion MARL for rescue offloading) undermined by an unsupported Lyapunov claim: the paper says solving P3 yields the optimal P1, but P3 only minimizes an upper bound over a subset of the variables.","tokens_in":30327,"tokens_out":3016,"would_cite":false,"duration_ms":27552,"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":"The paper claims that solving the per-slot Lyapunov problem P3 with the hybrid HG-MADDPG algorithm yields the optimal solution to the long-term UAV task-assignment and exploration problem P1, with simulated task-completion latency cuts of…","keywords":["UAV rescue","task assignment","exploration optimization","multi-agent reinforcement learning","Lyapunov optimization","generative diffusion model","Hungarian algorithm","computation offloading"],"falsifier":"In a simulation of an unknown rescue area, hide $D_b$, $C_b$, and $f_b$ until a UAV first enters each subarea; if the Hungarian assignment cannot be computed or collapses to random selection without those values, the joint-optimization claim for the exploration half fails.","tokens_in":29122,"feed_emoji":"🚁","tokens_out":6005,"duration_ms":52680,"temperature":0.7,"pith_summary":"This paper tries to establish that the joint long-term problem of task assignment and exploration in a low-altitude UAV rescue network—minimizing task completion latency and energy consumption under a long-term energy budget—can be solved online by a per-slot drift-plus-penalty problem. It claims that solving the per-slot problem P3 with the proposed HG-MADDPG algorithm gives the optimal solution of the original problem P1. HG-MADDPG combines a Hungarian-algorithm area selection, which assigns each UAV to a rescue subarea, with a generative diffusion model inside a multi-agent deep deterministic policy gradient, which chooses offloading ratios and GER computing resources. If correct, rescue systems can make per-slot decisions without future channel or mobility information while keeping energy queues stable, with simulated latency reduced by 20.35% over MADDPG and 12.56% over MAPPO.","feed_headline":"A hybrid MARL policy cuts UAV rescue offloading latency by 20%","feed_subtitle":"Lyapunov queue stability plus Hungarian area choice plus a diffusion actor keeps rescue task times low.","key_machinery":"The load-bearing mechanism is the virtual energy queue $Q(t_{i+1})=\\max\\{Q(t_i)+E^{\\mathrm{total}}(t_i)-\\bar{E}_u,0\\}$, whose Lyapunov drift is upper-bounded by $\\Theta+Q(t_i)y_k(t_i)$, so P3 minimizes the bound $\\Theta+V\\mathbb{E}[T^{\\mathrm{total}}(t_i)]+Q(t_i)y_k(t_i)$ each slot. The Hungarian area-selection algorithm matches UAVs to subareas using the cost $\\|L_u(t_i)-L_b\\|+D_b+C_b-e^{\\mathrm{rema}}_u(t_i)-f_b$, which reduces each agent's observation to its assigned subarea. The action generator is a denoising diffusion model reversed from Gaussian noise $x_T\\sim\\mathcal{N}(0,I)$ to a task ratio $x_0$ conditioned on the observation, with mean $\\mu_\\theta(x_t,\\eth,t)=\\frac{1}{\\sqrt{\\alpha_t}}(x_t-\\frac{\\beta_t}{\\sqrt{1-\\alpha_t}}\\epsilon_\\theta(x_t,\\eth,t))$, replacing the MADDPG actor network.","core_discovery":"Section 4.2 asserts that by solving P3, the optimal solution of P1 is obtained. The argument is that Lyapunov drift-plus-penalty minimization in each slot, with the virtual energy queue $Q(t_i)$, upper-bounds the long-term objective and keeps the queue stable, so the per-slot minimizer of $\\Theta + V\\mathbb{E}[T^{\\mathrm{total}}(t_i)] + Q(t_i)y_k(t_i)$ achieves the long-term optimum. The exploration half is a Hungarian assignment whose cost matrix combines distance to the subarea center, data size, computation intensity, remaining UAV energy, and average GER computing power; the task-assignment half is a diffusion-model actor inside MADDPG that generates offloading ratios and GER selections. Simulations report average latency reductions of 20.35% versus MADDPG and 12.56% versus MAPPO, with queuing energy and task-completion latency staying in a stable range as computing power, GER count, and data size vary.","pith_inferences":["The paper treats subarea data size $D_b$, computation intensity $C_b$, and average GER computing power $f_b$ as known when forming the Hungarian cost; a real unknown disaster area would require an additional first-visit sensing step that the paper does not specify.","The reported gains may be driven by the Hungarian dimensionality reduction rather than by the diffusion action generator; an ablation that replaces the diffusion actor with a standard Gaussian actor while keeping the Hungarian assignment would isolate the source.","A testable extension is to run the same Lyapunov queue and reward in a single-agent DDPG with Hungarian area selection; if latency gains persist, the contribution is the area-selection decomposition rather than multi-agent generative learning."],"forward_implications":["The long-term energy-budget constraint (20a) is replaced by a per-slot queue-stability condition, so no future information about channels or mobility is needed at decision time.","Each UAV only observes its Hungarian-assigned subarea, shrinking the observation space of the multi-agent policy and lowering per-agent complexity.","The reported 20.35% and 12.56% latency reductions over MADDPG and MAPPO hold when subarea statistics are available for the Hungarian cost matrix.","Adjusting the weight $V$ trades task latency against queue stability, letting an operator shift between fast and energy-conserving modes."],"supporting_citations":[{"why":"Supplies the Lyapunov-guided diffusion-based reinforcement-learning method that motivates combining queue stability with a diffusion policy.","marker":"[9]"},{"why":"Defines MADDPG, the multi-agent actor-critic algorithm that HG-MADDPG extends and the main baseline it outperforms.","marker":"[29]"},{"why":"Provides the Lyapunov drift-plus-penalty theory used to claim that solving the per-slot problem P3 solves the long-term problem P1.","marker":"[47]"},{"why":"Defines the Hungarian algorithm used for exploration area selection and its complexity.","marker":"[48]"},{"why":"Defines denoising diffusion probabilistic models, the generative mechanism behind the action generator in HG-MADDPG.","marker":"[50]"},{"why":"Supplies experimental parameter settings for UAV and GER computing and communication in the evaluation.","marker":"[24]"}],"fun_headline_variants":["Diffusion-actor MARL cuts rescue latency 20% vs MADDPG","Hungarian+diffusion MARL: 20% lower rescue latency","Generative AI + MARL makes rescue offloading stable and fast","UAV rescue latency cut 20% with hybrid MARL and diffusion","Diffusion-actor MARL: 20% faster rescue offloading, stable queues"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The Hungarian area-selection cost requires each subarea's data size, computation intensity, and average GER computing power to be known before the UAVs explore those subareas, although the rescue area is assumed to be unknown.","fun_headline_variants_meta":{"raw":{"variants":["Diffusion-actor MARL cuts rescue latency 20% vs MADDPG","Hungarian+diffusion MARL: 20% lower rescue latency","Generative AI + MARL makes rescue offloading stable and fast","UAV rescue latency cut 20% with hybrid MARL and diffusion","Diffusion-actor MARL: 20% faster rescue offloading, stable queues"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000583,"raw_usage":{"total_tokens":2739,"prompt_tokens":938,"completion_tokens":1801,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":554,"completion_tokens_details":{"reasoning_tokens":1701}},"tokens_in":554,"tokens_out":1801,"duration_ms":12718,"temperature":1.0,"reasoning_tokens":1701,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T12:06:20.912970+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"In a simulation of an unknown rescue area, hide $D_b$, $C_b$, and $f_b$ until a UAV first enters each subarea; if the Hungarian assignment cannot be computed or collapses to random selection without those values, the joint-optimization claim for the exploration half fails.","supporting_citations":[{"cited_title":"Intro- duction to algorithms (3-rd edition),","cited_arxiv_id":null,"evidence_quote":"Defines the Hungarian algorithm used for exploration area selection and its complexity."},{"cited_title":"Joint task offloading and resource allocation in aerial- terrestrial uav networks with edge and fog computing for post- disaster rescue,","cited_arxiv_id":null,"evidence_quote":"Supplies experimental parameter settings for UAV and GER computing and communication in the evaluation."},{"cited_title":"Dnn partitioning, task offloading, and resource allocation in dynamic vehicular networks: A lyapunov-guided diffusion-based reinforcement learning approach,","cited_arxiv_id":null,"evidence_quote":"Supplies the Lyapunov-guided diffusion-based reinforcement-learning method that motivates combining queue stability with a diffusion policy."},{"cited_title":"Maddpg-based joint service placement and task offloading in mec empowered air-ground integrated networks,","cited_arxiv_id":null,"evidence_quote":"Defines MADDPG, the multi-agent actor-critic algorithm that HG-MADDPG extends and the main baseline it outperforms."}],"review_version":1}