{"id":"10aa7ad5-b521-4243-bac8-3c03aaeb17ca","arxiv_id":"2501.14832","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"A semantic-aware network converts images to importance-weighted text triplets and uses a diffusion-model-based power allocation policy to maximize a self-defined semantic transmission quality metric.","lead":"This paper designs a wireless network that uses large AI models to turn images into short text descriptions, scores each description by importance, and allocates transmit power to those descriptions using a diffusion model. It proposes that this semantic-aware approach can reduce data traffic and improve transmission quality for image-based intelligent applications.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim is unverifiable because the triplet drop probability P_dj—the mapping from transmit power to semantic transmission quality—is never specified; every reported simulation curve depends on it.","rationale":"The reader's weakest_assumption identifies exactly this point, and my own reading agrees: the optimization objective is the semantic quality Q, but Q depends on an unstated P_dj. The paper's self-referential metric and missing simulation details compound the issue, but they are secondary. A supply of P_dj and parameters would allow the central comparison to be tested; absent that, REJECT is appropriate. I therefore recommend no change to the reader's verdict.","tokens_in":9284,"tokens_out":6149,"duration_ms":61018,"concrete_test":"Require the authors to write P_dj explicitly, e.g., P_dj = sum_{k=L_E+1}^{L_T} C(L_T,k) eps_j^k (1-eps_j)^{L_T-k} with eps_j from a stated modulation/BER model and with all constants taken from [7], including numerical values for L_T, L_E, noise PSD, and fading distribution. Then independently recompute the average-allocation and importance-based curves in Fig. 5 from this formula and check whether the reported diffusion-model curve outperforms them by the claimed margin; if the baseline curves cannot be reproduced with the stated parameters, the central simulation result is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing condition for the claimed optimality of the diffusion-based power allocation is a concrete, correct formula for P_dj (and hence for Q = sum_j I_j(1-P_dj)) as a function of the allocated power, channel state, and coding parameters. Section III-A defines P_dj verbally as the probability that more than L_E bit errors occur in a fixed-length L_T-bit triplet, and it asserts that the bit error probability has a 'complex exponential coupling relationship' with transmit power, citing [7]. But no BER expression, modulation order, coding scheme, L_T, L_E, noise level, fading distribution, or blocklength is given; Section IV merely says channel parameters are 'set as indicated in [7].' The objective is therefore not a well-defined function of p, so the reverse-diffusion policy in Section III-B has no mathematically specified reward to optimize, and Figures 4-6 cannot be reproduced or checked. If the inherited [7] model is inaccurate for this setting, the optimal allocation and the claimed margins over the baselines change. The missing formula is the first gate: until P_dj is written down, the central claim is not assessable.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a semantic-aware image communication architecture in which the RelTR scene-graph model extracts textual semantic triplets from images, the CLIP model assigns an importance score to each triplet, and a diffusion-model-based reinforcement learning policy allocates transmit power among triplets. The stated objective is to maximize the image semantic transmission quality Q = sum_j I_j * (1 - P_dj) under a total power constraint, where I_j is the semantic importance and P_dj is the triplet drop probability. The authors report simulations (Figures 4-6) claiming that the proposed diffusion-model scheme outperforms equal-power and importance-based allocation and converges faster than Soft Actor-Critic. The paper also discusses open issues in multimodal semantic networks. The core optimization problem, however, is never completely specified, and the evaluation uses the same metric that the policy is trained to maximize.","tokens_in":9393,"tokens_out":4785,"duration_ms":43616,"significance":"If fully specified and independently validated, the proposed architecture could be a useful step toward semantic-aware resource allocation: it combines established large models (RelTR, CLIP) with a modern generative policy (diffusion model), and the idea of allocating power according to semantic importance is topical and potentially impactful for reducing transmitted data and improving task-level quality. The manuscript, however, does not deliver a checkable optimization statement, a concrete channel/reliability model, or an external evaluation. There are no machine-checked proofs, parameter-free derivations, or reproducible code; the central quantitative claims rest on simulations whose underlying equations and parameters are absent. Because the optimization objective and the evaluation metric coincide, the reported improvements are not independently meaningful. The contribution is therefore more of an architectural vision than a verifiable research result in its current form.","major_comments":[{"comment":"The paper's optimization objective is not a well-defined function of the decision variables. The text defines Q = sum_{j=1}^N I_j * (1 - P_dj), but P_dj is only described as the probability that the number of erroneous bits in a triplet exceeds the error-correction capability threshold, and it is said to follow from a bit error probability that has \"a complex exponential coupling relationship with transmit power allocation decision [7]\". No equation for the bit error probability, no modulation order, no coding scheme, no blocklength L_T, no error-correction capability L_E, no noise variance, and no fading distribution are provided. Section IV merely states that channel parameters are \"set as indicated in [7]\". As a consequence, Q cannot be evaluated for a given power vector, the diffusion policy in Section III-B has no concrete reward function to optimize, and Figures 4-6 cannot be reproduced or checked against an explicit model. The authors must state P_dj(p) explicitly as a function of power, including all constants and the exact parameters taken from reference [7].","section":"Section III-A, definition of Q"},{"comment":"The proposed method is described only in prose. The environment vector e is said to include the wireless channel model, fixed encoding length of triplets, total transmit power, and number of triplets, but the state space, action space, reward function, discount factor, transition dynamics, and training procedure are not defined. The value network Q_nu and the \"Bellman operator\" mentioned at the end of Section III-B are not written down, and the objective used to train the denoising network epsilon_theta is not given. Without this specification, the claim that the reverse diffusion process maximizes semantic transmission quality is not a derivable statement, and the comparison of convergence behavior with Soft Actor-Critic cannot be assessed. Please provide a complete MDP formulation and the exact training loss, including how epsilon_theta and Q_nu are updated.","section":"Section III-B, diffusion-model-based allocation"},{"comment":"The evaluation is circular with respect to the design objective. The policy is trained to maximize Q, and the only reported performance metric is Q itself; no independent, task-level measure of semantic quality (for example, image retrieval accuracy, text-to-image generation fidelity, semantic similarity to the original image beyond the CLIP score, or a user study) is presented. The observed \"improvement\" over the benchmarks therefore partly reflects the optimizer succeeding on its own objective, and it does not establish that the proposed scheme improves semantic communication in an externally verifiable sense. At least one downstream-task metric, evaluated on data not used in training, should be reported.","section":"Section IV, Figures 4-6"},{"comment":"The experimental conditions are under-specified. The manuscript does not state the number of users U, the number of triplets N per image, the channel model parameters taken from reference [7], the training hyperparameters (learning rate, batch size, number of episodes, network architecture), or the exact formulas for the two benchmark schemes (\"average allocation\" and \"importance-based allocation\"). No error bars or number of independent runs are given for Figures 4-6. This lack of detail prevents replication and makes it impossible to judge the statistical significance of the claimed gains. All parameters needed to reproduce the figures should be reported, or the code should be released.","section":"Section IV, simulation setup"}],"minor_comments":[{"comment":"The statement that the one-stage RelTR method achieves \"faster inference with fewer parameters\" is not supported by any measurement or comparison; either provide the quantitative evidence or remove the comparative claim.","section":"Section II, page 3"},{"comment":"The sentence \"This resource allocation scheme increases the loss probability of the most important triplet, but it maximizes the overall transmission quality\" is confusing as written; it likely means that some power is diverted away from the most important triplet, but the wording should be clarified.","section":"Section III-A, paragraph on power allocation"},{"comment":"The caption says \"The semantic transmission quality versus training epochs\" but the y-axis label is not visible in the figure; please ensure all axes and legends are legible.","section":"Section IV, Figure 4"},{"comment":"References [11] and [15] are cited in preprint or \"to be published\" form; if published versions are available, they should be cited instead.","section":"References"}],"recommendation":"reject","confidential_remarks":"I agree with the reader's assessment. The missing triplet-drop model is not a cosmetic omission: it removes the mathematical content of the optimization and makes the experimental results unverifiable. In addition, the self-referential evaluation metric undermines the significance of the reported gains. The manuscript could be reconsidered if the authors supply a complete system model, an independent semantic quality metric, and full simulation details."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The high-level idea is coherent and worth a look: use scene graph triplets for semantic image compression, score them with CLIP, and allocate power to maximize an importance-weighted reliability sum. That's a sensible pipeline, and the diffusion-based RL choice is a reasonable fit for the decision problem. The paper also explains the importance-versus-reliability trade-off clearly. What's new here is the specific combination, not the individual pieces, and the paper does a good job of motivating why semantic triplets are valuable. The problem is that the core mapping from power to drop probability is absent. The triplet drop probability P_dj is defined verbally as the probability that the number of erroneous bits exceeds the error correction capability, but no equation links it to transmit power. Section III-A cites [7] for a 'complex exponential coupling' but gives no formula, no modulation order, no L_T, no L_E, no channel parameters. Section IV simply says channel parameters are set as in [7]. Without P_dj, Q is not a well-defined function of the power vector, so the diffusion policy has no properly specified reward to optimize, and Figures 4-6 are not reproducible. There is also a circularity issue: the training objective and the evaluation metric are the same, so the reported gains over baselines partly reflect the optimizer succeeding on its own objective. No error bars, no detailed simulation settings, and no code are provided. I'm not claiming the approach is wrong—the architecture is plausible, and the components are all from published work. But as it stands, this is a position piece with suggestive curves, not a fully supported technical result. If the authors supply the actual BER/drop model and simulation parameters, the central claim could be checked; without them, the quantitative contribution evaporates. Who is this for? Researchers in semantic communication looking for architecture ideas might get value from the high-level framework, but not from the numerical results. I would not cite the simulations. A serious referee could be used to force the authors to provide the missing equations—the gap is fundamental, not cosmetic. My recommendation: send it to peer review only with that gate made explicit. Expect either a major revision that fills the gap or a rejection.","headline":"A plausible semantic-aware architecture sits on an unstated drop-probability model, making the central quantitative claim uncheckable as written.","tokens_in":10033,"tokens_out":4104,"would_cite":false,"duration_ms":37734,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that a pipeline of scene-graph extraction, CLIP importance scoring, and diffusion-model power allocation maximizes semantic image transmission quality under a total power constraint.","keywords":["semantic communication","resource allocation","large models","diffusion model","power allocation","semantic transmission quality","scene graph generation","CLIP"],"falsifier":"Re-run the quality comparison with the bit-error probability formula actually used for the drop probability replaced by a measured bit-error curve from a Rayleigh fading testbed, over the 800 to 2400 W range; if the diffusion allocation no longer beats importance-based allocation, the headline result is tied to the inherited channel model rather than to the proposed architecture.","tokens_in":8971,"feed_emoji":"📡","tokens_out":10566,"duration_ms":786409,"temperature":0.7,"pith_summary":"This paper tries to establish that a large-model-driven pipeline can make wireless image transmission both leaner and better by turning each image into text triplets, scoring those triplets by semantic importance, and letting a diffusion-model policy decide how much power each triplet gets. The authors define semantic transmission quality as the sum over triplets of importance times the probability the triplet survives the channel, and they report that their power-allocation scheme beats equal-power and importance-only allocation in fading-channel simulations. If the claims hold, future networks could transmit less data for the same image-based task while delivering the semantic content that matters most to the user.","feed_headline":"Diffusion-model power allocation wins on image semantic quality","feed_subtitle":"An edge server turns images into importance-scored text triplets; the policy sends the important ones with more power.","key_machinery":"The central mechanism is a conditional diffusion policy $\\pi_\\theta(\\mathbf{p}|\\mathbf{e})$ that starts from Gaussian noise and denoises toward a power vector $\\mathbf{p}$, conditioned on an environment vector $\\mathbf{e}$ holding the channel model, triplet encoding length, total power budget, and number of triplets. The semantic side is carried by RelTR, a one-stage relation transformer that extracts subject-relation-object triplets directly from an image, and by CLIP, a contrastive language-image model whose cosine similarity between text and image vectors gives each triplet its importance score $I_j$. These two components feed the metric $Q = \\sum_j I_j(1-P_{dj})$, where $P_{dj}$ is the probability that a triplet's erroneous bits exceed the error-correction threshold; the diffusion policy couples semantic value to physical-layer power.","core_discovery":"On the paper's own terms, the central discovery is that the power-allocation problem in a semantic network can be solved by combining two quantities that pre-trained models provide before transmission: the cosine similarity between each extracted subject-relation-object triplet and the original image, and the triplet drop probability obtained from the bit-error behavior of the fading channel. The quality metric is $Q = \\sum_{j=1}^N I_j(1-P_{dj})$, and the diffusion policy is trained to maximize this metric under a total transmit-power budget. The paper reports that a diffusion model with 12 denoising steps converges faster than the soft actor-critic baseline and produces the highest $Q$ across transmit powers from 800 to 2400 W, above both average allocation and importance-based allocation.","pith_inferences":["Beyond this paper, replacing the inherited bit-error formula with an end-to-end drop model learned from real channel traces would test whether the diffusion policy preserves its quality gain under model mismatch.","A temporal extension is natural: video and AR frames generate their own importance-scored triplets, so the same policy could allocate power across both users and time rather than image by image.","Comparing the diffusion policy with a classical convex solver for the same objective would show how much of the reported gain comes from the optimization machinery and how much from the semantic importance weighting itself."],"forward_implications":["Images can be transmitted as text triplets over conventional bit-based wireless links, so the architecture does not require a new physical layer.","Power that follows both CLIP importance and triplet drop probability improves the defined semantic transmission quality compared with equal-power or importance-only allocation.","A denoising depth near 12 steps balances exploration against overfitting and converges faster than the soft actor-critic baseline.","The same importance-plus-reliability metric can be reused for other multimodal tasks, including visual question answering and text-to-image generation."],"supporting_citations":[{"why":"Supplies the wireless channel model and the bit-error probability relationship with transmit power used to compute triplet drop probability and set simulation parameters.","marker":"[7]"},{"why":"Provides the CLIP model that maps text triplets and images into a shared vector space so cosine similarity yields each triplet's semantic importance score.","marker":"[12]"},{"why":"Defines the RelTR relation transformer used to extract subject-relation-object triplets from images in a single stage.","marker":"[13]"},{"why":"Supplies the tutorial background on generative diffusion models for network optimization that motivates the power-allocation policy design.","marker":"[11]"},{"why":"Describes the diffusion-based reinforcement-learning framework for edge services, on which the reverse denoising and value-network training are based.","marker":"[14]"},{"why":"Provides the soft actor-critic algorithm used as the convergence and quality baseline in the simulations.","marker":"[15]"}],"fun_headline_variants":["Diffusion models tune power to boost semantic quality","Diffusion policy maximizes semantic transmission in fading channels","Power allocation via diffusion beats actor-critic in semantic nets","Semantic-aware power control: diffusion model wins","Diffusion power control boosts semantic quality"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The central claim depends on an unstated formula that ties transmit power to triplet loss probability; if that formula does not describe the actual fading channel, the reported quality gains are not guaranteed.","fun_headline_variants_meta":{"raw":{"variants":["Diffusion models tune power to boost semantic quality","Diffusion policy maximizes semantic transmission in fading channels","Power allocation via diffusion beats actor-critic in semantic nets","Semantic-aware power control: diffusion model wins","Diffusion power control boosts semantic quality"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00057,"raw_usage":{"total_tokens":2666,"prompt_tokens":881,"completion_tokens":1785,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":497,"completion_tokens_details":{"reasoning_tokens":1714}},"tokens_in":497,"tokens_out":1785,"duration_ms":14261,"temperature":1.0,"reasoning_tokens":1714,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T15:56:27.801542+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the quality comparison with the bit-error probability formula actually used for the drop probability replaced by a measured bit-error curve from a Rayleigh fading testbed, over the 800 to 2400 W range; if the diffusion allocation no longer beats importance-based allocation, the headline result is tied to the inherited channel model rather than to the proposed architecture.","supporting_citations":[{"cited_title":"Personalized saliency in task-oriented semantic commu- nications: image transmission and performance analysis,","cited_arxiv_id":null,"evidence_quote":"Supplies the wireless channel model and the bit-error probability relationship with transmit power used to compute triplet drop probability and set simulation parameters."},{"cited_title":"Learning transferable visual models from natural language supervision,","cited_arxiv_id":null,"evidence_quote":"Provides the CLIP model that maps text triplets and images into a shared vector space so cosine similarity yields each triplet's semantic importance score."},{"cited_title":"RelTR: Relation transformer for scene graph generation,","cited_arxiv_id":null,"evidence_quote":"Defines the RelTR relation transformer used to extract subject-relation-object triplets from images in a single stage."},{"cited_title":"Enhancing deep reinforcement learning: a tutorial on generative diffusion models in network optimization,","cited_arxiv_id":null,"evidence_quote":"Supplies the tutorial background on generative diffusion models for network optimization that motivates the power-allocation policy design."},{"cited_title":"Diffusion-based reinforcement learning for edge-enabled AI-generated content services,","cited_arxiv_id":null,"evidence_quote":"Describes the diffusion-based reinforcement-learning framework for edge services, on which the reverse denoising and value-network training are based."}],"review_version":1}