{"id":"67e5b36f-a432-429f-90de-a9011e4834b4","arxiv_id":"1908.00510","paper_version":4,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"HALK, a primal-dual kernel method with greedy compression, attains O(T^{-1/2}+alpha) expected suboptimality with zero average constraint violation and an explicit dictionary-size bound.","lead":"A decentralized algorithm for networks of agents that learn functions from local data while staying close to neighbors. It proves a new tradeoff between accuracy, constraint satisfaction, and memory size.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Assumption 5's norm-ball enforcement (dividing KOMP's coefficient vector by a constant) is not shown to preserve KOMP's ||f_{t+1}-f̃_{t+1}|| <= ε guarantee; Lemma 3 (eq. 100) and therefore Theorem 2 rest on this unverified step.","rationale":"The reader's weakest_assumption identifies the same soft spot: Assumption 5 and its ad hoc norm-ball enforcement. I agree that this is the load-bearing issue. The rest of the proof is a fairly standard primal-dual analysis; Lemma 2's unspecified K1 is harmless because it only needs to be finite, and the model-order bound's dependence on dual variables would be controlled if primal iterates were bounded. The central claim therefore holds only if the ball constraint can be imposed without breaking KOMP's ε-accuracy guarantee. Since the paper gives no such argument, the correct verdict remains CONDITIONAL: accept if the clipping/KOMP interaction is analyzed and the rates re-derived; otherwise the O(T^{-1/2}+α) suboptimality and zero average violation are not established. I am not recommending rejection because the intended fix appears plausible, e.g., proving dual-variable bounds and then choosing R_B large enough that clipping is inactive, but that proof is currently absent.","tokens_in":32740,"tokens_out":11211,"duration_ms":120548,"concrete_test":"Run Algorithm 1 on the Section V-A setting with the coefficient-division step active, recording ||f̃_{i,t+1}|| and ||f_{i,t+1}-f̃_{i,t+1}|| after each KOMP call, with parameters such as R_B=1, η=0.01, and a deliberately large dual multiplier to force ||f̃|| > R_B. If the realized projection error ever exceeds the KOMP tolerance ε, the enforcement of Assumption 5 is inconsistent with Lemma 3, and Theorem 2's proof has a genuine gap.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The theorem's proof requires every f_{i,t+1} to lie in the R_B-ball (Assumption 5) and, simultaneously, to satisfy the KOMP error guarantee ||f_{i,t+1}-f̃_{i,t+1}|| <= ε used in Lemma 3 and Lemma 4. The only argument offered for the ball condition is the sentence in §IV saying it 'may be explicitly enforced by dividing the norm of the coefficient vector output from KOMP by a large constant.' That operation changes f_{i,t+1}. If the unprojected KOMP output has norm larger than R_B, the rescaled (clipped) output is at distance at least ||f̃_{i,t+1}|| - R_B from f̃_{i,t+1}; this need not be <= ε, and can be unbounded if f̃ grows. Algorithm 2 as printed contains no such ball constraint, so the algorithm analyzed is not exactly the one implemented or specified. The gap is load-bearing: ∇_f L̂_t in (21) contains the dual multiplier μ_{ij,t}, and no separate bound on μ_t is proven, so the unprojected update can escape any fixed ball. Conversely, if the unprojected update were already bounded by R_B, the clipping would be unnecessary; if it is not, clipping invalidates the ε-accuracy lemma on which the √V ε/η terms in Lemma 4 and Theorem 2 depend. A rigorous fix is to prove an a priori bound on f̃_{t+1}, or to analyze the clipped projection with its true error bound.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper considers decentralized online learning in which each agent estimates a function in a reproducing kernel Hilbert space and agents coordinate through nonlinear proximity constraints H_ij(f_i,f_j) <= gamma_ij. The authors formulate a stochastic constrained convex program, introduce a regularized Lagrangian, and propose a decentralized stochastic primal-dual algorithm with greedy dictionary compression (KOMP), called HALK. The main theoretical claims are: Theorem 1 gives a non-asymptotic model-order bound O(alpha^{-2p}); Theorem 2 gives, under Assumptions 1-5 and with eta = T^{-1/2} and nu = zeta T^{-1/2} + Lambda alpha, an average suboptimality O(T^{-1/2}+alpha) and zero average constraint violation; Corollary 2 translates this into O(1/epsilon^2) iteration complexity and O(1/epsilon^{2p}) model complexity. The numerical section validates the method on spatio-temporal random field estimation and on temperature and salinity data from the Gulf of Mexico, including an adaptive bandwidth variant.","tokens_in":33074,"tokens_out":7361,"duration_ms":78196,"significance":"If the main theorems hold, the paper makes a meaningful contribution: it provides the first non-asymptotic characterization of dictionary size for a decentralized RKHS primal-dual method, and it claims strict feasibility on average with the same O(T^{-1/2}) suboptimality rate as unconstrained SGD. The proof structure follows a standard primal-dual template and the appendices are unusually detailed. The experiments use real oceanographic data and compare against relevant baselines, which strengthens the practical claims. However, the central result currently depends on an unanalyzed norm-ball enforcement in Assumption 5, and the model-order bound depends on the dual trajectory without a uniform bound on the dual iterates. These are load-bearing gaps rather than presentational issues, so the paper needs a substantial revision before the claims can be accepted.","major_comments":[{"comment":"The claimed enforcement of the Hilbert-ball bound in Assumption 5 is inconsistent with the KOMP approximation guarantee used in Lemmas 3 and 4. The paper states that the bound 'may be explicitly enforced by dividing the norm of the coefficient vector output from KOMP by a large constant,' but Algorithm 2 contains no such step, and the only guarantee provided by KOMP is ||f_{i,t+1} - \\tilde f_{i,t+1}||_H <= epsilon. If the unprojected KOMP output has norm larger than R_B, rescaling changes f_{i,t+1}, and the distance from the rescaled output to \\tilde f_{i,t+1} is at least ||\\tilde f_{i,t+1}|| - R_B, which need not be <= epsilon and can be large because the dual multipliers mu_{ij,t} in (21) are not a priori bounded. Since Lemma 3 (Eq. 100), Lemma 4 (Eq. 102), and the bound ||f_t - f||_H <= 2 sqrt(V) R_B in Appendix B all require the KOMP error guarantee and the ball bound to hold simultaneously, Theorem 2 does not currently cover the algorithm as specified. A rigorous fix would prove a uniform a priori bound on \\tilde f_{t+1}, or analyze the clipped projection with its exact error bound.","section":"Section IV, Assumption 5; Algorithm 2; Lemma 3 (Eq. 100); Lemma 4 (Eq. 102)"},{"comment":"The bound on the dual stochastic gradient introduces an unspecified constant K1 ('for some 0 < K1 < infinity'), which propagates into the constant K in Theorem 2 and into Gamma used to set nu = zeta T^{-1/2} + Lambda alpha. The convergence rate O(T^{-1/2}+alpha) is unaffected by the value of K1, but the feasibility claim (37) is established only for zeta >= Gamma and Lambda >= 4 V R_B; because K1 is not identified in terms of the problem data, the prescribed parameter choice is not constructive. Please either give an explicit expression for K1 in terms of the known constants (C, L_h, gamma_ij, nu, X, R_B, V, E) or explicitly state that the feasibility condition uses a sufficiently large unspecified constant and therefore is non-constructive.","section":"Appendix E, Lemma 2 (Eq. 88); Theorem 2; Corollary 2"},{"comment":"The model-order bound in Theorem 1 is M_{i,t} <= beta (R_{M_{i,t}}/alpha)^{2p}, where R_{M_{i,t}} = C + L_h E R_{i,t} and R_{i,t} = max_{j in n_i} |mu_{ij,t}| is a time-dependent function of the dual iterates. The paper presents this as a non-asymptotic bound of order O(alpha^{-2p}), but without a uniform upper bound on ||mu_t|| or on R_{i,t}, the bound is not an absolute model-complexity guarantee; it is a trajectory-dependent bound. The proof in Appendix A bounds the denominator of the packing threshold using Assumptions 2 and 3 but does not bound R_{i,t} itself. Please either prove a uniform bound on the dual iterates (which would also help resolve the Assumption 5 issue) or restate Theorem 1 as a conditional statement under an additional bounded-dual-iterates assumption.","section":"Appendix A, Theorem 1 (Eq. 35)"}],"minor_comments":[{"comment":"The symbol delta is used both for the dual-regularization parameter in (13) and for the Huber-loss parameter in (38); the text says 'we choose delta = 10^4' while the figure caption sets delta = 10^{-5}. Please rename one of the two parameters to avoid confusion.","section":"Section V-A, Eq. (38) and following paragraph"},{"comment":"The line 'while candidate dictionary is non-empty I/=empty' appears garbled, and the initialization 'f = \\tilde f' is overwritten later; please use consistent notation for the current function, dictionary, and index set.","section":"Algorithm 2"},{"comment":"The adaptive bandwidth update in (39) is not covered by the convergence analysis in Theorem 2, since the kernel is time-varying; the paper should state explicitly that this variant is heuristic and only validated experimentally.","section":"Section V-B, Eq. (39)"},{"comment":"The sentence 'the model order depends on the algorithm parameters only through the ratio alpha = eta/epsilon' contradicts the definition alpha = epsilon/eta in Theorem 1 and Corollary 2; please correct the ratio so that the prose matches the displayed equations.","section":"Paragraph after Theorem 1"},{"comment":"The entry for 'This Work' lists average constraint violation as 'zero'; this should read 'zero on average', since Theorem 2(ii) only guarantees the average violation is non-positive, not pathwise satisfaction.","section":"Table I"}],"recommendation":"major_revision","confidential_remarks":"The manuscript relies heavily on earlier works by the same authors or close collaborators, especially [15], [29], [10], [35], [44], and [45], but these are published results and the overlap is not concealed; I do not regard this as a reason to reject. The main concern is technical: the Assumption 5 enforcement gap and the uncharacterized K1 and dual-trajectory dependence are fixable in a revision, but they are load-bearing for Theorem 2. The paper's scope and level of detail fit a signal-processing or optimization journal, and the real-data experiments are a genuine strength."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The one thing you should know: this is a legitimate paper, not a desk reject, but Theorem 2 has a proof gap that needs fixing before I'd trust the rates. The concern about Assumption 5 in the stress-test note is correct.\n\nWhat is actually new: Theorem 1 gives a non-asymptotic model-order bound O(alpha^{-2p}) for multi-agent RKHS learning with proximity constraints, which prior work didn't have—earlier results were asymptotic or single-agent. Theorem 2 achieves O(T^{-1/2}+alpha) suboptimality with zero average constraint violation, matching the best vector-valued rates. The algorithmic machinery is largely assembled from the authors' own prior work—primal-dual from [15], KOMP from [29], constraint tightening from [9]—but the combination with heterogeneous proximity constraints plus the explicit dictionary bound is a real contribution. The proofs follow a standard template and are internally consistent up to the issue below.\n\nThe soft spot is load-bearing. Assumption 5 says the functions output from KOMP have Hilbert norm bounded by R_B, and the paper claims this can be enforced by dividing the coefficient vector by a large constant. That operation changes the output. If the unprojected KOMP output has norm larger than R_B, the rescaled point can be farther than epsilon from the unprojected function, and the guarantee ||f_{t+1} - f-tilde_{t+1}|| <= epsilon is exactly what Lemma 3 and the epsilon-dependent terms in Lemma 4 and Theorem 2 use. Algorithm 2 as printed has no such clipping, so the algorithm analyzed is not precisely the algorithm specified. This is fixable—prove an a priori bound on the unprojected iterates, or analyze the clipping with its true error bound—but it is not a cosmetic issue.\n\nMinor concerns: K1 in Lemma 2 is an unspecified constant, so the rates are not fully explicit; the model-order bound in Theorem 1 depends on the dual iterates R_{i,t}, so it is not a clean uniform dictionary bound; the experiments lack error bars and don't connect the parsimony constant P to alpha, but they're illustrative rather than central.\n\nMy take: this is a solid extension from a capable group, and the Assumption 5 gap is likely patchable. For someone working on decentralized kernel methods, the model-order bound and the rate improvement are worth engaging with. I'd send it to a serious referee, expecting a request for major revision. If you're in the area, cite it; if you're refereeing, accept the assignment and push on the clipping step.","headline":"Worth taking seriously, but the proof of the main theorem has a load-bearing gap around Assumption 5: the norm-clipping step is not shown to preserve the KOMP error guarantee that Lemma 3 and Theorem 2 depend on.","tokens_in":33633,"tokens_out":2067,"would_cite":true,"duration_ms":23864,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["90C15","90C25","62L20","68T05"],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper establishes that a decentralized kernel learning algorithm can match the O(T^{-1/2}) sub-optimality of unconstrained SGD while satisfying all network proximity constraints exactly on average.","keywords":["decentralized optimization","reproducing kernel Hilbert space","stochastic primal-dual method","proximity constraints","kernel orthogonal matching pursuit","online learning","constraint violation","model complexity"],"falsifier":"Run HALK on a compact domain with a Gaussian kernel and a stream whose unprojected gradients push $\\|f_{i,t}\\|_H$ above the claimed $R_B$, with the coefficient-rescaling rule active; measure $\\|f_{i,t+1}-\\tilde f_{i,t+1}\\|_H$ at every step. If the rescaling makes this exceed $\\varepsilon$ on a positive fraction of iterations, the projection-error bound behind Lemma 3 fails. Separately, set $\\nu$ below the theorem's prescribed $\\zeta T^{-1/2}+\\Lambda\\alpha$ and check whether the average constraint violation eventually becomes positive, which would contradict the claimed zero-violation regime.","tokens_in":32537,"feed_emoji":"🧮","tokens_out":9103,"duration_ms":89158,"temperature":0.7,"pith_summary":"This paper tries to establish that a network of agents, each learning a nonlinear regression function from its own data stream, can coordinate through proximity constraints without sacrificing the optimal convergence rate or an affordable model size. The proposed algorithm, Heterogeneous Adaptive Learning with Kernels (HALK), runs a functional stochastic primal-dual method on an augmented Lagrangian and compresses each agent's function with greedy kernel matching pursuit, so the number of stored kernel centers stays bounded while iterates remain near the unprojected descent path. Under convexity, Lipschitz, and boundedness assumptions, the paper claims an average expected sub-optimality of $O(T^{-1/2} + \\alpha)$ after $T$ iterations together with long-run average satisfaction of every proximity constraint, where $\\alpha=\\varepsilon/\\eta$ is the compression budget. It also supplies a non-asymptotic bound of $O(\\alpha^{-2p})$ on the dictionary size for data in $\\mathbb{R}^p$, making explicit how memory trades off against accuracy. A sympathetic reader would care because the combination of the optimal unconstrained stochastic-gradient rate with exact long-run feasibility has not previously been available for kernel-based decentralized learning.","feed_headline":"Kernel learning networks hit optimal rate, zero constraint violation","feed_subtitle":"Primal-dual compression keeps models small while network proximity constraints are exactly met on average.","key_machinery":"The engine of the algorithm is a functional Arrow-Hurwicz primal-dual step on the augmented Lagrangian $L(f,\\mu)=S(f)+\\langle\\mu,\\mathcal{H}(f)+\\nu\\mathbf{1}-\\gamma\\rangle - \\frac{\\delta\\eta}{2}\\|\\mu\\|^2$. The primal descent is taken in the RKHS: the reproducing property turns the functional gradient into $\\ell'_i(f_{i,t}(x_{i,t}),y_{i,t})\\kappa(x_{i,t},\\cdot)$ plus neighbor terms, and the dual ascent is an entry-wise projected gradient step for each edge. The $\\nu$-tightening of the constraints and the dual regularization are what convert long-run feasibility from an approximation into an exact guarantee. To keep the representation finite, each unprojected iterate is passed through destructive kernel orthogonal matching pursuit (KOMP), which greedily removes kernel centers until the Hilbert-norm error against the unprojected function would exceed $\\varepsilon$; a packing-number argument for Mercer kernels then gives the $O(\\alpha^{-2p})$ model-order bound. A generalized Representer theorem for the constrained Lagrangian justifies writing every primal minimizer as a finite kernel expansion.","core_discovery":"The central result is Theorem 2: with step-size $\\eta=T^{-1/2}$, compression tolerance $\\alpha=\\varepsilon/\\eta$, and a dual shift $\\nu=\\zeta T^{-1/2}+\\Lambda\\alpha$, the average expected sub-optimality of HALK is $O(T^{-1/2}+\\alpha)$, and for every edge the average constraint violation satisfies $\\frac{1}{T}\\sum_{t=1}^T \\mathbb{E}[H_{ij}(f_{i,t},f_{j,t})-\\gamma_{ij}]\\le 0$. Theorem 1 states that each agent's retained kernel dictionary has at most $\\beta(R_M/\\alpha)^{2p}$ elements, so fixing a memory budget $M$ places a floor of order $M^{-1/(2p)}$ on the achievable gap. Read together, the two theorems assert that the curse of kernelization can be controlled rather than merely lamented: lower $\\alpha$ for accuracy and larger dictionaries, or higher $\\alpha$ to save memory at a controlled cost. The proof is built from a per-iteration Lagrangian decrement inequality in which the KOMP projection error enters only as additive terms of order $\\varepsilon/\\eta$, and a lemma bounding the gap between the original and $\\nu$-tightened problems by $O(\\nu)$.","pith_inferences":["The adaptive-bandwidth experiments in Section V-B are outside the formal coverage of Theorem 2, since the kernel itself changes over time; an extension would treat the bandwidth update in (39) as part of the stochastic iteration and verify whether the $O(T^{-1/2})$ rate survives the added randomness.","The enforcement of Assumption 5 by rescaling KOMP's coefficient vector is not analyzed in the paper, so the most defensible reading is that Theorem 2 is proved for iterates that happen to stay in the ball, and the clipping rule is an unverified repair rather than a proof step.","Since KOMP prunes dictionaries from local observations only, a testable extension would let agents exchange or diversify their kernel centers, potentially improving constraint satisfaction per unit of memory while preserving the same overall argument.","The exact long-run feasibility result suggests a practical tuning rule for networked field estimation: set the tightening $\\nu$ just above the dominant $\\Lambda\\alpha$ term instead of hand-tuning penalty coefficients."],"forward_implications":["The decentralized kernel learning algorithm reaches the same $O(T^{-1/2})$ average sub-optimality as unconstrained stochastic gradient methods and, at the same time, satisfies network proximity constraints exactly on average over the run.","The memory-accuracy trade-off is governed by a single ratio $\\alpha=\\varepsilon/\\eta$: to reduce the optimality floor by a factor of two, the allowed dictionary size must grow by a factor of order $2^{2p}$ in the data dimension.","The average feasibility guarantee holds for every edge regardless of the compression budget, so the method does not require re-tuning the tightening parameter when the dictionary is resized.","The coverage extends to non-differentiable convex losses through subgradients, so hinge-type losses are admissible without changing the rates."],"supporting_citations":[{"why":"Supplies the projected stochastic primal-dual method for constrained online learning with kernels and the generalized Representer theorem for Lagrangians that HALK builds on.","marker":"[15]"},{"why":"Provides the parsimonious sparse projection framework and matching-pursuit model-order control that HALK adapts to the multi-agent setting.","marker":"[29]"},{"why":"Defines the proximity-without-consensus formulation for online multiagent optimization whose linear-model rates HALK extends to the RKHS.","marker":"[10]"},{"why":"Gives the kernelized consensus method with penalty-based constraint handling that HALK is compared against and improves upon in feasibility.","marker":"[35]"},{"why":"Establishes the long-term constraint regret framework and the rates for linear models that Theorem 2's O(T^{-1/2}) and zero-violation result refines.","marker":"[9]"},{"why":"Defines kernel matching pursuit, the compression primitive used in the destructive KOMP step of Algorithm 2.","marker":"[42]"},{"why":"Supplies the packing-number bound for dictionaries of Lipschitz Mercer kernels that Theorem 1's model-order bound relies on.","marker":"[46]"}],"fun_headline_variants":["Decentralized kernel learning achieves optimal rate and exact constraints","HALK achieves O(1/√T) rate with zero constraint violation","Kernel learning in heterogeneous networks: optimal rate, no constraint gap","Adaptive kernel learning: optimal rate and exact constraint satisfaction","HALK decentralizes kernel learning with optimal rate and constraint compliance"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The proof rests on Assumption 5: every compressed iterate and every optimum must lie in the same Hilbert ball of radius $R_B$, and the paper's suggested enforcement—dividing the KOMP coefficient vector by a large constant—is never analyzed; if that rescaling breaks the guarantee $\\|f_{t+1}-\\tilde f_{t+1}\\|_H\\le\\varepsilon$, the optimality and feasibility bounds no longer follow.","fun_headline_variants_meta":{"raw":{"variants":["Decentralized kernel learning achieves optimal rate and exact constraints","HALK achieves O(1/√T) rate with zero constraint violation","Kernel learning in heterogeneous networks: optimal rate, no constraint gap","Adaptive kernel learning: optimal rate and exact constraint satisfaction","HALK decentralizes kernel learning with optimal rate and constraint compliance"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000801,"raw_usage":{"total_tokens":3540,"prompt_tokens":985,"completion_tokens":2555,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":601,"completion_tokens_details":{"reasoning_tokens":2467}},"tokens_in":601,"tokens_out":2555,"duration_ms":19331,"temperature":1.0,"reasoning_tokens":2467,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-14T15:50:40.200991+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run HALK on a compact domain with a Gaussian kernel and a stream whose unprojected gradients push $\\|f_{i,t}\\|_H$ above the claimed $R_B$, with the coefficient-rescaling rule active; measure $\\|f_{i,t+1}-\\tilde f_{i,t+1}\\|_H$ at every step. If the rescaling makes this exceed $\\varepsilon$ on a positive fraction of iterations, the projection-error bound behind Lemma 3 fails. Separately, set $\\nu$ below the theorem's prescribed $\\zeta T^{-1/2}+\\Lambda\\alpha$ and check whether the average constraint violation eventually becomes positive, which would contradict the claimed zero-violation regime.","supporting_citations":[{"cited_title":"Projected st ochastic primal-dual method for constrained online learning with ke rnels,","cited_arxiv_id":null,"evidence_quote":"Supplies the projected stochastic primal-dual method for constrained online learning with kernels and the generalized Representer theorem for Lagrangians that HALK builds on."},{"cited_title":"Parsim onious online learning with kernels via sparse projections in function sp ace,","cited_arxiv_id":null,"evidence_quote":"Provides the parsimonious sparse projection framework and matching-pursuit model-order control that HALK adapts to the multi-agent setting."},{"cited_title":"Proximity with out consensus in online multiagent optimization,","cited_arxiv_id":null,"evidence_quote":"Defines the proximity-without-consensus formulation for online multiagent optimization whose linear-model rates HALK extends to the RKHS."},{"cited_title":"De centralized online learning with kernels,","cited_arxiv_id":null,"evidence_quote":"Gives the kernelized consensus method with penalty-based constraint handling that HALK is compared against and improves upon in feasibility."},{"cited_title":"Trading regret for effici ency: online convex optimization with long term constraints,","cited_arxiv_id":null,"evidence_quote":"Establishes the long-term constraint regret framework and the rates for linear models that Theorem 2's O(T^{-1/2}) and zero-violation result refines."},{"cited_title":"Kernel matching pursuit,","cited_arxiv_id":null,"evidence_quote":"Defines kernel matching pursuit, the compression primitive used in the destructive KOMP step of Algorithm 2."},{"cited_title":"The kernel recursive l east-squares algorithm,","cited_arxiv_id":null,"evidence_quote":"Supplies the packing-number bound for dictionaries of Lipschitz Mercer kernels that Theorem 1's model-order bound relies on."}],"review_version":1}