{"id":"0c9e8d78-8040-47a4-afc7-4f67400e56de","arxiv_id":"2607.04889","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Deep-unfolded alternating optimization for hybrid RIS mode selection and binary phases yields ~30% higher energy efficiency than plain projected gradient and most of the gain with only ~10% of elements active.","lead":"A hybrid reconfigurable intelligent surface that turns only some elements into amplifiers can raise energy efficiency of multiuser wireless links by roughly 10% over all-active and up to 3× over all-passive designs. The authors train a few projected-gradient steps as a small network so the surface chooses which elements to activate and how to set binary phases faster than classical optimizers.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"The abstract’s quantitative EE gains rest on designs optimized for the surrogate fEE (17) that drops active-RIS noise from every SINR and replaces true Pr by P_r^max; those gains can be inflated when the low-power assumption loosens.","rationale":"The reader correctly isolates the fEE surrogate as the single most load-bearing assumption behind the strongest claim. All other caveats (perfect CSI, ZF restriction, simulation-only validation, lack of code) are real but secondary: they affect absolute performance levels, whereas the surrogate can systematically bias the very relative gains that the abstract advertises. Because the paper already supplies a partial check (Fig. 4) and the absolute gaps remain small in the nominal geometry, the concern does not rise to a rejection; it simply confirms that the existing CONDITIONAL verdict is the right one. No stronger internal inconsistency or circularity is present. The concrete test above would settle whether the bias is material; until it is run, the quantitative claims should be read as holding under the low-power regime for which the surrogate was derived.","tokens_in":21909,"tokens_out":654,"duration_ms":28210,"concrete_test":"Re-run the AO-DU / fully-active / fully-passive comparisons of Figs. 5 and 7 on the stronger RIS–user channel already used in Fig. 4 (path-loss reduced by 10 dB), evaluating every design under the exact EE (16). If the hybrid-versus-fully-active advantage falls below ≈5 % or the hybrid-versus-passive factor drops materially below 2×, the abstract’s quantitative claims do not hold outside the paper’s low-power geometry.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claims (≈30 % over AO-PGD, ≥10 % over fully-active, up to 3× over fully-passive, and “most gain from Na≈10 %”) are numerical values of the true EE (16) attained by solutions of the surrogate problem (18). Under the ZF reformulation the authors drop σ_R²‖h_r,k^H Φ_a‖² from every SINR and replace the state-dependent Pr(Φ_a,P) by its hard ceiling P_r^max in the denominator (§IV-A, eqs. 16–17). Both omissions become optimistic precisely when more elements are active or when the RIS–user path loss is milder: the ignored noise term grows with |A| and with the amplitudes, so the approximation error is systematically larger for the fully-active baseline than for a sparse hybrid design. Consequently the reported hybrid-versus-active gap (and, to a lesser extent, the DU-versus-PGD gap) can be inflated by the surrogate. Fig. 4 shows the absolute gap remains modest in the paper’s geometry, yet the relative ranking claims are never re-evaluated under the stronger channel or under a true-EE optimizer, leaving the headline numbers dependent on the low-power regime in which the surrogate was justified.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The paper studies energy-efficiency (EE) maximization for a hybrid RIS-assisted MU-MISO downlink in which each RIS element can be switched between active (amplified) and passive modes. The design jointly optimizes BS beamforming, the active-element set, amplification gains, and binary phase shifts under BS/RIS power budgets, amplifier limits, amplification noise, and per-user SE QoS. An alternating-optimization (AO) framework is proposed: ZF beamforming with closed-form Dinkelbach power allocation for the BS subproblem, and a model-driven deep-unfolded projected gradient method (with differentiable relaxations for binary phases and Top-Na selection) for the RIS subproblem. A joint PGA baseline with barrier QoS penalties is also derived. Simulations under perfect CSI, blocked direct links, and Rician channels report faster convergence and higher EE than AO-PGD and PGA, roughly 30% EE gain over non-unfolded AO, at least 10% over fully-active RIS, up to threefold over fully-passive RIS, and that most of the gain is obtained with only a small active fraction (e.g., Na=10 of N=100) and a small share of the dynamic power budget allocated to the RIS.","tokens_in":22270,"tokens_out":1605,"duration_ms":12881,"significance":"If the numerical claims hold under the stated model, the work supplies a practical algorithmic route to hybrid-RIS EE design that jointly treats discrete mode selection and binary phases—constraints that conventional SCA/AO treatments often leave fixed or continuous. The explicit incorporation of amplifier noise, bias power, and power-budget sharing, together with the observation that sparse activation already captures most of the EE gain, is useful for hardware-aware RIS system design. Strengths include closed-form KKT power allocation (Algorithm 1), carefully derived gradients (Lemma 1 / Appendix A), and a transparent deep-unfolding construction that preserves the projected-gradient structure while learning only step sizes. The contribution is primarily algorithmic and empirical rather than theoretical optimality; its value therefore rests on the reliability of the reported EE rankings under the modeling approximations used for tractability.","major_comments":[{"comment":"§IV-A, eqs. (16)–(18) and the abstract claims: the designs are obtained by maximizing the surrogate fEE that (i) drops the active-RIS noise term σ_R²‖h_r,k^H Φ_a‖² from every SINR and (ii) replaces the state-dependent Pr(Φ_a,P) by its hard ceiling P_r^max in the EE denominator. Fig. 4 shows that the absolute gap between fEE and true EE (16) is modest in the paper’s geometry, yet the headline relative gains (≈30% vs AO-PGD, ≥10% vs fully-active, up to 3× vs fully-passive, and “most gain from Na≈10%”) are never re-evaluated under a true-EE objective or under the stronger RIS–user channel already considered in Fig. 4. Because the omitted noise term grows with |A| and with the amplitudes, the approximation error is systematically larger for the fully-active baseline than for a sparse hybrid design; consequently the hybrid-versus-active ranking (and, to a lesser extent, the DU-versus-PGD gap)","section":null},{"comment":"§VI and Figs. 5–7: all quantitative claims rest on Monte Carlo simulations under perfect CSI, blocked direct links, ZF, fixed Rician factors, and a single geometry (BS–RIS–user cluster). No sensitivity study is provided for imperfect CSI, residual multiuser interference under non-ZF beamforming, or non-blocked direct links—settings that are standard stress tests for RIS EE papers and that can change both the absolute EE and the hybrid-versus-active ranking. While perfect CSI is stated as an assumption, the abstract presents the percentage gains without this caveat; at minimum the manuscript should quantify degradation under realistic CSI error or justify why the ranking is expected to be robust.","section":null},{"comment":"§IV-B2 and Algorithm 2: the deep-unfolded PGD uses a straight-through estimator for Top-Na selection and a tanh relaxation for binary phases, with only the step sizes trained. The training protocol (400 channels, 100 epochs, J=5, Adam lr=0.01) is reported, but there is no ablation on the number of layers J, the sharpness β, or generalization to different (N,Na,P_r^max) than those used in training. Because the abstract’s “30% higher EE than the procedure without deep unfolding” is the central algorithmic claim, a short ablation confirming that the gain is not an artifact of a poorly tuned fixed-step AO-PGD baseline (or of a particular J) is needed for the claim to be load-bearing.","section":null}],"minor_comments":[{"comment":"Fig. 3 caption and text: the saw-tooth behavior of AO-DU/AO-PGD is attributed to alternating Dinkelbach re-solves; a short remark that the plotted EE is the true EE (16) evaluated after each outer iteration would remove ambiguity.","section":null},{"comment":"Notation: the indicator vector is rendered as “/x31A_N” in several places (e.g., around (3)); this appears to be a LaTeX encoding artifact and should be corrected to the standard 1_A notation.","section":null},{"comment":"§III-B1, power model: the claim that user power is omitted “similarly to [35]” is fine, but a one-sentence note that adding a constant user-circuit term would not change the optimizers would help readers who include it by default.","section":null},{"comment":"Related work (§II): several recent hybrid-RIS EE papers that already optimize mode switching (e.g., [12], [14], [15]) are cited; a clearer one-sentence contrast of what is new (joint binary-phase + Top-Na deep unfolding under the exact power model) would sharpen the novelty statement.","section":null},{"comment":"Algorithm 3 complexity: the O(S J K N²) claim is reasonable for large N, but a brief wall-clock comparison (or FLOPs count) against AO-PGD and PGA on the same hardware would make the “faster convergence” claim more concrete.","section":null},{"comment":"Typos / polish: “efficiency” consistently uses a special f ligature that may break search; “amplified” vs “amplification” wording around (8)–(9) is slightly inconsistent; “nearly fourfold improvement over the PGA-based method” in the contributions list is stronger than the abstract’s wording and should be aligned with the actual figure values.","section":null}],"recommendation":"major_revision","confidential_remarks":"The core algorithmic construction is competent and the hybrid-RIS EE message is timely. The main risk is that the abstract’s quantitative rankings are optimized under a surrogate whose bias favors sparse hybrid designs; if the authors can re-rank under true EE (16) even on a subset of channels, the paper becomes a solid minor-revision candidate. Scope is appropriate for a wireless-communications / signal-processing journal; novelty relative to the authors’ own conference version [1] and concurrent hybrid-RIS EE works should be checked by the editor for incrementalism, but the deep-unfolding treatment of joint mode selection and binary phases is a legitimate differentiator."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The useful takeaway is straightforward: a hybrid RIS with only a small active subset plus a deep-unfolded projected-gradient module for the combinatorial RIS subproblem beats both fully passive and fully active designs on EE under the paper’s power model, and the unrolling itself gives a clear speed/quality lift over plain PGD inside the same AO loop.\n\nWhat is actually new is the combination, not any single block. Hybrid RIS, EE via AO/Dinkelbach, ZF power allocation, and deep unfolding of gradient steps are all established (they cite the right lineage). The increment is unrolling PGD with differentiable surrogates for binary phases (tanh) and Top-Na active-set selection (STE), then embedding that inside an EE AO that also respects amplifier gain limits, RIS power, and 1-bit phases. Gradients and KKT power allocation are derived carefully; the algorithm description is reproducible enough to re-implement. Simulations are clean: hybrid wins, Na≈10 of 100 already captures most of the EE, and only a small share of the dynamic budget needs to go to the RIS. That design insight is the part worth keeping.\n\nThe soft spot that matters is the surrogate fEE: they drop active-RIS noise from every SINR and replace true Pr by its budget ceiling. Fig. 4 shows the absolute gap is modest in their geometry, so the ranking (hybrid > active > passive, DU > PGD) is unlikely to reverse, but the headline percentages (30 %, 10 %, 3×) are numbers of true EE attained by solutions of the surrogate. Under milder path loss or heavier RIS power use the gap would grow, and they never re-optimize under true EE. Perfect CSI, blocked direct links, and ZF are standard but idealized; no code or error bars. These are ordinary limitations for this venue class, not load-bearing cracks.\n\nThis is for people who already work on hybrid RIS or model-driven unrolling for discrete RIS constraints. It is not a foundational result, but it is a competent, honest methods contribution with a practical message. I would send it to peer review; a referee can push on the surrogate and imperfect CSI without the paper collapsing. Worth a look if you are in the area; not mandatory reading otherwise.","headline":"Solid hybrid-RIS EE methods paper: deep-unfolded PGD for joint active-set + 1-bit phases is the real increment, and the hybrid-vs-active/passive numbers are useful even if the surrogate softens the absolute claims.","tokens_in":22940,"tokens_out":579,"would_cite":true,"duration_ms":8695,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"A hybrid RIS with only a few active elements, optimized by deep-unfolded alternating optimization, yields substantially higher energy efficiency than fully passive or fully active surfaces.","keywords":["hybrid RIS","energy efficiency","deep unfolding","alternating optimization","active-element selection","binary phase shifts","MU-MISO downlink"],"falsifier":"Re-optimize the same system geometries with the exact energy-efficiency expression that keeps active-RIS noise and the true dynamic amplifier power; if the reported 30 percent, 10 percent, and threefold gains largely disappear or reverse under the exact metric, the central claim fails.","tokens_in":22752,"feed_emoji":"📡","tokens_out":947,"duration_ms":7227,"temperature":0.7,"pith_summary":"This paper shows that a hybrid reconfigurable intelligent surface—where each element can be switched between amplifying (active) and purely reflecting (passive) modes—can deliver markedly higher energy efficiency than either a fully passive or a fully active surface in a multi-user downlink. The design jointly chooses which elements to activate, their amplification gains, their binary phases, and the base-station beamforming under realistic power budgets, amplifier limits, and noise. The authors solve the resulting mixed-integer problem by alternating zero-forcing power allocation at the base station with a model-driven deep-unfolding network that learns step sizes for the RIS subproblem. Simulations indicate roughly 30 percent higher energy efficiency than the same alternating procedure without unfolding, at least 10 percent better than a fully active RIS, and up to three times better than a fully passive RIS; most of the gain appears once only about 10 percent of the elements are active and only a small fraction of the dynamic power is given to the surface.","feed_headline":"Hybrid RIS beats fully active or passive on energy efficiency","feed_subtitle":"Activating only ~10% of elements and a small power share captures most of the gain","key_machinery":"Deep-unfolded projected gradient descent for the RIS subproblem: the iterations of projected gradient ascent are unrolled into trainable layers whose step sizes are learned, while binary phases and discrete active-set selection are handled by differentiable surrogates (tanh phase map and straight-through top-Na selection).","core_discovery":"Under practical hardware constraints, jointly optimizing active/passive mode selection together with beamforming and reflection coefficients via deep-unfolded alternating optimization yields energy-efficiency gains of about 30 percent over plain projected-gradient alternating optimization, at least 10 percent over a fully active RIS, and up to threefold over a fully passive RIS, with most of the gain captured by activating only a small fraction of elements and allocating only a small share of dynamic power to the RIS.","pith_inferences":["The same deep-unfolding template could be applied to multi-RIS or multi-cell settings where the combinatorial active-set decisions become even larger.","If real hardware measurements confirm that active-element noise and unused amplifier power remain small, the hybrid architecture becomes a practical low-cost upgrade path for existing passive RIS deployments.","The observed saturation of energy efficiency with active-element count suggests a natural design rule: provision only enough amplifiers to reach the knee of the curve rather than maximising amplification hardware."],"forward_implications":["Hybrid RIS hardware with only a modest number of amplifiers can outperform both fully passive and fully active surfaces on energy efficiency.","Most of the energy-efficiency benefit is already obtained once roughly 10 percent of the elements are active.","Allocating only a small fraction of the total dynamic power budget to the RIS is typically more energy-efficient than giving it a large share.","Learning the step sizes of a classical projected-gradient RIS update via deep unfolding accelerates convergence and improves the final energy efficiency relative to hand-tuned or fixed-step projected gradient.","Binary (one-bit) phase control remains compatible with substantial energy-efficiency gains when the active-set selection is optimized jointly."],"fun_headline_variants":["Hybrid RIS deep-unfolding yields 30% EE gain over plain AO","Activate few RIS elements for most EE vs full active/passive","Deep-unfolded hybrid RIS beats full active and passive on EE","Small active RIS share captures bulk of hybrid energy-efficiency gains","Hybrid RIS mode optimization: 10%+ EE edge over fully active"],"cache_read_input_tokens":16512,"weakest_assumption_plain":"The optimization uses a simplified energy-efficiency formula that drops the active-element noise from every user's signal quality and pretends the RIS always burns its entire power budget; if those terms are not small, the design optimized for the surrogate can diverge from true energy efficiency.","fun_headline_variants_meta":{"raw":{"variants":["Hybrid RIS deep-unfolding yields 30% EE gain over plain AO","Activate few RIS elements for most EE vs full active/passive","Deep-unfolded hybrid RIS beats full active and passive on EE","Small active RIS share captures bulk of hybrid energy-efficiency gains","Hybrid RIS mode optimization: 10%+ EE edge over fully active"]},"model":"grok-4.5","effort":"low","cost_usd":0.0035,"raw_usage":{"total_tokens":1176,"prompt_tokens":793,"num_sources_used":0,"completion_tokens":75,"cost_in_usd_ticks":35000000,"prompt_tokens_details":{"text_tokens":793,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":308,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":793,"tokens_out":75,"duration_ms":3118,"temperature":1.0,"reasoning_tokens":308,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-11T11:53:12.290803+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Re-optimize the same system geometries with the exact energy-efficiency expression that keeps active-RIS noise and the true dynamic amplifier power; if the reported 30 percent, 10 percent, and threefold gains largely disappear or reverse under the exact metric, the central claim fails.","supporting_citations":[],"review_version":1}