{"id":"f40f7b6d-49a3-4b54-a06c-02ea70e825cc","arxiv_id":"2606.12024","paper_version":1,"verdict":"UNVERDICTED","confidence":"UNKNOWN","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Develops continuous and discrete antenna position optimization algorithms for movable antennas, including learning-based methods requiring less than full point-wise CSI.","lead":"The paper proposes general algorithms for optimizing positions of movable antennas in wireless systems, split into continuous methods for large-scale gains and discrete methods using sampled channel measurements. It also introduces learning-based variants to cut down on the need for complete channel state information at every point.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's UNVERDICTED verdict and weakest-assumption note are a direct consequence of the missing full text. With no manuscript content available for inspection, no independent load-bearing concern can be formulated or tested; the assessment therefore remains unchanged.","tokens_in":1742,"tokens_out":224,"duration_ms":9744,"concrete_test":"Obtain and read the full manuscript text; if the discrete APO section (§ on discretization and point-wise CSI selection) and learning-based section contain explicit algorithms and results, re-apply the same scrutiny to those sections.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The provided materials consist solely of the abstract; the full manuscript (including algorithm derivations, channel model assumptions, discretization details, learning-based method architectures, and numerical validation) is referenced as available via tool but not supplied. No concrete technical element of the central claim—general continuous/discrete APO without analytical models, or learning-based APO without full point-wise CSI—can therefore be examined for internal inconsistency or unsupported assumptions.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims to introduce general and practical antenna position optimization (APO) algorithms for movable antennas (MAs) that avoid reliance on simplified analytical channel models. It categorizes the methods into continuous APO (for large-scale array signal processing) and discrete APO (for small-scale multi-path reshaping via discretization of the movement region and selection based on point-wise CSI), plus learning-based variants that operate without full point-wise CSI acquisition. The work concludes by comparing application scenarios and validating effectiveness via numerical results.","tokens_in":1787,"tokens_out":364,"duration_ms":10084,"significance":"If the claimed generality and performance hold, the algorithms could meaningfully advance MA systems by enabling optimization in realistic settings without tractable analytical models, addressing a noted limitation of prior work. The learning-based approaches, if they achieve comparable results with reduced overhead, would be particularly impactful for practical deployment.","major_comments":[],"minor_comments":[{"comment":"The abstract refers to 'numerical results' validating the algorithms but provides no details on the simulation setup, baselines, or performance metrics; this should be expanded in the main text with specific figures or tables.","section":null},{"comment":"The distinction between continuous and discrete APO application scenarios (large-scale vs. small-scale) is stated but would benefit from clearer delineation of the movement region sizes or array configurations assumed.","section":null}],"recommendation":"uncertain","confidential_remarks":"Review materials supplied only the abstract; the full manuscript (algorithms, derivations, channel models, discretization details, learning architectures, and validation data) was referenced as available via tool but not included. This prevents any technical verification of the central claims, leading to the 'uncertain' recommendation. If the full text is provided, a standard review can proceed."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their careful summary of our work on general and practical antenna position optimization algorithms for movable antennas. We appreciate the acknowledgment of the potential significance if the claimed generality holds, particularly for the learning-based methods. Since the recommendation is listed as uncertain but no specific major comments are provided, we are prepared to clarify any aspects of the continuous APO, discrete APO, or learning-based approaches upon request.","responses":[],"tokens_in":1212,"tokens_out":100,"duration_ms":7636,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that they split APO into continuous versions for boosting large-scale performance and discrete versions that pick positions from sampled CSI points, then add learning-based shortcuts that skip full point-wise measurements. This targets the real issue that prior MA work leaned on simplified channel models.\n\nThey handle the modeling gap reasonably by shifting to measurement-driven discrete search and by flagging the CSI overhead problem. The categorization of use cases (large-scale array processing versus small-scale multipath reshaping) is a clear way to organize the methods.\n\nThe soft spots are bigger than minor. The abstract asserts the algorithms are more general and effective yet supplies zero equations, no description of the discrete search procedure, no architecture for the learning methods, and no numbers from the promised numerical results. That leaves the central claims uncheckable: whether discrete APO actually finds good positions from point-wise data alone, and whether the learning versions come close without the full CSI set. The weakest assumption is that these shortcuts preserve performance in practice.\n\nThis is for wireless researchers already working on movable antennas who need implementation ideas rather than first-principles theory. A reader in that niche could extract the high-level workflow and the CSI-reduction angle, but would still need the full derivations and plots to judge utility.\n\nIt should go to peer review because the direction is practical and the modeling limitation it attacks is genuine, even though the current write-up is too thin on evidence to stand on its own.","headline":"The paper gives continuous and discrete APO algorithms for movable antennas that work from point-wise CSI without analytical models, plus learning variants to cut measurement overhead, but the abstract shows no actual derivations or quantified gains.","tokens_in":2293,"tokens_out":378,"would_cite":false,"duration_ms":18700,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Movable antenna positions can be optimized using continuous and discrete algorithms based solely on point-wise channel measurements without analytical models.","keywords":["movable antenna","antenna position optimization","continuous APO","discrete APO","learning-based optimization","point-wise CSI","multi-path channel reshaping","wireless communications"],"falsifier":"A side-by-side test in which the throughput or error rate achieved by the discrete or learning-based positions is compared against positions found by exhaustive search over a finer grid or by an analytical model in the same physical multi-path setup.","tokens_in":2633,"feed_emoji":"📡","tokens_out":676,"duration_ms":13689,"temperature":0.7,"pith_summary":"The paper seeks to overcome the main barrier to movable antennas by developing general antenna position optimization methods that do not depend on simplified theoretical channel models. It splits the task into continuous APO, suited to large-scale array processing, and discrete APO, which reshapes small-scale multi-path channels by sampling the movement region and choosing positions from point-wise CSI. Learning-based variants are added to lower the cost of collecting full CSI. A reader would care because these steps turn the theoretical promise of movable antennas into workable techniques for real wireless systems where exact models are unavailable. If the methods hold, they widen the range of environments where local antenna movement can deliver measurable gains.","feed_headline":"Movable antennas positioned from point-wise CSI alone","feed_subtitle":"Continuous and discrete algorithms select locations without analytical channel models or complete measurements","key_machinery":"The discrete APO procedure that divides the confined movement region into discrete sampling points and applies discrete selection algorithms directly on point-wise CSI measurements.","core_discovery":"The authors present more general and effective APO algorithms categorized as continuous APO for flexible array signal processing to boost large-scale communication performance and discrete APO for small-scale multi-path channel reshaping. The discrete approach discretizes the antenna movement region into sampling points and employs discrete algorithms to determine the optimal MA positions based on the point-wise CSI without requiring an analytical channel model. They also introduce efficient learning-based APO algorithms that operate without requiring full point-wise CSI to reduce acquisition overhead, and they compare application scenarios while validating effectiveness numerically.","pith_inferences":["The sampling-plus-learning pattern could be reused for other reconfigurable antenna hardware that lacks closed-form channel expressions.","Real-time position adjustment might become feasible if the learning models are updated incrementally from ongoing partial measurements.","The approach may extend naturally to joint optimization of both position and other parameters such as beamforming weights when full CSI remains costly."],"forward_implications":["Continuous APO supports flexible array signal processing that improves large-scale communication metrics.","Discrete APO enables targeted reshaping of small-scale multi-path channels using only sampled CSI.","Learning-based variants cut the CSI acquisition overhead while retaining comparable performance.","The two categories address distinct scales and can be chosen according to the application scenario."],"fun_headline_variants":["Movable antennas positioned from point-wise CSI samples","Discrete APO for MA positions without channel models","Learning-based APO reduces CSI acquisition for MAs","Continuous APO for flexible MA array processing","Point sampling enables MA position optimization from CSI"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"That optimal or near-optimal positions can be reliably identified from a finite set of point-wise CSI samples alone and that learning-based methods can match performance when the full set of measurements is unavailable.","fun_headline_variants_meta":{"raw":{"variants":["Movable antennas positioned from point-wise CSI samples","Discrete APO for MA positions without channel models","Learning-based APO reduces CSI acquisition for MAs","Continuous APO for flexible MA array processing","Point sampling enables MA position optimization from CSI"]},"model":"grok-4.3","cost_usd":0.00574,"raw_usage":{"total_tokens":2748,"prompt_tokens":689,"num_sources_used":0,"completion_tokens":57,"cost_in_usd_ticks":57399500,"prompt_tokens_details":{"text_tokens":689,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2002,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":689,"tokens_out":57,"duration_ms":12218,"temperature":1.0,"reasoning_tokens":2002,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T08:47:52.883909+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A side-by-side test in which the throughput or error rate achieved by the discrete or learning-based positions is compared against positions found by exhaustive search over a finer grid or by an analytical model in the same physical multi-path setup.","supporting_citations":[],"review_version":1}