{"id":"0c60358c-acc2-442d-8991-67f836ba89e2","arxiv_id":"2607.08116","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.5,"correctness_risk":"medium","formal_verification":"none","parameter_count":6,"one_line_summary":"A device–server split of recurrent latent LLM reasoning plus semantic MoE-SAC scheduling yields about 18% higher simulated system throughput than plain SAC under energy, recurrence, and latency budgets.","lead":"MORES splits latent LLM reasoning between phones and edge servers over wireless links, then uses a task-aware MoE reinforcement learner to pick how deep to reason and how much to prune before sending. If the gains hold outside simulation, edge devices could get stronger on-demand reasoning without shipping full models or long chain-of-thought text.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"The 18% throughput claim rests on offline accuracy curves that may not match the online δ_k produced by the actual recurrent model under the same pruning and channel noise.","rationale":"The reader correctly isolates the pruning-fidelity assumption as the weakest link. I sharpen it only by tying it directly to the definition of U and to the offline-to-online gap: the 18% number is not an independent measurement of end-to-end correctness under the wireless model; it is the output of an RL loop whose reward is supplied by pre-computed accuracy tables. No mathematical contradiction appears in (P1) or the SAC/MoE construction, the code link is provided, and the systems contribution (latent split + semantic MoE routing) remains intact. Therefore the verdict stays CONDITIONAL; the concrete re-execution test above is the minimal check that would either validate or deflate the headline gain. No stronger objection (e.g., internal inconsistency of the MDP or impossibility of the partition) is warranted from the text.","tokens_in":20995,"tokens_out":675,"duration_ms":6876,"concrete_test":"Freeze the trained MoE-SAC and SAC policies. For each of the K=100 requests in the evaluation episodes that produced Figs. 10–12, re-execute the actual recurrent model (same architecture used for Figs. 7–9) with the chosen (r_k,ρ_k), applying the exact pruned/quantized uplink features of Eq. 11 and the realized channel. Recompute U from the true δ_k. If the MoE-SAC advantage falls below ~5% (or disappears), the 18% claim does not survive online latent fidelity.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim is that semantic MoE-SAC raises average throughput U by ~18% vs SAC (e.g., U≈33 vs 28 at E_dev_max=80, G_max=3e5) by jointly choosing r_k and ρ_k. U is defined as the fraction of correct answers (Eq. 15), so every reported gain is only as reliable as the mapping from (r,ρ,channel) to δ_k. That mapping is taken from offline accuracy-vs-pruning/recurrence curves on GSM8K/MBPP/HellaSwag (Figs. 7–9) and then used inside the DRL environment. The paper never states that the same recurrent-depth backbone is re-run end-to-end for every online decision under the exact uplink pruning (Eq. 11), quantization q=16, and channel noise of the wireless model (§3.4). If the offline curves overstate residual semantic fidelity after pruning, the scheduler is optimizing a surrogate that systematically over-values high-ρ / low-r actions; the measured 18% then becomes an artifact of the simulator rather than a property of the real latent service. This is the single point on which the strongest claim most depends and is least secured by the manuscript.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The paper proposes MORES, a device–edge cooperative framework for implicit (latent) LLM reasoning over wireless networks. Latent recurrent updates are partitioned so devices run a prelude encoder, a fixed number of local recurrent steps re, and a coda decoder, while servers continue remaining recurrent steps; intermediate latent features may be uplink-pruned at rate ρ. The authors formulate a joint scheduling problem (P1) that maximizes average answer correctness U under device energy, server recurrence budget, and per-token TBT latency constraints, with decision variables (rk, ρk). They solve it with a semantic MoE-augmented SAC agent whose experts are deterministically gated by task type (via a semantic router) and scenario, and report roughly 18% higher U than plain SAC (e.g., U≈33 vs 28 at E_dev_max=80, G_max=3×10^5) on a composite of GSM8K, MBPP, and HellaSwag under a simulated wireless model.","tokens_in":21438,"tokens_out":1570,"duration_ms":23650,"significance":"If the evaluation of δk under pruning and wireless transfer is faithful, the work is a solid systems contribution at the intersection of inference-time scaling and edge–cloud wireless inference. Partitioning recursive latent reasoning (rather than explicit CoT tokens) is a natural fit for communication-efficient cooperative inference, and the joint (r, ρ) control problem is well motivated by the measured task heterogeneity in Figs. 6–9. The semantic MoE-SAC design, code release, and systematic sweeps over energy, recurrence budget, and re are concrete strengths. The significance is primarily systems/algorithmic rather than a new learning-theoretic result; it would be of interest to the wireless/edge-AI community provided the simulator-to-model fidelity gap is closed.","major_comments":[{"comment":"The central 18% throughput claim (Figs. 10–12; abstract) rests on U = (1/K) Σ δk (Eq. 15) with reward ut = δt. Figs. 7–9 report offline accuracy vs (r, ρ) on GSM8K/MBPP/HellaSwag, but §5 never states whether the DRL environment obtains δk by re-running the same recurrent-depth backbone end-to-end on the actually pruned, quantized (q=16) uplink features under the wireless model of Eqs. (11)–(14), or by table lookup from those offline curves. If the latter, the agent optimizes a surrogate that may systematically over-value high-ρ / low-r actions; the reported gain is then a property of the simulator, not of the latent service. Please clarify the online δk pipeline and, if lookup is used, re-evaluate with true pruned-latent inference (or ablate the gap).","section":null},{"comment":"MDP state (Eq. 20) is st = {t, G_res,t, E_dev_res,t} and §3.4 states that channel gain hk is unknown to device and server. Yet the MoE experts and Fig. 13 are conditioned on task–channel scenarios (poor/fair/good), and the semantic router (§4.2) injects task type. How channel (and task) enter the policy at decision time is not specified in the MDP; if they are side information to the router/gating only, the formulation should say so and justify that this does not contradict the “unknown channel” assumption. Without this, expert specialization in Fig. 13 is hard to reconcile with the stated observation model.","section":null},{"comment":"Uplink pruning (§3.4, Eq. 11) is defined only as a scalar fraction ρk of “latent information” removed before transmission. For server-side continuation zi = R(e, zi−1) (Eq. 2 / Alg. 1) to remain well-defined, the paper must specify which coordinates are dropped (structured vs random), how the server reconstructs or masks the incomplete (e, z_re), and whether the offline accuracy surfaces in Figs. 7–9 use that same operator. Without a concrete pruning operator tied to the recurrent unit, the accuracy–cost trade-off that (P1) and the MoE agent exploit is under-specified.","section":null},{"comment":"Baselines in §5 are limited to conventional SAC. For a joint discrete (r, ρ) scheduling claim, at least one non-RL policy (fixed (r, ρ), task-conditional greedy from Figs. 7–8, or myopic energy/latency heuristic) is needed to show that the 18% is not merely “SAC vs weaker SAC.” Adding these would substantially strengthen the load-bearing experimental claim.","section":null}],"minor_comments":[{"comment":"“Throughput” U is average correctness, not requests per unit time. The term is defined in Eq. (15) but remains easy to misread in the abstract and §5; consider “average success rate” or “correctness throughput” in titles of Figs. 10–12.","section":null},{"comment":"Action grids (§5.1): r ∈ {8,…,32}, ρ ∈ {0,…,8%} (and offline curves go to 15%). State that the DRL action set is discrete and whether continuous SAC is projected onto this grid.","section":null},{"comment":"Complexity analysis (Eqs. 27–28) claims ~2× cost from MoE; useful, but report wall-clock actor/critic step time vs SAC to confirm the asymptotic claim under the actual network sizes used.","section":null},{"comment":"Fig. 3 (token-wise convergence depth) is suggestive but not quantitatively linked to the choice of re or the action set; a short caption note on how it motivates re would help.","section":null},{"comment":"Notation: αk,l is introduced for tokens processed per round, then used in energy/latency; a one-line reminder that only the first round processes nk tokens would reduce reader load in §3.3–3.4.","section":null},{"comment":"Related work on latent/recurrent-depth reasoning and edge split inference is adequate; a brief pointer to any prior work on feature pruning of intermediate LLM activations (beyond [27],[28]) would round out §2.2.","section":null},{"comment":"Typos/style: “T o address” / “T o optimize” (abstract/intro spacing), “Soft Actor-Critic (SAC) algorithm” repeated; “Reasoning-as-aService” missing hyphen in abstract title line.","section":null}],"recommendation":"major_revision","confidential_remarks":"The contribution is a reasonable fit for a networking/edge-AI venue if the δk fidelity and MDP–MoE observation issues are fixed. The skeptic’s concern about offline accuracy lookup is the single most important author response item; if they already run full pruned inference online and simply under-described it, the paper can move quickly to minor revision. I would not reject on novelty grounds—the latent-partition + joint (r, ρ) framing is distinctive enough—but I would not accept on the current experimental write-up."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The short version: this is a solid wireless-systems + RL paper that turns recurrent latent reasoning into a device–server service and shows semantic MoE-SAC beating plain SAC by about 18% on simulated throughput. It is not a new reasoning method; it is a resource-allocation wrapper around one.\n\nWhat is actually new is the joint action of recurrent depth r and uplink pruning ρ under device energy, server recurrence budget, and TBT latency, plus a deterministic semantic router that routes MoE experts by task type and channel. The offline accuracy-vs-(r,ρ) surfaces on GSM8K/MBPP/HellaSwag do real work: they show tasks differ in sensitivity, so task-aware experts are not decoration. Problem (P1) and the MDP are cleanly stated, the architecture description is readable, and they ship code. That is more than many MEC-LLM papers do.\n\nThe soft spot that matters is the one the stress-test flags. U is average correctness. Correctness is taken from offline curves, not from re-running the same recurrent backbone under the exact pruned uplink, q=16, and channel model for every online decision. If those curves overstate residual semantics after pruning, the 18% is a simulator artifact. That is a real fidelity gap, not a math contradiction or circular identity. Secondary issues in proportion: three discrete channel gains, linear T_comp, fixed power/bandwidth, no error bars. Common for this class of paper; they limit how hard you should lean on the headline number, not whether the design is coherent.\n\nThis is for people working on edge LLM inference and MEC resource allocation. A serious referee should see it—the formulation and the MoE-SAC comparison are grounded enough to deserve review even if validation needs tightening. Engage if you care about wireless LLM systems; skip if you want foundational latent-reasoning theory.","headline":"Competent edge-systems packaging of latent recurrent reasoning with a clear simulated MoE-SAC gain; the 18% number rides on offline accuracy surfaces the online loop never re-runs end-to-end.","tokens_in":22027,"tokens_out":489,"would_cite":true,"duration_ms":13188,"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":"MORES turns latent LLM reasoning into a wireless service: devices and edge servers share recurrent steps, and a semantic MoE DRL agent lifts system throughput about 18% over plain SAC.","keywords":["large language models","inference-time scaling","latent reasoning","mobile edge computing","deep reinforcement learning","mixture of experts","wireless networks","distributed inference"],"falsifier":"Run the same recurrent-depth model end-to-end on a real wireless link with the paper’s pruning rates (up to ~8–15%) and quantization; if measured answer accuracy falls far below the offline GSM8K/MBPP/HellaSwag curves in Figs. 7–9, or if the learned (r, ρ) policy no longer beats SAC on throughput, the joint scheduler’s claimed trade-off fails.","tokens_in":21873,"feed_emoji":"📡","tokens_out":1002,"duration_ms":10077,"temperature":0.7,"pith_summary":"Large language models can reason better at inference time by running more computation rather than growing parameters, but both explicit chain-of-thought and implicit latent recursion are too heavy for phones and other edge devices. MORES treats that extra reasoning as a service over the wireless network. It focuses on implicit latent reasoning: a device runs the encoder, a few local recurrent updates, and the decoder, while an edge server continues the same recurrent unit on the intermediate latent state when deeper thinking is needed. Intermediate features can be pruned before uplink so communication stays cheap. How many recurrent steps to request and how hard to prune become a joint scheduling decision under device energy, server recurrence budget, and per-token latency caps. The paper solves that decision with a Soft Actor-Critic agent whose actor and critic use a semantic Mixture-of-Experts design: a lightweight router classifies the query type, deterministic experts specialize by task and channel, and a shared expert carries common knowledge. On a mix of math, code, and commonsense benchmarks with three channel qualities, that agent raises average correct-answer throughput by roughly 18% over conventional SAC under the same budgets.","feed_headline":"Latent LLM reasoning shared over wireless gains 18% throughput","feed_subtitle":"Devices and edge servers split recurrent steps; a semantic MoE agent schedules depth and pruning","key_machinery":"Distributed latent recursion plus a semantic MoE-SAC controller: the device applies the prelude encoder and a fixed number of local recurrent steps, ships the (optionally pruned) latent representation and state, the server continues the same recurrent unit, and the device decodes; the controller’s semantic router and deterministic expert branches set the total recurrent steps r and pruning rate ρ per request.","core_discovery":"The paper claims that recursive latent reasoning can be cleanly partitioned across a wireless device–server link, and that a semantic MoE-based SAC agent that jointly chooses the total recurrent depth and the uplink pruning rate can exploit task and channel heterogeneity to raise long-run system throughput by about 18% relative to ordinary SAC under device energy, server recurrence, and latency constraints.","pith_inferences":["If latent pruning remains faithful under real channel noise and quantization, MORES-style services become a practical alternative to shipping long explicit CoT sequences over cellular or Wi-Fi links.","The same semantic-router + shared-expert pattern could be reused for multimodal or multi-user edge inference where task mix and radio conditions vary even faster than in the paper’s three-benchmark, three-channel setting.","Once recurrence budgets are priced, operators could treat reasoning depth as a billable network resource rather than only as a model hyperparameter."],"forward_implications":["Edge devices can request adjustable reasoning depth from a server without shipping full chain-of-thought tokens, cutting both communication volume and explicit intermediate output.","Task-type and channel-aware expert routing lets a single DRL policy specialize resource choices for math, code, and commonsense queries under poor, fair, and good channels.","System throughput becomes a schedulable quantity under explicit device-energy and server-recurrence budgets rather than an uncontrolled byproduct of fixed model depth.","The same partitioning idea extends, in principle, to any recurrent latent reasoning stack whose intermediate state is compact enough to prune and transmit."],"fun_headline_variants":["Edge-server split of latent LLM steps lifts throughput 18% via MoE schedule","Wireless partition of recursive LLM reasoning yields 18% system throughput gain","Semantic MoE agent splits latent reasoning over air for 18% higher throughput","MORES shares recurrent LLM updates wirelessly, boosting throughput by 18%","Device-cloud latent reasoning co-op with adaptive depth raises throughput 18%"],"cache_read_input_tokens":16512,"weakest_assumption_plain":"That you can throw away a non-trivial fraction of the latent features on the uplink and still leave enough semantic state for the server’s continued recurrence to produce accurate answers at the rates the offline accuracy curves suggest.","fun_headline_variants_meta":{"raw":{"variants":["Edge-server split of latent LLM steps lifts throughput 18% via MoE schedule","Wireless partition of recursive LLM reasoning yields 18% system throughput gain","Semantic MoE agent splits latent reasoning over air for 18% higher throughput","MORES shares recurrent LLM updates wirelessly, boosting throughput by 18%","Device-cloud latent reasoning co-op with adaptive depth raises throughput 18%"]},"model":"grok-4.5","effort":"low","cost_usd":0.004514,"raw_usage":{"total_tokens":1373,"prompt_tokens":835,"num_sources_used":0,"completion_tokens":103,"cost_in_usd_ticks":45140000,"prompt_tokens_details":{"text_tokens":835,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":435,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":835,"tokens_out":103,"duration_ms":4501,"temperature":1.0,"reasoning_tokens":435,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-10T12:48:42.374531+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Run the same recurrent-depth model end-to-end on a real wireless link with the paper’s pruning rates (up to ~8–15%) and quantization; if measured answer accuracy falls far below the offline GSM8K/MBPP/HellaSwag curves in Figs. 7–9, or if the learned (r, ρ) policy no longer beats SAC on throughput, the joint scheduler’s claimed trade-off fails.","supporting_citations":[],"review_version":1}