{"id":"709f078b-af91-43b6-ace2-8568072f6565","arxiv_id":"2508.18610","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":5,"one_line_summary":"FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.","lead":"This paper describes a multi-agent reinforcement learning system for peer-to-peer electricity trading in which a large language model scores fairness and feeds those scores back into agents' rewards. The authors report that the approach shifts more trades to neighbors, cuts consumer bills, and keeps the grid utility profitable across simulated and real-world-style communities.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"No ablation isolates the LLM fairness-shaping term: all cases use the full Eq. (17) reward, so the 54% P2P share and 27-30% savings cannot be attributed to the LLM critic.","rationale":"I agree with the reader's rejection but for a slightly different emphasis. The reader's weakest assumption is that the LLM scoring function is stable and accurate; that is a real reproducibility and validity defect. My strongest concern is more directly about the causal claim: without an ablation that removes or replaces the fairness bonuses, the experiments cannot distinguish the effect of LLM-guided fairness shaping from the effect of the underlying PPO/CDA market simulator. The paper's own results make this distinction urgent because Cases 1-3 vary community size, horizon, and asset mix and all use transferred policies, so the consistency of the 54/46 split across cases could be an artifact of the CDA/grid tariff structure rather than of fairness shaping. Secondary internal inconsistencies reinforce the unverified status but are not the main load-bearing point: Section 2.2 states that each hour is independent because there is no storage, while Section 2.1.1 and Case 3 explicitly include storage; and the largest single-hour peer trade drops from about 24 kWh in Case 1 to about 2.4 kWh in the larger Case 2. The concrete test above would settle the attribution question directly; if the no-shaping baseline already achieves the headline numbers, the central claim fails, which supports the reader's REJECT verdict without requiring a change.","tokens_in":15581,"tokens_out":5912,"duration_ms":55204,"concrete_test":"Run the Case 1 setup with identical seeds and hyperparameters under three conditions: (A) full FairMarket-RL reward as in Eq. (17) with the actual LLM output; (B) the same pipeline with beta_grid = beta_price = beta_peer = 0 (economic-only PPO); and (C) the same reward shape but FTG, FBS, and FPP computed by deterministic closed-form definitions, e.g., FTG = P2P-served demand share, FBS = 1 minus normalized variance of seller quantities, and FPP = 1 minus normalized interquartile range of clearing prices. Compare P2P share, consumer bill reduction, grid profit, and fairness trajectories across at least 5 seeds. If condition B already reaches roughly 54% P2P share and 27-30% savings, or if condition C matches A, then the central LLM-attribution claim is unsupported. Report the LLM model, prompt, and temperature used for condition A.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that LLM-guided fairness shaping shifts P2P markets toward equitable outcomes without sacrificing economic performance. The experiments, however, never compare the full reward in Eq. (17) against a control that removes or replaces the fairness bonuses. All three cases train or transfer policies with the same shaped reward, and no baseline sets beta_grid = beta_price = beta_peer = 0 or substitutes deterministic fairness scores for the LLM outputs. Consequently, the reported ~54% P2P share, 27-30% consumer savings, FTG/FBS/FPP values, and utility profitability are all compatible with the possibility that the CDA clearing plus PPO alone produce these outcomes, and that the LLM critic contributes nothing. This is not a minor omission: the title and Section 4 make the LLM critic the causal mechanism, while Eq. (17) adds the LLM scores as slot-level bonuses that are identical for all prosumers except for the seller-share weighting on FBS, so the shaping signal could easily be redundant with what the auction already does. The undefined LLM scoring in Section 2.3 (no formulas, prompt, model, temperature, or calibration) compounds the problem by making the treatment condition unreproducible, but the decisive gap is the missing control condition.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes FairMarket-RL, a multi-agent reinforcement learning framework for peer-to-peer (P2P) electricity markets in which a large language model (LLM) computes three slot-level fairness metrics—FTG, FBS, and FPP—from the post-trade ledger and adds them as reward-shaping bonuses to a PPO-trained bidding policy. The market is modeled as a continuous double auction with discrete price–quantity actions, partial observability, storage dynamics, and grid settlement. Three case studies are presented: a 30-day, three-prosumer pilot; a 90-day, ten-prosumer synthetic community; and a 90-day, twelve-household real-world dataset with battery-equipped and PV-only prosumers. The paper reports that the framework shifts trades toward local P2P exchange (about 54% P2P share), reduces consumer costs by 27–30% relative to grid-only procurement, maintains high fairness scores, preserves grid profitability, and behaves reasonably under PV and load perturbations.","tokens_in":15775,"tokens_out":5093,"duration_ms":47732,"significance":"If the central claim were established, the paper would make a useful contribution: it would show that LLM-computed fairness signals can be embedded into MARL reward shaping for electricity markets without sacrificing economic performance. The manuscript has several strengths: the environment is detailed and physically grounded, the authors provide source code, the case-study progression from a small pilot to a mixed-asset real-world dataset is sensible, and the economic outcomes (consumer bills, grid profit) are independent of the fairness metrics and provide some external grounding. However, the load-bearing causal claim—that the LLM-guided fairness shaping, rather than the auction mechanism or the unshaped PPO policy, produces the reported outcomes—is not supported by the experiments as presented. The treatment condition is incompletely specified, and no control condition is run.","major_comments":[{"comment":"The LLM fairness critic is not specified in any reproducible way. Section 2.3 states only that \"we send the post-slot ledger to LLM, which returns three fairness metrics scaled to the interval [0,1]\", but the paper does not identify the LLM, the prompt, the temperature, the parsing procedure, or any calibration. Moreover, FTG, FBS, and FPP are defined only verbally (e.g., \"1 when quantities are evenly shared\"), with no mathematical formulas. Because these scores enter the agent rewards through Eq. (17) and are then plotted in Figures 4, 8, and 12 as evidence of success, the reported fairness results cannot be independently verified and the treatment condition is unreproducible. A deterministic, formula-based definition of FTG, FBS, and FPP, together with an ablation that replaces the LLM with those formulas, is essential to support the paper's claims.","section":"Section 2.3 and Eq. (17)"},{"comment":"No control condition isolates the effect of the fairness-shaping terms. All experiments train (or transfer) policies with the full reward in Eq. (17), including the LLM-derived bonuses; there is no baseline with beta_grid = beta_price = beta_peer = 0, no PPO-only baseline without fairness shaping, and no ablation that replaces the LLM outputs with deterministic fairness scores. Consequently, the approximately 54% P2P share, the 27–30% consumer savings, and the reported FTG/FBS/FPP values are all compatible with the possibility that the CDA clearing and unshaped PPO alone produce these outcomes. Since the title and the conclusion in Section 4 attribute the results to LLM-guided fairness shaping, this missing control is a load-bearing gap.","section":"Sections 3.1–3.3, Eq. (17), and Section 4"},{"comment":"There is a direct internal inconsistency in the experimental protocol. Section 3.2.1 states that \"Fairness-shaped PPO policies trained in Case 1 are transferred without retraining,\" and Section 3.3.1 states that \"Policies are fixed from previous cases checkpoints to evaluate generalization without retraining or hyperparameter changes.\" Yet Sections 3.2.2 and 3.3.2 and Figures 8 and 12 present \"training diagnostics\" with Episode returns and fairness metrics over 10,000 training episodes. If the policies are not retrained, these curves cannot be training curves; if they are retrained, the transfer/generalization claim is contradicted. The manuscript must clarify which setting produced Figures 8 and 12 and what those curves actually represent.","section":"Sections 3.2.1–3.2.2 and 3.3.1–3.3.2, Figures 8 and 12"},{"comment":"The economic results depend on tariff assumptions that are never reported. The claimed consumer cost reductions of 27–30% are computed relative to a grid-only baseline, and the grid's net profit (e.g., $57.7 in Case 1, about $590 in Case 2, about $379 in Case 3) depends directly on the retail and feed-in tariffs. The paper does not state these tariff values or the baseline procurement cost formula, so the economic claims cannot be reproduced or assessed. The authors should report the tariffs used, the grid-only baseline calculation, and the resulting sensitivity of the savings numbers to tariff choices.","section":"Sections 3.1.5, 3.2.6, and 3.3.6"},{"comment":"The reported fairness values are internally inconsistent across cases and do not support the conclusion of stable high fairness. Case 1 states that \"FTG stabilizes around 0.80–0.85\" (Section 3.1.2), while Case 2 reports that \"FTG improves to about 0.72 by late training\" (Section 3.2.2), and Case 3 says that FTG converges \"slightly below\" an 0.8 threshold (Section 3.3.2), although no threshold is defined anywhere. The paper should explain whether these differences are expected and should not describe fairness as uniformly \"high\" or \"stable\" when the headline metric moves from about 0.8–0.85 to about 0.72.","section":"Sections 3.1.2, 3.2.2, and 3.3.2"},{"comment":"All reported results are from single training runs with no error bars, seeds, or statistical measures. The sensitivity analysis in Section 3.3.3 also appears to be a single fixed-policy counterfactual per perturbation. Given that the central claims concern robustness and generalization, the absence of multiple seeds or confidence intervals makes it difficult to distinguish systematic effects from noise in the PPO training and in the stochastic environment.","section":"Figures 4, 8, and 12; Section 3.3.3"}],"minor_comments":[{"comment":"The fairness metric names are inconsistent: the Introduction uses \"Fairness-by-Share (FBS)\" and \"Fairness-by-Price (FPP)\", while Section 2.3 uses \"Fairness-Between-Sellers (FBS)\" and \"Fairness-of-Pricing (FPP)\". The names should be standardized throughout.","section":"Section 2.3 and Introduction"},{"comment":"Two different figures are both labeled \"Figure 9\": one for hourly energy volumes and one for market fairness metrics. The latter should be renumbered.","section":"Section 3.2.3 and 3.2.4"},{"comment":"The text contains an incomplete word: \"At the start of each slotuseholds receive...\" should be \"At the start of each slot, households receive...\".","section":"Section 2.1.3"},{"comment":"The notation for the seller-share weighting is ambiguous: the denominator \"∑_{k=1}^{m} q_{k,t}^{sold}\" is not typeset cleanly, and the sum should be over all prosumers in that slot. Please clarify the indexing and define the case when the denominator is zero.","section":"Equation (17)"},{"comment":"The claim that the largest single-hour peer trade in Case 2 was about 2.4 kWh, while the 90-day P2P total is 2,260 kWh, is surprising; please verify whether this value is in kWh or reflects a different aggregation unit, and clarify the units consistently with Case 1, where the largest hourly trade is reported as about 24 kWh.","section":"Section 3.2.3"}],"recommendation":"major_revision","confidential_remarks":"The paper is an extension of the authors' own preprint, which is acknowledged, so I see no novelty-disclosure concern. The main issue is not the idea but the evidence: the current experiments cannot distinguish the effect of the LLM fairness shaping from the auction/PPO baseline, and the LLM module is described at a level that prevents replication. Both issues are fixable with additional experiments and specification, but they are central to the claims, so the revision should be substantial."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe headline: the paper proposes an LLM-based fairness critic that shapes PPO rewards for P2P market bidding, and claims it aligns private incentives with equity without sacrificing economic performance. The architecture is novel and the engineering is solid, but the evidence as presented does not establish the claim. The decisive problem is that there is no control condition: every experiment uses the full shaped reward, so you cannot tell whether the LLM fairness bonuses, or just the CDA plus PPO, produce the 54% P2P split and 27-30% savings. The stress-test note is right on this point.\n\nWhat's genuinely new: the LLM-as-fairness-critic component is absent from prior work, and extending the earlier short letter with batteries, a 90-day real-world dataset, and sensitivity analyses is a real step up. The environment model is well specified—discrete price-quantity actions, CDA clearing, physical constraints, and SOC dynamics are all clearly described. The computational times and the GitHub link are useful.\n\nThe soft spots are substantial. First, no ablation isolates the fairness term; the paper needs a run with the LLM bonuses removed, and ideally with deterministic fairness scores, to attribute any causal effect. Second, the LLM scoring function is completely unspecified: no model, prompt, temperature, or calibration, and no formula connecting the ledger to FTG/FBS/FPP. Since these scores are both the reward and the headline outcome, the fairness results are only as credible as the black-box scorer. Third, the economic reproduction requires tariffs and beta scaling factors, which are not reported. Fourth, there are internal numeric oddities—Case 2's largest single-hour peer trade is reported as 2.4 kWh versus Case 1's 24 kWh, which looks like a typo or an inconsistent result, and the runs are single-seed with no error bars. The FTG 0.85 vs 54% P2P split is less damning than the reader suggests: if FTG is an unweighted hourly average, the two can coexist.\n\nWho is this for? Readers working on LLM-based reward shaping in multi-agent energy markets will find it a useful engineering template, but they should not treat the reported fairness or economic numbers as validated until the ablation and LLM details are provided. I would send this to peer review—a serious referee can demand the missing control and specification—but I would not accept it in its current form. The authors should be asked to add the ablation, document the LLM critic fully, report all parameters, and run multiple seeds.\n\nBring it to reading group if you want a case study in circular evaluation and missing baselines.\n\nBest.","headline":"Clever architecture, but the central claim is untested: no control isolates the LLM fairness critic, and the LLM scoring is underspecified, so the reported fairness and savings are not yet attributable to the method.","tokens_in":16426,"tokens_out":6607,"would_cite":false,"duration_ms":61729,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"LLM-scored fairness bonuses shift peer-to-peer energy markets toward local trading, cutting consumer bills by up to 30% without destroying grid profit.","keywords":["peer-to-peer energy trading","continuous double auction","multi-agent reinforcement learning","fairness shaping","large language model critic","reward shaping","prosumer markets","partial observability"],"falsifier":"Fix one hour's post-slot ledger from Case 1 and run the LLM critic ten or more times, also under several different prompts or models; if the returned FTG/FBS/FPP triplets vary by more than a small tolerance (say 0.1 on [0,1]), Eq. (17) is not a well-defined function of the ledger and the reported fairness outcomes cannot be attributed to a stable shaping signal.","tokens_in":15279,"feed_emoji":"⚡","tokens_out":5531,"duration_ms":48902,"temperature":0.7,"pith_summary":"This paper tries to establish that fairness can be injected into peer-to-peer electricity trading without sacrificing profit. A large language model reads each hour's trade ledger and returns three fairness scores—grid reliance, seller balance, and price spread—which are added as reward bonuses to multi-agent reinforcement learning agents bidding in a continuous double auction. Across a 30-day pilot, a 90-day scaled community, and a 90-day real-world dataset, the trained policies shift roughly 54% of energy to local peer-to-peer trades, cut consumer costs by 27–30% versus grid-only procurement, keep seller participation balanced, and leave the grid profitable. The claim a sympathetic reader would take away is that LLM-guided fairness shaping is a modular, scalable way to align private bidding incentives with community-level equity, and that it can be layered onto standard PPO training without destabilizing learning.","feed_headline":"LLM fairness scores steer energy markets to 54% local trading","feed_subtitle":"Language-model fairness bonuses reshape bidding rewards, cutting consumer bills up to 30% without hurting grid profit.","key_machinery":"The load-bearing mechanism is the fairness-shaped reward in Eq. (17): a prosumer's slot reward is raw profit plus three LLM-derived fairness bonuses—FTG (grid-reliance penalty), FPP (price-spread tightness), and FBS scaled by the seller's volume share—with coefficients $\\lambda$ ramped linearly over episodes so that fairness guidance fades in only after basic trading is learned. This sits inside a continuous double auction in which the lowest asks match the highest bids while $p_{\\text{buy}} \\geq p_{\\text{sell}}$, with agents trained by Proximal Policy Optimization under partial observability: each agent sees only its own forecasts, weather, time, and battery state, not the order book. The LLM critic is the novel component, converting the post-slot ledger into $[0,1]$ fairness scores that become part of the learning signal.","core_discovery":"The central discovery is that post-slot fairness feedback can act as a shaping signal rather than a post-hoc evaluation. After each auction slot, the LLM critic converts the ledger into normalized scores FTG (1 if all demand is met via P2P), FBS (1 if seller quantities are evenly shared), and FPP (1 if clearing prices cluster tightly around the median), and these enter each prosumer's reward as scaled bonuses with coefficients that ramp from zero to full strength over training (Eqs. 17–20). The fairness bonuses complement rather than overwhelm economic incentives; the peer-sharing term is additionally weighted by the seller's share of the slot's traded volume. The paper argues that this aligns private incentives with community equity under partial observability, and reports that across all three cases the market stabilizes at roughly 54% peer-to-peer energy, consumer bills fall by 27–30%, seller-sharing entropy stays high, and grid net profit remains positive.","pith_inferences":["The LLM's specific role may be replaceable: FTG, FBS, and FPP are aggregate statistics of the ledger, so a hand-coded formula could plausibly produce the same scores; the paper's real contribution would then be the reward-shaping scheme rather than the language model itself. A direct ablation replacing the LLM with a deterministic calculator would settle this.","If the shaping scheme generalizes, the same template could steer markets toward other normative goals—carbon intensity, local resilience, or participation of vulnerable households—by swapping the scorer that reads the ledger.","The fairness metrics are defined at community level, so the promise depends on the community being the right unit; in larger systems with heterogeneous neighborhoods, aggregating one set of scores per slot may hide intra-community inequities."],"forward_implications":["If the central claim holds, fairness shaping transfers from a small pilot to a larger community and to a real-world mixed-asset dataset without retraining, since Cases 2 and 3 reuse policies from earlier checkpoints.","The ramp schedule lets fairness incentives phase in during training, and the paper reports stable convergence in roughly 1,000–1,500 episodes across cases, suggesting the shaping does not destabilize PPO.","The concrete economic corollaries are the roughly 54/46 peer-to-grid split and 27–30% consumer bill reductions: local trading becomes the default without the grid losing money.","Sensitivity results around ±20% PV and ±10% load imply that the shaped policies adapt proportionally, preserving seller-sharing entropy and grid viability under supply and demand shocks.","Because the framework is modular, swapping the LLM scorer or the fairness metrics would change which community-level objective is being shaped, making the design adaptable to other equity targets."],"supporting_citations":[{"why":"Supplies the PPO algorithm used to train all bidding agents.","marker":"[40]"},{"why":"Defines the continuous double auction clearing mechanism used to match asks and bids.","marker":"[41]"},{"why":"DA-MADDPG baseline whose bill-reduction and internal-trade results the paper compares against.","marker":"[19]"},{"why":"Prior short-letter version of the FairMarket-RL framework that this paper extends and scales.","marker":"[28]"},{"why":"Hierarchical price-mediation fairness scheme that motivates the ledger-based fairness feedback approach.","marker":"[21]"},{"why":"Distributional fairness optimization approach cited as an alternative fairness mechanism.","marker":"[22]"},{"why":"Evolutionary multi-objective study of the fairness-efficiency trade-off, framing the fairness gap in prior work.","marker":"[23]"},{"why":"Constrained Markov game approach that treats fairness as a constraint rather than a shaping signal.","marker":"[27]"}],"fun_headline_variants":["LLM fairness steers P2P markets to 54% local trades","Fairness bonuses in RL cut consumer bills by 30%","AI fairness shaping: 54% local energy, 30% savings","LLM-guided rewards make energy markets fair and local","Fairness scores shift auctions to 54% local P2P flow"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole argument rests on the unstated premise that the LLM's fairness scores are stable, repeatable, and accurate reflections of the trade ledger, since the paper never specifies the scoring functions, the LLM model, the prompt, or any calibration, and Eq. (17) feeds those scores directly into training.","fun_headline_variants_meta":{"raw":{"variants":["LLM fairness steers P2P markets to 54% local trades","Fairness bonuses in RL cut consumer bills by 30%","AI fairness shaping: 54% local energy, 30% savings","LLM-guided rewards make energy markets fair and local","Fairness scores shift auctions to 54% local P2P flow"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000891,"raw_usage":{"total_tokens":3877,"prompt_tokens":1011,"completion_tokens":2866,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":627,"completion_tokens_details":{"reasoning_tokens":2775}},"tokens_in":627,"tokens_out":2866,"duration_ms":21873,"temperature":1.0,"reasoning_tokens":2775,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T16:55:42.718854+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Fix one hour's post-slot ledger from Case 1 and run the LLM critic ten or more times, also under several different prompts or models; if the returned FTG/FBS/FPP triplets vary by more than a small tolerance (say 0.1 on [0,1]), Eq. (17) is not a well-defined function of the ledger and the reported fairness outcomes cannot be attributed to a stable shaping signal.","supporting_citations":[{"cited_title":"Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model,","cited_arxiv_id":null,"evidence_quote":"Defines the continuous double auction clearing mechanism used to match asks and bids."},{"cited_title":"Jadhav, B","cited_arxiv_id":null,"evidence_quote":"Prior short-letter version of the FairMarket-RL framework that this paper extends and scales."},{"cited_title":"Designing fairness in autonomous peer -to-peer energy trading,","cited_arxiv_id":null,"evidence_quote":"Hierarchical price-mediation fairness scheme that motivates the ledger-based fairness feedback approach."},{"cited_title":"Distributionally Fair Peer-to-Peer Electricity Trading","cited_arxiv_id":"2501.09713","evidence_quote":"Distributional fairness optimization approach cited as an alternative fairness mechanism."},{"cited_title":"A Peer -to-Peer Energy Trading Model for Optimizing Both Efficiency and Fairness,","cited_arxiv_id":null,"evidence_quote":"Evolutionary multi-objective study of the fairness-efficiency trade-off, framing the fairness gap in prior work."}],"review_version":2}