{"id":"4f746840-dd6d-4345-b1c4-e33deb807711","arxiv_id":"2505.02634","paper_version":2,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":6,"one_line_summary":"Transfer learning from a NeuralFoil surrogate to XFoil cuts DRL airfoil-optimization training time by about 86% while keeping aerodynamic performance close to full XFoil training.","lead":"A deep reinforcement learning agent is trained to reshape airfoils for better lift-to-drag ratio while preserving maximum thickness, using a reward that combines both goals. Pretraining on a fast surrogate and fine-tuning on XFoil cuts training time by about 86% with nearly matched performance.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Hybrid-case claim of 'comparable performance' is contradicted by the paper's own Table VI: at σ≥30, TL improvement drops 16–52% and best-median by 8–20%, so the 'up to 94% time reduction' claim needs qualification.","rationale":"The reader's weakest assumption focused on the untuned PSO baseline. That is a legitimate comparison-fairness concern, but I do not think it is the most load-bearing point for the paper's headline claim. The headline claim has two parts: (1) in the purely aerodynamic case, TL strategy #1 reaches improvement 136±44 versus 140±49 and best 236(28) versus 241(37), with an estimated 86% solver-time reduction; this part is internally consistent and credible. (2) In the hybrid case, the paper claims time reductions up to 94% while performance is only 'slightly lower (around 10%)'. The paper's own Tables IV and VI contradict that summary: for σ=30, 100, and 1000 the relative improvement losses are roughly 16%, 26%, and 52%, and the best-median losses range from 8% to 20%. These are the same σ values where the paper advertises the largest time reductions. Therefore the 'comparable performance' claim is not uniformly supported; it is load-bearing because the abstract and conclusions present the hybrid result as a central contribution. A reader should condition acceptance on either restricting the claim to the pure aerodynamic and low-σ regimes, or on providing equivalence tests with a predefined margin. I still believe the paper's core methodology is promising and the pure-aerodynamic TL result is valuable, so the existing CONDITIONAL verdict remains appropriate; no change to the reader's verdict label is needed.","tokens_in":18470,"tokens_out":11426,"duration_ms":139179,"concrete_test":"Take the pointwise statistics from Table IV and Table VI for each σ and compute the relative loss in improvement and in best-median as (no-TL value − TL value)/no-TL value. Apply a pre-specified equivalence margin, e.g. at most 10% relative loss in improvement, and identify the set of σ values for which the TL agent is actually 'comparable'. If σ≥30 exceeds this margin, as the reported numbers suggest, the hybrid 'up to 94%' claim should be revised to state the accompanying performance degradation, or the TL training budget at high σ should be increased until it matches the no-TL performance.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that TL-enhanced DRL achieves performance comparable to full XFoil training while saving up to 94% of solver time is internally inconsistent in the hybrid case. Comparing the no-TL results in Table IV with the TL results in Table VI at identical σ: at σ=30, improvement falls from 94±41 to 79±36 (−16%) and best-median from 186 to 171 (−8%); at σ=100, improvement falls from 84±42 to 62±35 (−26%) and best-median from 176 to 151 (−14%); at σ=1000, improvement falls from 54±35 to 26±22 (−52%) and best-median from 137 to 110 (−20%). The paper's text states that 'the improvement in aerodynamic efficiency is only slightly lower for every value of σ (around a 10%)', which is not supported by these pointwise statistics for σ≥30. Moreover, the largest time reductions (92.6%, 94.8%, 89.7%) occur precisely at σ=30, 100, and 1000, where the performance loss is largest. Thus the claim of 'comparable performance without meaningful performance loss' is defensible only for the purely aerodynamic case and for low σ (roughly σ≤20); as written, it overgeneralizes to the hybrid regime. This is not a disagreement about external consensus but an internal inconsistency between the reported tables and the qualitative summary.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a transfer learning (TL) enhanced deep reinforcement learning (DRL) framework for two-dimensional airfoil shape optimisation, using Proximal Policy Optimization (PPO) over an 18-parameter CST representation. The reward scalarises a multi-objective problem: it combines the lift-to-drag ratio with a Gaussian penalty that preserves the airfoil's maximum thickness as a structural-integrity proxy. The agent is trained either directly with XFoil or pre-trained with the NeuralFoil surrogate and then fine-tuned with XFoil under four transfer strategies. The authors evaluate the resulting policies on the UIUC/Aerosandbox airfoil dataset and compare them with Particle Swarm Optimization (PSO) as implemented in Xoptfoil2. The central empirical claim is that TL-enhanced DRL achieves performance close to full XFoil training while reducing estimated solver time by about 86% in the purely aerodynamic case and up to about 94% in the hybrid aerodynamic/structural case.","tokens_in":18789,"tokens_out":6137,"duration_ms":74304,"significance":"If the central claims hold after revision, the paper makes a useful contribution to data-driven aerodynamic shape optimisation. It demonstrates a concrete multi-fidelity DRL pipeline, evaluates it on a large airfoil dataset, includes several transfer-learning strategies including a non-converging control, and reports statistical comparisons. The paper also provides detailed PPO hyperparameters, solver settings, CST bounds, and states that the framework is available through the open-source pyLOM library, which supports reproducibility. The main qualitative result—that a cheap surrogate can pre-train a policy that is then fine-tuned with a high-fidelity panel method—is plausible and of broad interest. However, the headline comparability claim in the hybrid case is not supported by the paper's own tables, one methodological equation appears to contain a sign error, and the PSO baseline is explicitly untuned; these issues must be addressed before the empirical conclusions can be accepted.","major_comments":[{"comment":"The state update is written as STATE ← −STATE + α ⊙ ACTION. As written, this replaces the current state by its negative plus a bounded action increment, which is not an incremental geometry modification and contradicts the surrounding text describing how the action 'performs the change in the parameters'. If the implementation follows Eq. (2), the learned policy operates on a different dynamical system than described; if the implementation uses STATE + α ⊙ ACTION, the equation must be corrected. Please fix this sign error and state explicitly which update rule was actually used in the experiments.","section":"Section II.B.1, Eq. (2)"},{"comment":"The statement that 'the improvement in aerodynamic efficiency is only slightly lower for every value of σ (around a 10%)' is contradicted by the pointwise comparison of Table VI with Table IV. For σ=30, improvement drops from 94±41 to 79±36 (16%) and best-median from 186 to 171 (8%); for σ=100, improvement drops from 84±42 to 62±35 (26%) and best-median from 176 to 151 (14%); for σ=1000, improvement drops from 54±35 to 26±22 (52%) and best-median from 137 to 110 (20%). These are also the cases with the largest time reductions (92.6%, 94.8%, and 89.7%). The claim of 'comparable performance' should therefore be restricted to the purely aerodynamic case and to small σ (roughly σ≤20), or the results should be reframed as a trade-off in which high values of σ sacrifice more performance in exchange for larger time savings.","section":"Section III.C, hybrid optimisation case, Tables IV and VI"},{"comment":"The number of evaluated airfoils for the same DRL/XFoil purely aerodynamic setup is 1456 in Table II but 1982 in Table V. The text attributes differences in evaluated airfoils to XFoil non-convergence and says the number 'fluctuates a bit', but a 526-airfoil gap (about 36%) is far larger than that wording suggests and changes the evaluation set across comparisons. Please report the evaluation-set construction, give per-agent convergence counts, and either use a fixed common evaluation set or show explicitly that the qualitative conclusions are insensitive to the differing sets.","section":"Section III.A and Table V"},{"comment":"The comparison with PSO uses Xoptfoil2 default parameters without tuning, while the DRL agents have had hyperparameters tuned, and the authors explicitly state that they cannot guarantee that PSO results would not change after thorough hypertuning. Because the headline claim that DRL outperforms PSO depends on this baseline, the statement should be softened or supplemented with a sensitivity check (for example, a reasonable PSO budget or tuned swarm parameters). As written, the comparison is not a fully fair apples-to-apples comparison, and the reader cannot assess how much of the DRL advantage is due to the DRL method itself versus the untuned PSO configuration.","section":"Section III.A, PSO baseline"}],"minor_comments":[{"comment":"There is a typo in the introduction: 'As advanced' should be 'As noted' or 'As mentioned'.","section":"Section I"},{"comment":"The word 'certifying' is used twice to describe single evaluation results; 'indicating' or 'consistent with' would be more accurate given the stochastic and baseline-dependent nature of the comparison.","section":"Section III.A"},{"comment":"The timing paragraph says DRL takes 0.0147 s/airfoil for 'all 1566 airfoils', while Table II reports 1456 evaluated airfoils for the same DRL setup; clarify which evaluation set is used for the timing comparison.","section":"Section III.A, timing comparison"},{"comment":"The statement that a call to XFoil takes 73 ms and a call to the smallest NeuralFoil model takes 4 ms is attributed to 'NeuralFoil's GitHub'; please provide the exact version or URL and state whether these times include any environment overhead or are pure solver-call times.","section":"Section III.C, solver times"},{"comment":"The hyperparameter section says an Optuna search was conducted for the NeuralFoil agent but does not report the resulting configuration or comparison; add those results to support the claim that the Optuna configuration 'closely aligns' with the empirically tuned parameters.","section":"Appendix B"},{"comment":"The conclusion restates the 86% time-reduction figure but does not mention the σ-dependent performance loss in the hybrid case; it should be aligned with the corrected, qualified claim.","section":"Conclusions"}],"recommendation":"major_revision","confidential_remarks":"The paper is within the journal's scope and the central idea is worth publishing after revision. The main issues are fixable: correcting Eq. (2), reconciling the evaluated-airfoil counts, and replacing the overgeneralized hybrid-case claim with a properly qualified statement. I recommend major revision rather than rejection because the outstanding problems are local and do not invalidate the overall approach."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The headline: this is the most systematic comparison of transfer-learning strategies for DRL airfoil optimization I have seen, and the empirical core—measured improvements, statistical tests, evaluation on a large airfoil set—is largely credible. The hybrid-case claim of 'comparable performance' is where it overreaches.\n\nWhat is actually new: the paper compares four weight-transfer strategies (full copy, partial copy, frozen/fine-tuned splits) for pretraining on NeuralFoil and fine-tuning on XFoil, and measures time reduction against a no-TL baseline. That direct comparison is not in the prior DRL airfoil work they cite. The Gaussian max-thickness penalty is a simple but sensible way to scalarize the aerodynamic/structural trade-off, and the sensitivity sweep over sigma is useful. Credit also for releasing code in pyLOM, for using Mann-Whitney U and paired sign tests rather than just eyeballing means, and for honest discussion of PSO's untuned defaults.\n\nThe soft spots, in proportion:\n\n1. Equation (2) has a sign error: STATE <- -STATE + alpha*ACTION flips the state, which would make the reported learning impossible. This is almost certainly a typo, but as written it is wrong and will confuse anyone implementing the method.\n\n2. The evaluated-airfoil counts do not reconcile. Table II reports 1456 airfoils for the pure-aerodynamic DRL agent; Table IV and Table V report 1982 for the same setup. The difference is too large to wave off as XFoil non-convergence. The authors need to explain which number is correct.\n\n3. The abstract's unqualified claim that TL achieves 'comparable performance' breaks down in the hybrid case at sigma >= 30. The stress-test note holds up: comparing Table IV with Table VI, improvement drops by 16% at sigma=30, 26% at sigma=100, and 52% at sigma=1000, with best-median dropping 8-20%. The text says 'only slightly lower... around a 10%,' which the paper's own tables contradict. The 'up to 94% time reduction' is largest precisely where the performance loss is largest. This is not a minor quibble; it is an internal inconsistency between the reported tables and the qualitative summary.\n\n4. The PSO baseline uses Xoptfoil2 defaults with no tuning. The authors acknowledge this. It weakens the 'DRL outperforms PSO' headline only modestly because the speed advantage is enormous, but the aerodynamic-improvement comparison might shift with tuned PSO.\n\n5. The solver-time comparison multiplies step counts by assumed per-call times (73 ms XFoil, 4 ms NeuralFoil) rather than measuring wall-clock. For a training-efficiency claim, that is a reasonable proxy but should be labeled as such more prominently.\n\nThe paper deserves a serious referee. The central empirical claim—that transfer learning from a surrogate cuts training cost by roughly 86% in the pure-aerodynamic case with near-equal performance—is credible and useful. The hybrid overgeneralization is fixable by qualifying the claim to low sigma and pure-aerodynamic settings, and by correcting the internal numbers. My recommendation: send it to peer review, but the referee should demand those corrections before acceptance.","headline":"A credible, practical DRL airfoil-optimization paper with a systematic comparison of transfer-learning strategies, but the hybrid-case claim of 'comparable performance' overgeneralizes and needs qualification.","tokens_in":19327,"tokens_out":1869,"would_cite":true,"duration_ms":23988,"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":"A reinforcement-learning airfoil designer pre-trained on a fast neural surrogate and fine-tuned on the accurate XFoil solver reaches nearly the same lift-to-drag improvement as full-XFoil training while cutting estimated solver time by…","keywords":["transfer learning","deep reinforcement learning","airfoil shape optimisation","lift-to-drag ratio","structural constraint","Proximal Policy Optimisation","surrogate aerodynamic model","Particle Swarm Optimisation"],"falsifier":"Time the full training runs end-to-end on identical hardware instead of estimating solver cost from step counts times nominal per-call times, and re-run the PSO baseline with hyperparameters tuned for the same XFoil budget on the same UIUC airfoils. The quantitative claims stand or fall on two numbers: the transfer-learning saving staying near 86% of real wall-clock time, and tuned PSO failing to close the gap to the DRL improvement of \\(141\\pm48\\) against PSO's \\(105\\pm50\\).","tokens_in":18223,"feed_emoji":"✈️","tokens_out":20052,"duration_ms":194379,"temperature":0.7,"pith_summary":"This paper claims that a deep reinforcement learning (DRL) agent that reshapes airfoils can be trained for a fraction of the usual cost by first pre-training the policy with a fast neural-network surrogate for the aerodynamics and then fine-tuning it with the slower, accurate panel-method solver XFoil. The optimisation target is the lift-to-drag ratio \\(C_L/C_D\\), optionally combined with a structural requirement — preserving the airfoil's maximum thickness — that is folded into the reward through a Gaussian penalty. On the full UIUC evaluation set, the best transfer-learning agent reaches a mean lift-to-drag improvement of \\(136 \\pm 44\\), very close to the \\(140 \\pm 49\\) of the agent trained entirely with XFoil, while the estimated solver time drops by about 86% in the purely aerodynamic case and up to about 94% in the hybrid case. The paper also argues that a single trained agent outperforms Particle Swarm Optimisation in aerodynamic improvement and is orders of magnitude faster per airfoil at evaluation time. If these results hold, surrogate-pretrained DRL turns airfoil optimisation into a one-time training cost and makes room for expensive high-fidelity solvers inside the loop.","feed_headline":"86% cheaper airfoil optimisation via surrogate pre-training","feed_subtitle":"Pre-training on NeuralFoil before fine-tuning on XFoil preserves lift-to-drag gains while slashing the solver budget.","key_machinery":"The load-bearing machinery is a two-solver training pipeline with a scalarised reward. The reward at step \\(i\\), \\(R_i = \\lambda_i\\kappa_i(C_L/C_D)_i - \\lambda_{i-1}\\kappa_{i-1}(C_L/C_D)_{i-1}\\), combines the differential gain in lift-to-drag with a Gaussian structural regulariser \\(\\lambda_i = $e^{{-\\sigma(x_i-1)^2}}$\\) acting on the maximum-thickness ratio \\(x_i\\), so one scalar drives both aerodynamic gain and thickness preservation. The transfer mechanism is the weight handover: the PPO actor-critic policy pre-trained on the fast surrogate NeuralFoil (4 ms per call) is re-initialised on XFoil (73 ms per call) with all weights shared and training continued (strategy #1), plus an added entropy term that pushes the fine-tuned agent to re-explore the design space. The geometry space is bounded by the 18-parameter CST representation, and each episode starts from a randomly chosen member of a 20-airfoil NACA set, so the learned policy applies to many shapes rather than a single one. The whole argument works by shifting most of the learning onto the cheap solver and keeping only a short corrective phase on the accurate one.","core_discovery":"The central discovery, stated on the paper's own terms, is that transfer learning from a cheap surrogate removes most of the expensive-solver burden of DRL airfoil optimisation without sacrificing measured performance. The agent works in an 18-parameter Class-Shape Transformation (CST) representation and receives, at each step, the differential reward \\(R_i = \\lambda_i\\kappa_i(C_L/C_D)_i - \\lambda_{i-1}\\kappa_{i-1}(C_L/C_D)_{i-1}\\), where \\(\\kappa\\) is the NeuralFoil confidence (set to 1 when XFoil is the solver) and \\(\\lambda_i = $e^{{-\\sigma(x_i-1)^2}}$\\) penalises departures of the maximum-thickness ratio \\(x_i = MT_i/MT_0\\) from 1, scalarising aerodynamics and structural preservation into one objective. An agent pre-trained for 26,312 steps with NeuralFoil and fine-tuned for 10,240 steps with XFoil (transfer strategy #1, which copies all weights and continues training) scores an improvement of \\(136 \\pm 44\\) on the UIUC evaluation set versus \\(140 \\pm 49\\) for the agent trained from scratch with 81,920 XFoil steps, while the estimated solver time falls from 5,980 s to 853 s, an 86% reduction. The same pipeline in the hybrid case cuts estimated time by up to about 94% at the price of roughly 10% lower aerodynamic improvement, and the paper reports that DRL dominates PSO in per-airfoil lift-to-drag improvement while being about \\(1.65 \\times $10^{5}$\\) times faster at inference, with the caveat that PSO preserves maximum thickness almost exactly whereas DRL does so only approximately.","pith_inferences":["The 86–94% saving is computed by multiplying training step counts by nominal per-call solver times (73 ms for XFoil, 4 ms for NeuralFoil), not by measuring end-to-end wall-clock training; a direct timing study is the natural next check, since policy updates and evaluation overhead are excluded from the figure.","The hybrid comparison is asymmetric: PSO enforces the thickness constraint almost exactly (mean deviation \\(\\Delta MT = 0.67\\%\\)) while DRL enforces it softly (\\(\\Delta MT = 11.5\\%\\) at \\(\\sigma=15\\)); adding an exact thickness constraint to the DRL reward, or a soft penalty to PSO, would put the two on equal footing and isolate the algorithm comparison from the constraint-enforcement comparison.","All training and evaluation happen at one operating point (\\(AoA=2^\\circ\\), \\(Ma=0.5\\), \\(Re=10^6\\)); since the reward already downweights low-confidence NeuralFoil predictions via \\(\\kappa\\), a testable prediction is that the transfer-learning time saving shrinks at off-design conditions where the surrogate is less certain and the fine-tuning phase has to work harder."],"forward_implications":["A transfer-learning-trained agent performs very close to the fully XFoil-trained agent on the whole UIUC set (improvement \\(136\\pm44\\) vs \\(140\\pm49\\); best \\(236(28)\\) vs \\(241(37)\\)), so surrogate pre-training is nearly a free speedup rather than a performance tax.","Because the trained policy optimises a new airfoil without any solver call (about 0.015 s per airfoil on one CPU thread), DRL is roughly \\(1.65\\times10^5\\) times faster than single-process PSO per airfoil, and the advantage grows with the number of airfoils to be shaped.","The structural regulariser \\(\\sigma\\) is a tunable trade-off knob: raising it from 0 to 1000 lowers mean improvement from \\(140\\pm49\\) to \\(54\\pm35\\) while cutting mean maximum-thickness deviation from \\(64\\pm22\\%\\) to \\(7\\pm8\\%\\), so practitioners can set the aerodynamics-versus-structure balance without re-engineering the reward.","The paper's own conclusion extends the surrogate-first, solver-second recipe to more expensive settings — CFD-based optimisation and three-dimensional wing design — where the relative saving from pre-training would be even larger."],"supporting_citations":[{"why":"Supplies XFoil, the accurate viscous panel-method solver whose per-call cost defines the expensive training budget and which is the evaluation standard for every agent.","marker":"[34]"},{"why":"Supplies NeuralFoil, the fast physics-informed surrogate used for pre-training; its speed and confidence output \\(\\kappa\\) are what make the transfer-learning scheme work.","marker":"[33]"},{"why":"Defines the PPO algorithm whose actor-critic weights are transferred and fine-tuned across solvers.","marker":"[42]"},{"why":"The prior DRL airfoil-optimisation work whose episode and reward formulation this paper adapts and extends.","marker":"[18]"},{"why":"Supplies Xoptfoil2, the PSO tool used as the classical gradient-free baseline; it calls XFoil underneath, making the DRL/PSO comparison consistent.","marker":"[50]"},{"why":"Supplies the AeroSandbox airfoil collection (a superset of the UIUC dataset) on which all agents are evaluated.","marker":"[46]"},{"why":"The prior multi-fidelity RL shape-optimisation framework whose reported cost reductions this paper claims to exceed.","marker":"[30]"},{"why":"Supplies the Class-Shape Transformation parametrisation that defines the 18-dimensional geometry space the agent acts on.","marker":"[36]"}],"fun_headline_variants":["Transfer learning cuts DRL airfoil optimization cost by 86%","Surrogate pre-training reduces DRL airfoil solver time by 86%","DRL airfoil design: 86% cheaper with transfer learning","Pre-training on cheap surrogate makes DRL airfoil optimization 86% cheaper","Transfer learning makes DRL airfoil optimization 86% more efficient"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The comparison against classical optimisation hinges on Xoptfoil2's default, untuned Particle Swarm settings being a reasonably strong baseline; the paper itself states that thorough PSO hyperparameter tuning could shift those results.","fun_headline_variants_meta":{"raw":{"variants":["Transfer learning cuts DRL airfoil optimization cost by 86%","Surrogate pre-training reduces DRL airfoil solver time by 86%","DRL airfoil design: 86% cheaper with transfer learning","Pre-training on cheap surrogate makes DRL airfoil optimization 86% cheaper","Transfer learning makes DRL airfoil optimization 86% more efficient"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000332,"raw_usage":{"total_tokens":1914,"prompt_tokens":1080,"completion_tokens":834,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":696,"completion_tokens_details":{"reasoning_tokens":734}},"tokens_in":696,"tokens_out":834,"duration_ms":8977,"temperature":1.0,"reasoning_tokens":734,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T00:46:21.417360+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Time the full training runs end-to-end on identical hardware instead of estimating solver cost from step counts times nominal per-call times, and re-run the PSO baseline with hyperparameters tuned for the same XFoil budget on the same UIUC airfoils. The quantitative claims stand or fall on two numbers: the transfer-learning saving staying near 86% of real wall-clock time, and tuned PSO failing to close the gap to the DRL improvement of \\(141\\pm48\\) against PSO's \\(105\\pm50\\).","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies XFoil, the accurate viscous panel-method solver whose per-call cost defines the expensive training budget and which is the evaluation standard for every agent."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"The prior DRL airfoil-optimisation work whose episode and reward formulation this paper adapts and extends."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies Xoptfoil2, the PSO tool used as the classical gradient-free baseline; it calls XFoil underneath, making the DRL/PSO comparison consistent."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the AeroSandbox airfoil collection (a superset of the UIUC dataset) on which all agents are evaluated."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the Class-Shape Transformation parametrisation that defines the 18-dimensional geometry space the agent acts on."}],"review_version":1}