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REVIEW 4 major objections 5 minor 33 cited by

Memory modules, not wider observation windows, are what let robot policies solve memory-dependent tasks.

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2026-08-02 19:39 UTC pith:HUDFRONG

load-bearing objection A useful memory benchmark with a clean difficulty metric, but the headline claim that explicit memory causes the gains is only partly established—the baselines are not matched, and the internal ablations need error bars. the 4 major comments →

arxiv 2603.01229 v3 pith:HUDFRONG submitted 2026-03-01 cs.RO cs.AI

RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design

Tianxing Chen , Yuran Wang , Mingleyang Li , Yan Qin , Hao Shi , Zixuan Li , Yifan Hu , Yingsheng Zhang
show 11 more authors
Kaixuan Wang Yue Chen Hongcheng Wang Junjie Wang Tianhang Yang Renjing Xu Ruihai Wu Yao Mu Yaodong Yang Hao Dong Ping Luo
This is my paper
classification cs.RO cs.AI
keywords memory-dependent manipulationTask Memory Complexitynon-Markovian decision makingimitation learningvision-language-action policiesrobotic manipulation benchmarkdual-arm manipulationexplicit memory modules
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper's central claim is that most robotic manipulation policies fail on tasks where the right action depends on something seen or done earlier, because they assume the current frame is enough. To test this, the authors introduce Task Memory Complexity, a metric that classifies tasks by how many task-relevant past observations an optimal policy must retain, and RMBench, nine dual-arm tasks spanning this scale. They then build Mem-0, a policy with explicit memory components, and report that it outperforms memory-free baselines by 38.4 percentage points on average on M(1) tasks and 21.2 points on M(n) tasks. A reader should care because the result suggests a concrete design principle: inject explicit memory rather than relying purely on larger context windows for non-Markovian manipulation.

Core claim

The paper argues that memory-dependent manipulation tasks are inherently non-Markovian, and that the standard design of predicting actions from a fixed window of recent observations is the main reason current policies fail on them. Its evidence is a benchmark in which tasks are annotated by Task Memory Complexity — the smallest number of past task-relevant observations an optimal policy needs — and a modular policy, Mem-0, whose explicit memory components can be removed one at a time. In the paper's evaluation over 100 rollouts per task, Mem-0's success rate averages 52.8% on five M(1) tasks and 28.5% on four M(n) tasks, versus 14.4% and 7.3% for the best of four baselines. The ablation resu

What carries the argument

The central object is Task Memory Complexity, which labels each task M(0), M(1), or M(n) according to how many task-relevant past observations an optimal policy must retain. The carrying mechanism is Mem-0's dual-system architecture: a Planning Module that receives the initial frame, the language goal, and a key-memory window of completed-subtask summaries, and emits a subtask; an Execution Module that conditions a diffusion action generator on the current frame, a persistent anchor memory set at subtask start, and a sliding window of recent image latents; and a Subtask End Classifier that triggers replanning only after predicting termination for eight consecutive timesteps. This division le

Load-bearing premise

The load-bearing premise is that the M(1)/M(n) annotations and task designs isolate memory as the causal variable — if perception difficulty or low-level manipulation precision, rather than lack of memory, drives baseline failures, the central claim about memory mechanisms collapses.

What would settle it

Give a memory-free baseline the ground-truth past state as an extra input (or a much longer observation window) on the same RMBench tasks, and compare across multiple seeds. If its success rate reaches Mem-0's, then the gap is not caused by missing memory; if it does not, the paper's attribution is supported.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the central claim holds, manipulation policies for non-Markovian tasks should include explicit memory modules rather than relying on fixed-length observation histories.
  • Anchor memory — persisting a task-critical reference frame across a subtask — is the largest single contributor to success in the ablations; removing it drops M(1) average from 52.8% to 26.8%.
  • Key memory, the record of completed subtasks, is essential for M(n) tasks: removing it collapses the M(n) average from 28.5% to 4.8%.
  • Reliable subtask-termination detection is a bottleneck; with ground-truth termination signals, the M(n) average rises to 45.3%, pointing to the classifier as the next target for improvement.
  • Real-world trials on three aligned tasks show the same ordering, with the memory-enabled policy at 22.5% average success versus 5.8% for the strongest baseline.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If TMC is adopted, it gives the field a task-side memory measure that could be used to build curricula: train on M(0) tasks, then M(1), then M(n), with memory capacity added deliberately at each rung.
  • The Press Button failure suggests that explicit memory alone cannot compensate for perception that cannot resolve subtle state cues; a testable fix is adding tactile or proprioceptive signals to the classifier input.
  • Because the paper reports single evaluations rather than seed-averaged means with error bars, the size of the memory advantage may shrink under statistical repetition; re-running the same 100-rollout protocol across seeds would tell how much of the gap is robust.
  • The anchor-memory idea is a cheap inductive bias that could be transferred to any vision-language-action policy: store one reference observation at subtask boundaries and condition on it, without requiring a full recurrent memory.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces RMBench, a simulation benchmark of nine dual-arm manipulation tasks intended to evaluate memory-dependent robotic manipulation. It also defines a Task Memory Complexity (TMC) metric to classify tasks as M(1) or M(n), and proposes Mem-0, a modular policy with a planning module, an execution module, and a subtask-end classifier, using key memory, anchor memory, and sliding memory. The paper evaluates Mem-0 against DP, ACT, Pi0.5, and X-VLA on RMBench, reports ablation studies, and presents real-world experiments on three tasks. The central claim is that explicit memory modeling yields large systematic gains, summarized as 38.4% average improvement on M(1) tasks and 21.2% on M(n) tasks relative to baselines (§5.1).

Significance. The benchmark and the TMC taxonomy address an underexplored problem: systematic evaluation of memory-dependent manipulation. The modular design of Mem-0, with separable key/anchor/sliding memory components, is a useful experimental vehicle, and the paper includes qualitative failure analysis that helps identify practical bottlenecks. If the central attribution claim were established, the work would provide valuable design guidance for memory-aware policies. However, the current experimental evidence does not isolate memory as the causal factor. The comparisons in Table 1 are confounded by architectural differences, no uncertainty estimates are given, and the TMC annotations are not independently validated. The strengths are the tasks, the modular policy, and the detailed ablations; the weakness is the evidence linking the reported gains specifically to memory.

major comments (4)
  1. [§4, §5.1, Table 1] The central claim that explicit memory produces the reported +38.4%/+21.2% gains is underdetermined because Mem-0 differs from the baselines in many respects beyond memory: it uses Qwen3-VL backbones, a diffusion-transformer action head, a subtask classifier, a planner, and a custom training recipe (§4). DP and ACT are lightweight non-VLA policies trained from scratch; Pi0.5 and X-VLA are not given subtask decomposition or any memory augmentation. The ablations in Table 2 remove one memory module at a time but never remove all memory components while keeping the rest of the architecture identical. I request a memory-free control of Mem-0 (same VLM, same DiT action head, same classifier, same training, but with anchor/sliding/key memory disabled) to support the attribution.
  2. [§5.1, Table 1 and §5.3, Table 3] All success rates are point estimates without standard deviations, confidence intervals, training seeds, or significance tests. For 100 rollout episodes, the standard error of a binary success rate at 50% is 5%; at 4% it is about 2%. Thus differences such as Mem-0 4% vs Pi0.5/X-VLA 9% on Observe and Pick Up, or Mem-0 14% vs DP 20% on Swap T, may be noise. The real-world results in Table 3 use 40 trials and likewise report no variance. Report results across at least three training seeds with means and standard errors, and ideally a paired or bootstrap significance test, before claiming systematic gains.
  3. [§3.1, §3.3, §5.1] The M(1)/M(n) annotations are asserted by construction rather than established by an independent procedure. The TMC definition in Eq. (2) is formal, but the assignment of each task to a memory-complexity level and the claim that these tasks isolate memory as the causal variable are not validated. Baseline failures are attributed to the Markovian assumption in §5.1, but perception error, low-level manipulation precision, and task difficulty are not controlled. Provide a concrete test of the memory bottleneck, e.g., evaluate a memory-free version of Mem-0 with access to a privileged summary of the task-relevant past, or augment the baselines with simple memory inputs (such as a remembered object state) and show that the gap closes. Without this, the benchmark result cannot separate memory from other task demands.
  4. [§5.1, Table 1] The per-task results contradict parts of the strong summary. On two of five M(1) tasks, Mem-0 underperforms the baselines: Observe and Pick Up (4% vs 9% for Pi0.5 and X-VLA) and Swap T (14% vs 20% for DP). On Press Button, all methods achieve 0%. The paper acknowledges these outcomes in §5.1, but the aggregate statement of 'significant improvements on the majority of memory-dependent tasks' glosses over them. The claim should be qualified per task, and the reasons for these counterexamples should be analyzed more quantitatively rather than only qualitatively in Appendix C.
minor comments (5)
  1. [§4.2, Eq. (6)] The notation M^m_t for the two memory buffers is confusing; the paper later writes Manchor_t and Mslide_t. Use consistent subscripts/superscripts for anchor and sliding memories.
  2. [§3.3, Table 4, §5.3] Task names are inconsistent across the paper: 'Put Back Block' vs 'Put Back Blocks', 'Swap Block' vs 'Swap Blocks'. Standardize these names.
  3. [§5.1] The sentence beginning 'we benchmark a diverse set of policies' begins with a lowercase 'we'; also the text could explicitly state whether the 50 demonstrations and 100 rollouts are the same across all baselines, which is implied but not specified in the table caption.
  4. [§4.3] The subtask-end classifier's temporal consistency window L=8 and the action horizon H=30 are fixed hyperparameters; the paper does not report sensitivity to these choices. A brief sensitivity check, even in the appendix, would strengthen the design claims.
  5. [Appendix C] The failure analysis is useful but mostly qualitative. It would be improved by reporting the frequency of each failure mode across the evaluated rollouts, especially for the Press Button task where the success rate is zero.

Circularity Check

0 steps flagged

No significant circularity: the memory-benefit claim is an empirical benchmark result with internal ablations, not a consequence forced by the paper's definitions or self-citations.

full rationale

The paper's claimed derivation chain is: Task Memory Complexity (TMC) definition → RMBench task design → evaluation of Mem-0 and baselines → insight that explicit memory components matter. None of these steps reduces to its own inputs by construction. TMC is defined through POMDP optimality over memory states, not in terms of Mem-0 or in terms of measured success rates. The M(1)/M(n) annotations are design assumptions about the tasks, not parameters fitted to the policy outputs. The headline gains (+38.4% on M(1), +21.2% on M(n)) are reported success-rate differences from Table 1; they are measured outcomes, not predictions derived from the memory architecture, and they could in principle have disfavored Mem-0. The ablation study in Table 2 removes anchor, sliding, and key memory components and shows performance drops, providing an internal control that is not circular. The citation of RoboTwin 2.0 (Chen et al., 2025a) supports the simulator infrastructure only and is not load-bearing for the memory claim; no uniqueness theorem or prior-work ansatz is imported to force the design. The skeptic's concern that Mem-0 differs from baselines in backbone, planning, and training is a confound or external-validity issue, not an equation-level circularity, so it does not raise the circularity score.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 2 invented entities

The paper's central claims rest on several domain assumptions: the POMDP/memory-state formalism, the manual TMC annotations, the attribution of baseline failures to Markovian design, and the stability of success-rate estimates without error bars. The main free parameters are architectural choices (K, L, H, training hyperparameters) that are not systematically varied. The TMC metric and Mem-0 memory modules are invented constructs whose utility is evidenced only within the paper.

free parameters (4)
  • Sliding memory window size K = not reported
    Eq. 7 truncates the sliding memory to the most recent K elements, but K is never specified or ablated; success rates may depend on it.
  • Subtask-end consistency window L = 8
    Eq. 9 requires L=8 consecutive classifier positives before termination; hand-chosen and not ablated.
  • Action horizon H = 30
    Fixed DiT action horizon in Eq. 8; affects control frequency and error accumulation.
  • Training hyperparameters (LoRA rank, LR, batch, iterations) = see Tables 5-6
    Chosen for Mem-0, not justified by ablation; baselines' settings not reported.
axioms (4)
  • standard math Robotic manipulation can be modeled as a POMDP with latent state and memory state M_t^(m) (Eq. 1-2)
    Background formalism; not under test.
  • domain assumption The number of task-relevant past observations m can be annotated per task and correctly identifies the memory demand
    Authors assign M(1)/M(n) labels by hand; no validation that optimal policy requires exactly m observations.
  • domain assumption Baseline policies' failures are due to Markovian fixed-window assumptions rather than other factors
    Stated in §5.1 interpretation; not experimentally isolated (e.g., no control with sliding-window baseline).
  • domain assumption Success rate over 100 simulation rollouts / 40 real trials is a reliable performance measure
    No seeds or confidence intervals; empirical conclusions assume stability.
invented entities (2)
  • Task Memory Complexity (M(m)) no independent evidence
    purpose: Labels tasks by minimal number of past observations needed for optimal control
    Defined by the authors and applied to their own tasks; no external falsifiable handle beyond the benchmark itself.
  • Mem-0's key/anchor/sliding memory modules no independent evidence
    purpose: Retain task-relevant history for subtask planning and action generation
    Their benefit is demonstrated only on the authors' own benchmark; no external replication.

pith-pipeline@v1.3.0-alltime-deepseek · 15846 in / 11002 out tokens · 100043 ms · 2026-08-02T19:39:23.341558+00:00 · methodology

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read the original abstract

Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle to solve tasks that require reasoning over historical observations and maintaining task-relevant information over time, which are common requirements in real-world manipulation scenarios. Although several memory-aware policies have been proposed, systematic evaluation of memory-dependent manipulation remains underexplored, and the relationship between architectural design choices and memory performance is still not well understood. To address this gap, we introduce RMBench, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities. We further propose Mem-0, a modular manipulation policy with explicit memory components designed to support controlled ablation studies. Through extensive simulation and real-world experiments, we identify memory-related limitations in existing policies and provide empirical insights into how architectural design choices influence memory performance. The website is available at https://rmbench.github.io/.

Figures

Figures reproduced from arXiv: 2603.01229 by Hao Dong, Hao Shi, Hongcheng Wang, Junjie Wang, Kaixuan Wang, Mingleyang Li, Ping Luo, Renjing Xu, Ruihai Wu, Tianhang Yang, Tianxing Chen, Yan Qin, Yaodong Yang, Yao Mu, Yifan Hu, Yingsheng Zhang, Yue Chen, Yuran Wang, Zixuan Li.

Figure 1
Figure 1. Figure 1: RMBench Tasks. We illustrate the nine memory-dependent tasks in RMBench along with their key execution steps. Tasks detailed description are shown in Appendix. A. 3.1. Task Memory Complexity (TMC) Robotic manipulation often operates under partial observ￾ability, where the current observation alone may be insuffi￾cient to determine task progress or the correct next action without access to past information.… view at source ↗
Figure 2
Figure 2. Figure 2: Mem-0 Pipeline. Mem-0 comprises a Planning Module and an Execution Module linked by a Subtask End Classifier. The Planning Module generates high-level subtasks from task instructions, observations, and key-frame memory, while the Execution Module produces low-level actions using the current observation, the subtask, and fused anchor and sliding memories in a diffusion-based policy. Upon subtask completion,… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of Baseline Typical Error. Because the baseline predicts the next action solely from the current observation, it struggles to perform reliably on non-Markovian tasks that require persistent memory over time. information to inform subsequent motion decisions, the Planning Module is unable to reliably infer the correct next subtask when restricted to the current observation alone, resulting in … view at source ↗
Figure 4
Figure 4. Figure 4: Real-world Experiment Tasks. The real-world experi￾mental setup is illustrated above. To assess the real-world performance of Mem-0, we eval￾uate it on three physical manipulation tasks aligned with RMBench: Put Back Blocks, Rearrange Blocks and Cover Blocks. We compare Mem-0 against ACT and Pi0.5, with all data collection and evaluation conducted on the X-One dual-arm robotic platform. For each task, we c… view at source ↗
Figure 6
Figure 6. Figure 6: Failure examples of Swap Blocks. (Top) Premature termination after a single subtask. (Middle) Premature termination after two subtasks. (Bottom) Failure to terminate on time, resulting in the initiation of a redun￾dant subtask. 0 Rearrange Blocks T Initial scene Move blocks Press button Release Press button (redundant) Back to origin Move blocks [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Failure examples of Rearrange Blocks. Mem-0 redundantly presses the button, resulting in task failure. C.1. Failures Analysis for M(1) Tasks For M(1) tasks, in addition to the failures illustrated in [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Failure examples of Cover Blocks. The Classifier fails to accurately detect the completion of the Uncover xxx subtask, thereby preventing a subtask transition. As the instruction remains unchanged, the model is forced to operate under a wrong task context, leading to unintended and erratic behaviors. Cover Blocks. The current design of Classifier occasionally fails to accurately perceive the ongoing task p… view at source ↗
Figure 9
Figure 9. Figure 9: Failure examples of Blocks Ranking Try. Upon pressing the button, the system is expected to transition to the next subtask to execute the swapping of designated blocks. However, the Classifier fails to trigger this transition promptly, causing the task to stall in the Press button state. This leads to a coordination conflict between the dual arms: the right hand attempts to initiate manipulation while the … view at source ↗
Figure 10
Figure 10. Figure 10: Failure examples of Press Button. (Top) Insufficient presses: the Classifier issues a false positive termination signal even when the button-press is unsuccessful. (Bottom) Excessive presses: the Classifier fails to recognize a successful subtask completion, leading to redundant execution of the same subtask. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Failure examples of Battery Try. (Top) For the horizontally oriented cylindrical battery, a suboptimal grasp pose prevents a successful lift and causes significant displacement, leading the model into unforeseen observational states. (Bottom) The model fails to commit to a specific manipulation strategy during battery adjustment, resulting in a mean action that leads to improper placement in the slot. Pre… view at source ↗

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22 extracted references · 17 linked inside Pith · cited by 28 Pith papers

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    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    Han, S., Qiu, B., Liao, Y ., Huang, S., Gao, C., Yan, S., and Liu, S. Robocerebra: A large-scale benchmark for long- horizon robotic manipulation evaluation.arXiv preprint arXiv:2506.06677,

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    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    Li, Q., Liang, Y ., Wang, Z., Luo, L., Chen, X., Liao, M., Wei, F., Deng, Y ., Xu, S., Zhang, Y ., et al. Cogact: A foundational vision-language-action model for synergiz- ing cognition and action in robotic manipulation.arXiv preprint arXiv:2411.19650, 2024b. Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H. R., Fu, C., Lunawat, I., Sieh, I., Kir...

    Pith/arXiv arXiv

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    Lin, M., Ding, P., Wang, S., Zhuang, Z., Liu, Y ., Tong, X., Song, W., Lyu, S., Huang, S., and Wang, D. Hif-vla: Hindsight, insight and foresight through motion represen- tation for vision-language-action models.arXiv preprint arXiv:2512.09928,

    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    arXiv

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    Pith/arXiv arXiv

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    Pith/arXiv arXiv

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    URL https: //arxiv.org/abs/2505.11032. Wen, J., Zhu, Y ., Zhu, M., Tang, Z., Li, J., Zhou, Z., Liu, X., Shen, C., Peng, Y ., and Feng, F. Diffusionvla: Scaling robot foundation models via unified diffusion and autore- gression. InForty-second International Conference on Machine Learning. Wen, J., Zhu, M., Zhu, Y ., Tang, Z., Li, J., Zhou, Z., Li, C., Liu,...

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    Wen, J., Zhu, Y ., Li, J., Tang, Z., Shen, C., and Feng, F. Dexvla: Vision-language model with plug-in diffu- sion expert for general robot control.arXiv preprint arXiv:2502.05855, 2025a. Wen, J., Zhu, Y ., Li, J., Zhu, M., Tang, Z., Wu, K., Xu, Z., Liu, N., Cheng, R., Shen, C., et al. Tinyvla: Towards fast, data-efficient vision-language-action models fo...

    Pith/arXiv arXiv

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    URL https://arxiv.org/abs/ 2304.13705. Zheng, J., Li, J., Wang, Z., Liu, D., Kang, X., Feng, Y ., Zheng, Y ., Zou, J., Chen, Y ., Zeng, J., Zhang, Y .- Q., Pang, J., Liu, J., Wang, T., and Zhan, X. X-vla: Soft-prompted transformer as scalable cross-embodiment vision-language-action model,

    Pith/arXiv arXiv

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    URL https:// arxiv.org/abs/2510.10274. Zheng, Y ., Zhang, R., Zhang, J., Ye, Y ., Luo, Z., Feng, Z., and Ma, Y . Llamafactory: Unified efficient fine- tuning of 100+ language models. InProceedings of the 62nd Annual Meeting of the Association for Computa- tional Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand,

    Pith/arXiv arXiv

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    3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations.arXiv preprint arXiv:2403.03954,

    Ze, Y ., Zhang, G., Zhang, K., Hu, C., Wang, M., and Xu, H. 3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations.arXiv preprint arXiv:2403.03954,

    Pith/arXiv arXiv

  19. [2023]

    Autobio: A simulation and benchmark for robotic automation in digi- tal biology laboratory.arXiv preprint arXiv:2505.14030,

    Lan, Z., Jiang, Y ., Wang, R., Xie, X., Zhang, R., Zhu, Y ., Li, P., Yang, T., Chen, T., Gao, H., et al. Autobio: A simulation and benchmark for robotic automation in digi- tal biology laboratory.arXiv preprint arXiv:2505.14030,

    Pith/arXiv arXiv

  20. [2024]

    Expertise need not monopolize: Action-specialized mixture of ex- perts for vision-language-action learning.arXiv preprint arXiv:2510.14300,

    Shen, W., Liu, Y ., Wu, Y ., Liang, Z., Gu, S., Wang, D., Nian, T., Xu, L., Qin, Y ., Pang, J., et al. Expertise need not monopolize: Action-specialized mixture of ex- perts for vision-language-action learning.arXiv preprint arXiv:2510.14300,

    arXiv

  21. [2025]

    Univtac: A unified simulation platform for visuo-tactile manipulation data generation, learning, and benchmarking.arXiv preprint arXiv:2602.10093,

    Chen, B., Wan, W., Chen, T., Guo, X., Xu, C., Qi, Y ., Zhang, H., Wu, L., Xu, T., Li, Z., et al. Univtac: A unified simulation platform for visuo-tactile manipulation data generation, learning, and benchmarking.arXiv preprint arXiv:2602.10093,

    arXiv

  22. [2026]

    Robotwin 2.0: A scal- able data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation

    Chen, T., Chen, Z., Chen, B., Cai, Z., Liu, Y ., Li, Z., Liang, Q., Lin, X., Ge, Y ., Gu, Z., et al. Robotwin 2.0: A scal- able data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506.18088, 2025a. Chen, T., Mu, Y ., Liang, Z., Chen, Z., Peng, S., Chen, Q., Xu, M., Hu, R., Zhang, H....

    Pith/arXiv arXiv