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REVIEW 4 major objections 5 minor 26 references

A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that an Informer-based Transformer predicts a surgical robot's tool-tip position with over 90% accuracy under packet loss, outperforming LSTM, RNN, and TCN on the JIGSAWS knot-tying task.

desk verdict A relevant application of Informer to telesurgery position prediction, but the undefined accuracy metric and the unresolved ground-truth term in Eq (15) make the >90% claim unsupported. read the letter →

arxiv 2501.14678 v1 pith:AD5KGR6S submitted 2025-01-24 cs.RO cs.AI

Muhammad Hanif Lashari , Shakil Ahmed , Wafa Batayneh , Ashfaq Khokhar This is my paper · ORCID
classification cs.ROcs.AI
keywords TactileInternetremoteroboticsurgerypositionestimationInformermodelpacketlossHiddenMarkovJIGSAWSdatasetlong-sequencetime-seriesforecasting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to establish that a Transformer-based forecasting model called the Informer can keep the patient-side robot arm of a remote surgical system moving accurately even when the network drops packets. The authors simulate random and bursty packet loss with a four-state Hidden Markov Model, feed the corrupted position stream into the Informer, and report per-axis prediction accuracies of 96.68%, 95.96%, and 90.37% on JIGSAWS knot-tying kinematics. If the claim holds, telesurgery systems could compensate for lost haptic data locally rather than waiting for retransmissions. The paper also claims this framework beats RNN, LSTM, and TCN baselines on the same task while keeping computational cost near $\mathcal{O}(L \log L)$.

What carries the argument

The load-bearing machinery is the Informer model, a Transformer variant whose ProbSparse attention keeps only the top-$u$ queries selected by a KL-divergence sparsity score, cutting attention cost from $\mathcal{O}(L^2)$ to $\mathcal{O}(L \log L)$. A four-state Hidden Markov Model supplies realistic packet-loss patterns that corrupt the position stream before it reaches the predictor. The paper's modification adds the squared true-minus-predicted position error $e_x(t)^\top W e_x(t)$ to the sparsity metric, and the optimization problem from Section III is folded into training through loss terms for position error, energy, smoothness, and network robustness.

What would settle it

Run the model's test phase with the true-position term in Eq. (15) replaced by a causal estimate or removed, and compare the resulting X, Y, and Z accuracies with the reported 96.68%, 95.96%, and 90.37%; a large drop would show the published accuracy depends on access to future ground truth.

Watch

Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that the Informer architecture—a Transformer built for long-sequence time-series forecasting—can be adapted to estimate the 3D tool-tip position of the patient-side manipulator accurately enough to absorb network-induced packet loss in Tactile Internet telesurgery. The authors modify the Informer's ProbSparse attention with a position-error term, embed constraints such as energy efficiency and smoothness through a differentiable optimization layer, and augment the input with simulated network conditions. Evaluated on 39 knot-tying trials from the JIGSAWS dataset, the model is reported to achieve over 90% accuracy on all three axes and lower MSE, MAE, and RMSE than LSTM, RNN, and TCN.

Load-bearing premise

The claim that the model predicts future positions above 90% accuracy depends on a formula that, as written, needs the true position being predicted; if that formula is active at test time, the accuracy figure is not a prediction.

Editorial extensions

If this is right

  • If the reported accuracy is reproducible, telesurgery systems can keep the patient-side arm on course during packet loss without relying on retransmission, which would add unacceptable latency.
  • The $\mathcal{O}(L \log L)$ complexity claim means the same model could run on edge hardware near the patient side, not only in a data center.
  • The accuracy gap between axes, with Z lagging X and Y, suggests future versions should weight attention toward the axis with the fastest dynamics.
  • The four-state HMM evaluation creates a benchmark protocol that separates burst-loss and random-loss behavior, making model comparisons more informative than a single average loss rate.
  • Embedding network features such as predicted latency and jitter as auxiliary inputs points toward a predictor that adapts to changing network conditions in real time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the same packet-loss-plus-Informer pipeline could be applied to other telemetry channels, such as orientation, gripper angle, or force, which are equally latency-critical in telesurgery.
  • Inference: the per-axis accuracy differences suggest a future variant could allocate attention budget dynamically per axis, putting more weight on the fastest-moving coordinate.
  • Inference: feeding predicted latency and jitter as auxiliary inputs, as the paper proposes, points toward a closed-loop controller where the predictor and the network estimator are trained jointly.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript proposes an Informer-based prediction framework for estimating the patient-side manipulator's tool-tip position in remote robotic surgery under network packet loss. Packet loss is simulated with a four-state hidden Markov model, and the authors claim to integrate an optimization problem into the Informer's training via a differentiable optimization layer, modifying the ProbSparse attention sparsity metric to prioritize position error. The method is evaluated on the JIGSAWS knot-tying dataset, reporting over 90% prediction accuracy under various network conditions and claiming superiority over TCN, RNN, and LSTM.

Significance. If the reported results were valid, this would be a practically relevant demonstration of an efficient transformer for a Tactile Internet application, with the O(L log L) complexity argument being a strength. The use of a public dataset and comparison with standard baselines is another positive. However, the central numerical claims are not currently credible: Eq. (15) as written introduces ground-truth leakage into the attention mechanism, the 'accuracy' metric is never defined, the evaluation protocol lacks essential details, and no code or error bars are provided. These issues undermine the paper's headline contribution, although the underlying idea remains potentially salvageable.

major comments (4)
  1. [Section V-B-b, Eq. (15)] The modified sparsity metric M_pos(q_i, K) = M(q_i, K) + λ1 e_x(t)^T W e_x(t), with e_x(t) = p(t) − p_hat(t), is stated without any qualification about its use at inference. At test time, p(t) is the unknown target position, so this term cannot be computed without accessing ground-truth labels. If the term is active during inference, the attention mechanism is conditioned on the target, making the reported accuracies in Section VI the result of label leakage rather than genuine prediction. If the term is intended only for training, this must be stated explicitly, the inference-time forward pass must be defined, and the experiments must be re-run accordingly. As written, the validity of every accuracy figure in Table II and Figure 4 is in question.
  2. [Section VI-C2, Table II] The paper never defines the 'accuracy' metric used to report values such as 96.68% for the X axis. Without a formula (e.g., 1 − normalized RMSE, threshold-based hit rate, or R²), these percentages are uninterpretable and cannot be compared across models or conditions. The authors should define the metric precisely, report it alongside MSE/MAE/RMSE, and provide uncertainty estimates (e.g., standard deviation over multiple runs or trials).
  3. [Section VI] The experimental protocol is incomplete. There is no description of the train/test split (e.g., which of the 39 JIGSAWS knot-tying trials are used for training versus testing), how the HMM packet-loss corruption is applied to training and test sequences, the hyperparameters of the Informer and baselines, or the number of runs. The comparison in Table I is therefore not reproducible, and it is unclear whether TCN, RNN, and LSTM were tuned with comparable effort or received identical input features. The authors should provide full implementation details, code, and a clear evaluation protocol.
  4. [Section V-a] The claimed 'differentiable optimization layer' is not actually specified. Equations (14), (16), and (17) are weighted loss terms appended to the training objective, not an OptNet-style differentiable optimization layer with a constrained forward pass, KKT conditions, or a projection step. Either the paper should present the actual layer and how constraints are enforced during the forward pass, or it should reframe the contribution as a multi-term training loss with penalty-based regularization. As written, the contribution 'embedding constraints using a differentiable optimization layer' is not supported.
minor comments (5)
  1. [Section IV-B] The heading contains a typo: 'Approch' should be 'Approach'.
  2. [Section IV-B-d, Eq. (10)] Equation (10) is written identically to the standard attention formula in Eq. (7), but the text says Q is a sparse matrix containing the top-u queries. The equation should explicitly use the selected sparse queries, e.g., \bar{Q}, to avoid confusion.
  3. [Section IV-A, Eqs. (5)-(6)] The symbol \hat{p}(t) is used to denote the corrupted input sequence (set to zero on packet loss), whereas elsewhere in the paper \hat{p}(t) denotes the predicted position. This notation clash makes the data flow confusing and should be resolved, for example by using p_corrupted(t) for the corrupted input.
  4. [References] The reference list is malformed: reference [2] is truncated ('Accelerating Tactile...'), and reference [3] appears to be merged with the text of reference [4]. The bibliography needs to be cleaned up.
  5. [Section VI-C1, Fig. 4] The text says the packet loss pattern is shown over 1000 time steps, while Fig. 4 shows predictions for 200 test time steps; the relationship between these sequence lengths and the JIGSAWS trial lengths should be clarified.

Circularity Check

1 steps flagged · score 8.0 of 10

Eq. (15) injects the ground-truth position error into the attention sparsity metric, so the reported >90% prediction accuracy may be conditioned on the target itself.

  1. self definitional [Section V-B, Eq. (15)]
    "Mpos(qi, K) = M (qi, K) + λ1ex(t)T W ex(t) (15) where M (qi, K) is the original sparsity metric for attention weights. ex(t) = p(t)− ˆp(t) is the state estimation error for the tooltip position."

    The modified ProbSparse attention metric is defined using ex(t) = p(t) − p_hat(t), where p(t) is the future tool-tip position the model is supposed to predict and p_hat(t) is the model's own estimate. At inference, p(t) is not available; computing ex(t) requires the ground-truth label. If the term is active in the forward pass, the attention weights are steered by the target value, so the reported accuracy figures in Section VI are not independent predictions but outputs partly derived from the labels. If one instead substitutes p_hat(t) for p(t), the forward pass becomes self-referential: attention depends on the output, which depends on attention.

full rationale

The paper's claimed derivation chain is: 4-state HMM simulates packet loss, the corrupted position sequence is fed to an Informer model, and a modified ProbSparse attention mechanism yields >90% position-prediction accuracy. The base Informer architecture and the HMM are external, standard components, and the prior-work self-citation [19] about Kalman filtering is not load-bearing. The one novel, load-bearing step is Eq. (15), which adds the position-estimation error ex(t)^T W ex(t) to the attention sparsity score. Since ex(t) requires the true position p(t) at the time step being predicted, the attention mechanism is defined in terms of the target itself unless the paper explicitly removes the term at test time, which it never does. The literal reading of the model therefore makes the reported prediction accuracies (96.68%, 95.96%, 90.37%) partly products of the ground-truth trajectory. This is a definitional circularity in the central mechanism, not merely a missing benchmark or an undefined accuracy metric, and it undermines every accuracy claim in Section VI. Under a charitable training-only interpretation the results might be salvageable, but the text provides no such restriction, so the central prediction claim reduces by construction to its input.

Assumptions & free parameters 12 free parameters · 5 assumptions · 0 invented entities

The central claim depends on a large set of unstated hyperparameters, all the alpha, beta, lambda, eta, gamma, and delta weights, plus Informer architecture details, on an unvalidated packet-loss model, and on the availability of the true position error in Eq (15). No code or data is released, so the reader cannot tell which components were actually implemented.

free parameters (12)
  • Alpha (α)
    Weights the energy and robustness terms in the objective Eq (2); no value is reported.
  • Beta (β)
    Weights the network robustness term in Eq (2); no value reported.
  • Lambda1 (λ1)
    Scales the position-error term in Eq (15); no value reported, and it controls the claimed novelty of the attention modification.
  • Weighting matrix W
    Weighting matrix in Eq (15) emphasizing x, y, z components; unspecified.
  • Gamma1 (γ1)
    Regularization weight for smoothness in Eq (16); unspecified.
  • Gamma2 (γ2)
    Latency penalty weight in Eq (20); unspecified.
  • Delta1 (δ1)
    Energy penalty weight in decoder loss Eq (17); unspecified.
  • Eta1, Eta2, Eta3 (η1, η2, η3)
    Weights for packet loss, latency, and jitter in Eq (18); unspecified.
  • Burst density PB and gap density PG = varied 0.3 to 0.8 / 0.95 to 0.70
    Markov chain transition probabilities chosen by hand across experiments; not fit to real network traces.
  • Burst length and gap length = varied 4 to 12 / 3 to 8
    Chosen per Table II without justification.
  • Informer top-query factor c
    Informer hyperparameter controlling sparsity, u = c * ln LQ; not specified.
  • Informer architecture hyperparameters
    Layers, heads, d_model, learning rate, and batch size are not reported.
assumptions (5)
  • domain assumption Zero-valued samples represent lost packets (Eq 5).
    The corruption model replaces missing positions with [0,0,0]; real systems may use hold-last-value or prediction, so this choice drives the difficulty and realism of the task.
  • domain assumption The 4-state Markov chain of Eq (3) accurately describes Tactile Internet packet-loss dynamics.
    The model is asserted to be realistic but no comparison to real TI traces or validation is provided.
  • domain assumption JIGSAWS knot-tying kinematics at 30 Hz represent the haptic command stream of remote surgery.
    The dataset is a bench-top surgical activity dataset, not a haptic teleoperation command stream with network timing.
  • ad hoc to paper The differentiable optimization layer can be trained end-to-end and preserves O(L log L).
    The paper describes the layer but gives no training algorithm, no convergence guarantee, and no experiment showing it was used.
  • ad hoc to paper The sparsity metric Mpos in Eq (15) is evaluable without the target p(t) at inference.
    ex(t)=p(t)-p_hat(t) contains the ground truth; the paper never explains how this term is handled at test time.

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Cite this review

Pith. "Pith review of A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model." pith.science (2026). https://pith.science/paper/AD5KGR6S

@misc{pith2026250114678,
  author       = {Pith},
  title        = {Pith review of: A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AD5KGR6S}},
  note         = {Machine review of arXiv:2501.14678}
}
read the original abstract

Precise and real-time estimation of the robotic arm's position on the patient's side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation. Additionally, it combines a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of O(L log L). The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90 percent under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework's superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery.

Figures

Figures reproduced from arXiv: 2501.14678 by the authors.

Figure 1
Figure 1. Remote Robotic Surgery Framework Utilizing TI and Informer Model [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Informer Model Encoder-Decoder Framework with ProbSparse [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The top plot (Part 1) shows the simulated packet loss pattern across [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Prediction performance of the Informer model under packet loss [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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