Pith. sign in

REVIEW 3 major objections 6 minor 24 references

Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read 97.5% of maneuvers succeed when an LLM pilots a neuromorphic drone

desk verdict A real but thinly evaluated neuromorphic-LLM drone demo; the go/no-go safety rule has a sign bug that needs fixing before this should be published. read the letter →

arxiv 2501.19259 v2 pith:Z4WWSXHR submitted 2025-01-31 cs.RO cs.CVcs.LGcs.NEcs.SYeess.SY

Amogh Joshi , Sourav Sanyal , Kaushik Roy This is my paper · ORCID
classification cs.ROcs.CVcs.LGcs.NEcs.SYeess.SY
keywords LLMneuromorphicvisioneventcameraspikingneuralnetworkautonomousdroneedgeAIhuman-robotinteractionmotionplanning
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

This paper sets out to establish that a drone can be flown interactively by combining a fine-tuned large language model with neuromorphic vision: a person speaks or types a maneuver, the LLM decides whether it is safe and turns it into a high-level plan, and an event camera with a spiking neural network plus a physics-based planner executes the plan on a Parrot Bebop2. The authors report that this is the first integration of an LLM with neuromorphic sensing for interactive autonomous indoor flight, and they demonstrate it with a moving ring obstacle. If the approach holds, it points to a way of giving humans natural-language control over agile, energy-efficient robots without relying on heavy vision-language models.

What carries the argument

The load-bearing piece is a go/no-go gate inside the LLM. A fine-tuned classification head on Llama-3.2-3B Instruct receives a user prompt, an environment state report, and drone capability bounds, and outputs either a high-level command or a rejection; the labels it was trained on are generated by Algorithm 1, a kinematic feasibility check comparing the required acceleration, final velocity, and traversal time against the drone's maximum acceleration, maximum safe velocity, and time horizon. This gate lets an LLM that has never seen real flight dynamics make online safety decisions, and it is coupled to the neuromorphic planner so that execution, not just language understanding, is event-driven.

What would settle it

Run the real-maneuver protocol with the ring commanded to reverse direction mid-flight so the execution-time acceleration exceeds the assumed $a_{\max}$; if the drone fails to reject and collides, the go/no-go gate has been bypassed by the very kinematic model it trusts.

Watch

Extended reading notes

Core claim

The central claim is that a small fine-tuned LLM can act as the human-interaction layer of a neuromorphic navigation stack, replacing bulkier vision-language pipelines. Neuro-LIFT uses a Llama-3.2-3B Instruct model with a classification head, fine-tuned for five epochs on 5,000 prompt-environment-capability samples whose go/no-go labels come from a kinematic feasibility check, to translate natural-language commands into high-level flight plans or a safe reject. Those plans are executed by a real-time adaptation of EV-Planner, which combines DVS event data and a shallow spiking neural network for ring tracking with physics-driven planning and PID control on a Parrot Bebop2. The paper reports 97.5% maneuvering accuracy on a held-out test set and 39 of 40 successful real maneuvers, with reject cases triggering an immediate landing. The intended contribution is the first reported LLM-plus-neuromorphic interactive autonomous flight system.

Load-bearing premise

The safety story rests on the kinematic feasibility check in Algorithm 1, which assumes the drone can follow a constant-acceleration profile to a target velocity within the time horizon, while real flight dynamics, wind, sensing error, and actuator limits are not part of that check.

Editorial extensions

If this is right

  • A single fine-tuned LLM can convert spoken commands into executable flight plans without a vision-language model, and the neuromorphic planner can carry them out in real time.
  • The reject mechanism provides a concrete safety behavior: infeasible or risky commands terminate in an immediate landing rather than an attempted maneuver.
  • The modular architecture means the language, sensing, and planning components can be upgraded or replaced independently.
  • Event-based sensing keeps latency and power low enough for the planning and control stack to run at the edge on a Jetson Nano alongside the drone's low-level PID controllers.
  • The reported test accuracy and real-maneuver success rate indicate that the fine-tuned LLM generalizes to prompt phrasings beyond those in its training set.

Reading between the lines

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

  • The kinematic labeler is the true safety envelope: anything the constant-acceleration model cannot foresee, such as wind, actuator lag, or tracking error, sits outside what the go/no-go gate was trained to judge; a natural next test is to generate labels from closed-loop simulation or a learned dynamics model instead of Algorithm 1.
  • The same LLM-plus-neuromorphic gating pattern could transfer to ground robots or underwater vehicles by swapping in that platform's kinematic or dynamic model for feasibility labeling.
  • Because the DVS sensor and Jetson are mounted on a tripod rather than onboard, the full stack has not yet been shown to fit the Bebop2's roughly 50-gram payload; a larger airframe or a smaller event camera would settle whether the claimed edge deployment is truly self-contained.
  • The moving-ring test could be extended to multiple rings or a ring with changing orientation, which would test whether the go/no-go labels and the planner handle richer obstacle geometry than the current single-pivot swaying ring.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces Neuro-LIFT, a framework that combines a fine-tuned Llama-3.2-3B Instruct LLM for natural-language command interpretation, a Davis346B event camera with a spiking neural network for object tracking, and the EV-Planner algorithm on a Parrot Bebop2 quadrotor. The LLM receives a user command plus an environment state report and outputs a go/no-go decision based on a kinematic feasibility check (Algorithm 1). If the decision is go, EV-Planner generates a flight trajectory that is executed through PID control. The authors report 97.5% accuracy on a 1000-sample offline test set, and a real-world demonstration of 39/40 successful maneuvers through a moving ring obstacle. The central claim is that this is the first integration of LLMs with neuromorphic sensing for interactive, autonomous indoor flight.

Significance. If the system works as described, it is a useful proof-of-concept for combining high-level language-based reasoning with low-latency neuromorphic perception and physics-based planning on a physical drone. The authors deserve credit for building a real hardware demonstration, randomizing obstacle velocity and adding random delays to reduce unintended feedback, and clearly presenting Algorithm 1 for inspection. However, the safety-critical go/no-go layer contains a sign error in the feasibility check, and the quantitative claims about latency and power reduction are unsupported by measurements. These issues substantially weaken the current evidence for the paper's safety and efficiency claims, although the general architecture remains a defensible research direction.

major comments (3)
  1. [III-B, Algorithm 1] The kinematic feasibility check only rejects maneuvers when a > amax or vf > vmax. It never checks a < -amax, so a maneuver requiring the drone to decelerate harder than its maximum safe braking acceleration is labeled feasible. It also never checks vf < 0, so trajectories that would require reversing direction pass the check. Because the LLM is fine-tuned on labels generated by this rule and the same rule is used as the in-flight go/no-go gate, the paper's safety claims are not supported. The check should be corrected (e.g., require |a| <= amax and vf >= 0, or an explicit deceleration limit) and the LLM evaluation and flight tests should be re-run with the corrected rule.
  2. [IV-B] The only quantitative results for the flight demonstration are the aggregate numbers "97.5% maneuvering accuracy" and "39/40 maneuvers successful." The paper does not specify the trial protocol, the success/failure criteria for each maneuver, the number of go versus no-go decisions, or any confidence interval. Moreover, because the 97.5% accuracy is measured against labels produced by Algorithm 1, it primarily measures consistency with a rule that has the flaw described in the previous comment; it does not validate the safety or physical feasibility of the decisions. A detailed breakdown and a corrected label set are needed.
  3. [Abstract, III-D, IV] The paper repeatedly claims that the neuromorphic approach reduces latency and power consumption (e.g., "significantly reducing latency and power consumption" in the conclusion and "optimizing actuator energy use" in related work), but no latency or power measurements are reported for the DVS sensor, the SNN, the Jetson Nano, or the overall system. These claims should be supported by direct measurements, or explicitly deferred to future work to avoid overstating the contribution.
minor comments (6)
  1. [I] The reference to "Fig. 4" in the introductory overview should be "Fig. 1," which is the diagram actually referenced.
  2. [III-B] The text says the model is fine-tuned "in few-shot fashion" but describes training on 5000 samples for 5 epochs; this is standard supervised fine-tuning rather than few-shot learning, so the terminology should be clarified.
  3. [III-B] There is a typo: "Simliar" should be "Similar."
  4. [III-E] The caption of Fig. 3 states that the motion capture system provides poses, but the text says this feedback is not used by the planner; please clarify what the Optitrack data is used for in the presented results.
  5. [IV-A] Figure 6 shows qualitative detection examples; adding a quantitative detection or tracking metric (e.g., detection rate, localization error) would strengthen the evaluation.
  6. [V] The authors should more explicitly differentiate Neuro-LIFT from prior LLM-based drone navigation work, particularly the vision-language model approach of [20], since Neuro-LIFT uses a language-only LLM with a separate neuromorphic perception module.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the LLM is a supervised classifier fitted to Algorithm 1, and the real-world maneuvers provide external, non-circular validation; self-citations are present but not load-bearing.

full rationale

The paper is an integration paper rather than a formal derivation, and I find no load-bearing circular step. In Section III-B the LLM is explicitly fine-tuned as a binary classifier on labels generated by Algorithm 1 ('These labels are generated by checking the kinematic, velocity, and time feasibility of the required movement, as shown in Algorithm 1'), so the reported 97.5% figure is best read as supervised agreement with that rule on a same-distribution synthetic test set, not as an independent physical prediction. This is a standard supervised-learning evaluation: it measures how well the LLM learned to mimic a closed-form rule, and it does not make the central claim circular because the paper does not claim the LLM derives feasibility from first principles. The separate real-world result (39/40 maneuvers) is an external, non-circular validation, although it is thinly documented. The main imported component, EV-Planner [13], is cited as prior published work with its own hardware demonstrations; although the reference shares authors, it is not invoked as a uniqueness theorem or as the sole justification for an ansatz. A genuine safety limitation exists—Algorithm 1 checks neither a < -amax nor vf < 0, so it can certify maneuvers requiring impossible braking or reversed velocity—but that is a correctness and robustness flaw, not circularity. There are self-citations (e.g., [1], [13], [20], [24]), but none carries the derivation's weight. Score 1 reflects minor presentational conflation of classifier accuracy with 'maneuvering accuracy' and the inherited-rule limitation, not circular reasoning.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The system relies on several domain assumptions that are not independently validated: a simple kinematic model for safety, DVS-based tracking accuracy, and external motion capture for pose. The free parameters vmax, amax, and t are fitted experimentally and are load-bearing for the feasibility check. No new physical entities are introduced.

free parameters (3)
  • max safe velocity vmax = not stated
    Obtained experimentally such that roll and pitch angles remain within 'safe' limits (Section III-B).
  • max safe acceleration amax = not stated
    Same experimental tuning as vmax; used in Algorithm 1 feasibility check.
  • time horizon t = not stated
    Time-to-collision or maneuver time horizon in Algorithm 1; no value or derivation is given.
assumptions (4)
  • domain assumption Kinematic feasibility (Algorithm 1) is a sufficient proxy for real-world maneuver safety.
    The LLM is trained to output go/no-go based on this check; if the check is too permissive or ignores non-kinematic constraints, the safety decisions are invalid. No flight dynamics or sensing error model is added.
  • domain assumption The DVS plus shallow SNN detects and tracks the ring accurately enough for planning at the required rate.
    Section III-A and IV-A assert tracking capability with qualitative images only; no tracking error or latency numbers are provided.
  • domain assumption Optitrack motion capture provides sufficiently accurate and low-latency pose of the drone and sensor.
    Figure 3 and Section III-C use motion capture poses; the text says pose feedback is not used by the planner, but the sensor-drone relative motion is canceled using this pose, so the system depends on the mocap.
  • domain assumption The fine-tuned LLM generalizes from synthetic feasibility labels to real-world prompts and environment states.
    The fine-tuning dataset is synthetic, generated with random coordinates; the paper reports 97.5% accuracy but does not analyze failure cases or domain shift.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge." pith.science (2026). https://pith.science/paper/Z4WWSXHR

@misc{pith2026250119259,
  author       = {Pith},
  title        = {Pith review of: Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z4WWSXHR}},
  note         = {Machine review of arXiv:2501.19259}
}
read the original abstract

The integration of human-intuitive interactions into autonomous systems has been limited. Traditional Natural Language Processing (NLP) systems struggle with context and intent understanding, severely restricting human-robot interaction. Recent advancements in Large Language Models (LLMs) have transformed this dynamic, allowing for intuitive and high-level communication through speech and text, and bridging the gap between human commands and robotic actions. Additionally, autonomous navigation has emerged as a central focus in robotics research, with artificial intelligence (AI) increasingly being leveraged to enhance these systems. However, existing AI-based navigation algorithms face significant challenges in latency-critical tasks where rapid decision-making is critical. Traditional frame-based vision systems, while effective for high-level decision-making, suffer from high energy consumption and latency, limiting their applicability in real-time scenarios. Neuromorphic vision systems, combining event-based cameras and spiking neural networks (SNNs), offer a promising alternative by enabling energy-efficient, low-latency navigation. Despite their potential, real-world implementations of these systems, particularly on physical platforms such as drones, remain scarce. In this work, we present Neuro-LIFT, a real-time neuromorphic navigation framework implemented on a Parrot Bebop2 quadrotor. Leveraging an LLM for natural language processing, Neuro-LIFT translates human speech into high-level planning commands which are then autonomously executed using event-based neuromorphic vision and physics-driven planning. Our framework demonstrates its capabilities in navigating in a dynamic environment, avoiding obstacles, and adapting to human instructions in real-time.

Figures

Figures reproduced from arXiv: 2501.19259 by the authors.

Figure 1
Figure 1. Functional Overview of the Neuro-LIFT Framework [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Components of the Neuromorphic Sensing Module [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Planning and control module: Drone and DVS sensor poses are taken from the Optitrack motion capture system [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Neuro-LIFT Edge-AI System Architecture. EV-Planner is adapted from [13]. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Pulley-and-chain mounting mechanism of the ring [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Processing of event data captured by the Neuromor [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Drone navigation trajectories through the ring. The success and reject cases are visualized with the ring’s trajectory [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Drone trajectory for the reject case showing immediate [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Phases of Neuro-LIFT Control Execution for ”Fly through Center of Ring” [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 12 canonical work pages

  1. [1]

    SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning

    A. Joshi, A. K. Kosta, and K. Roy, “Shire: Enhancing sample efficiency using human intuition in reinforcement learning,” 2024. [Online]. Available: https://arxiv.org/abs/2409.09990

    work page Pith review arXiv 2024

  2. [2]

    Language models are few-shot learners,

    T. Brown, B. Mann, and et al. , “Language models are few-shot learners,” Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020

    work page 1901

  3. [3]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

    arXiv 2023

  4. [4]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763

    2021

  5. [5]

    Zero-shot text-to-image generation,

    A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. V oss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . Pmlr, 2021, pp. 8821–8831

    2021

  6. [6]

    Palm-e: An embodied multimodal language model,

    D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu et al., “Palm-e: An embodied multimodal language model,” arXiv preprint arXiv:2303.03378, 2023

    arXiv 2023

  7. [7]

    A low power, fully event-based gesture recognition system,

    A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza et al. , “A low power, fully event-based gesture recognition system,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7243–7252

    2017

  8. [8]

    A 128 ×128 120 db 15 µs latency asynchronous temporal contrast vision sensor,

    P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128 ×128 120 db 15 µs latency asynchronous temporal contrast vision sensor,” IEEE journal of solid-state circuits , vol. 43, no. 2, pp. 566–576, 2008

    2008

Show all 24 references
  1. [9]

    A qvga 143 db dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain cds,

    C. Posch, D. Matolin, and R. Wohlgenannt, “A qvga 143 db dynamic range frame-free pwm image sensor with lossless pixel-level video compression and time-domain cds,” IEEE Journal of Solid-State Cir- cuits, vol. 46, no. 1, pp. 259–275, 2010

    2010

  2. [10]

    Spikenet: A simulator for modeling large networks of integrate and fire neurons,

    A. Delorme, J. Gautrais, R. Van Rullen, and S. Thorpe, “Spikenet: A simulator for modeling large networks of integrate and fire neurons,” Neurocomputing, vol. 26, pp. 989–996, 1999

    1999

  3. [11]

    Spike-flownet: event-based optical flow estimation with energy-efficient hybrid neural networks,

    C. Lee, A. K. Kosta, A. Z. Zhu, K. Chaney, K. Daniilidis, and K. Roy, “Spike-flownet: event-based optical flow estimation with energy-efficient hybrid neural networks,” in European Conference on Computer Vision. Springer, 2020, pp. 366–382

    2020

  4. [12]

    Adaptive-spikenet: event-based optical flow estimation using spiking neural networks with learnable neuronal dynamics,

    A. K. Kosta and K. Roy, “Adaptive-spikenet: event-based optical flow estimation using spiking neural networks with learnable neuronal dynamics,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 6021–6027

    2023

  5. [13]

    Ev-planner: Energy-efficient robot navigation via event-based physics-guided neuromorphic plan- ner,

    S. Sanyal, R. K. Manna, and K. Roy, “Ev-planner: Energy-efficient robot navigation via event-based physics-guided neuromorphic plan- ner,” IEEE Robotics and Automation Letters , 2024

    2024

  6. [14]

    The llama 3 herd of models,

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, and et al., “The llama 3 herd of models,” 2024. [Online]. Available: https: //arxiv.org/abs/2407.21783

    2024 arXiv

  7. [15]

    Dotie-detecting objects through temporal isolation of events using a spiking architecture,

    M. Nagaraj, C. M. Liyanagedera, and K. Roy, “Dotie-detecting objects through temporal isolation of events using a spiking architecture,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 4858–4864

    2023

  8. [16]

    Toffe – temporally-binned object flow from events for high-speed and energy-efficient object detection and tracking,

    A. K. Kosta, A. Joshi, A. Roy, R. K. Manna, M. Nagaraj, and K. Roy, “Toffe – temporally-binned object flow from events for high-speed and energy-efficient object detection and tracking,” 2025. [Online]. Available: https://arxiv.org/abs/2501.12482

    2025 arXiv

  9. [17]

    [Online]

    Optitrack Motion Capture System . [Online]. Available: https: //optitrack.com/

  10. [18]

    On transforming reinforcement learning with transformers: The development trajectory,

    S. Hu, L. Shen, Y . Zhang, Y . Chen, and D. Tao, “On transforming reinforcement learning with transformers: The development trajectory,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 12, pp. 8580–8599, 2024

    2024

  11. [19]

    A survey on multimodal large language models for autonomous driving,

    C. Cui, Y . Ma, X. Cao, W. Ye, Y . Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao et al. , “A survey on multimodal large language models for autonomous driving,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 958– 979

    2024

  12. [20]

    Asma: An adaptive safety margin algorithm for vision-language drone navigation via scene-aware control barrier functions,

    S. Sanyal and K. Roy, “Asma: An adaptive safety margin algorithm for vision-language drone navigation via scene-aware control barrier functions,” 2024. [Online]. Available: https://arxiv.org/abs/2409.10283

    2024 arXiv

  13. [21]

    Hifi-cs: Towards open vocabulary visual grounding for robotic grasping using vision-language models,

    V . Bhat, P. Krishnamurthy, R. Karri, and F. Khorrami, “Hifi-cs: Towards open vocabulary visual grounding for robotic grasping using vision-language models,” arXiv preprint arXiv:2409.10419 , 2024

    2024 arXiv

  14. [22]

    Lingo-space: Language- conditioned incremental grounding for space,

    D. Kim, N. Oh, D. Hwang, and D. Park, “Lingo-space: Language- conditioned incremental grounding for space,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 9, 2024, pp. 10 314–10 322

    2024

  15. [23]

    Ramp-net: A robust adaptive mpc for quadro- tors via physics-informed neural network,

    S. Sanyal and K. Roy, “Ramp-net: A robust adaptive mpc for quadro- tors via physics-informed neural network,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1019–1025

    2023

  16. [24]

    Real-time neuromorphic navigation: Integrating event-based vision and physics-driven planning on a parrot bebop2 quadrotor,

    A. Joshi, S. Sanyal, and K. Roy, “Real-time neuromorphic navigation: Integrating event-based vision and physics-driven planning on a parrot bebop2 quadrotor,” 2024. [Online]. Available: https: //arxiv.org/abs/2407.00931

    2024 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.