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REVIEW 3 major objections 2 minor 9 cited by

Observational signatures and polarized images of rotating charged black holes in Kalb-Ramond Gravity

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper claims that M87* observations constrain the charge and Lorentz-violating parameters of a rotating Kalb-Ramond black hole more tightly than Sgr A* does, and that the hole's polarized image has an empty inner shadow, unlike horizon

desk verdict The submission is a metadata-only ghost: the abstract describes a black-hole imaging paper, but the attached full text is an unrelated Urbanite cs.HC paper, so there is nothing to review. read the letter →

arxiv 2508.07393 v1 pith:CT53NBOC submitted 2025-08-10 gr-qc

Chen-Yu Yang , Ke-Jian He , Xiao-Xiong Zeng , Li-Fang Li This is my paper
classification gr-qc
keywords Kalb-RamondgravityblackholeshadowpolarizedimagethinaccretiondiskEventHorizonTelescopeM87*SgrA*Lorentzviolation
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 computes what a rotating charged black hole in Kalb-Ramond gravity—general relativity extended by a Lorentz-violating antisymmetric tensor field—would look like when lit by a thin, equatorial accretion disk, and uses the simulated images to compare against Event Horizon Telescope observations of M87* and Sgr A*. Its central assertion is that the M87* data impose stronger constraints on the charge $Q$ and the Lorentz-violating parameter $G$ than the Sgr A* data do. The authors also report that increasing $a$, $Q$, and $G$ shrinks the shadow and makes the critical curve brighter and more distinct, that high observer inclination deforms the inner shadow into a hat-like shape, and that a retrograde disk is dimmed by gravitational redshift. Their most distinctive claim is that, under synchrotron emission, no polarization vectors appear inside the inner shadow—a feature they present as a potential discriminator between black holes with horizons and horizonless compact objects.

What carries the argument

The load-bearing object is the rotating charged black hole metric in Kalb-Ramond gravity, with spin $a$, charge $Q$, and Lorentz-violating parameter $G$. The mechanism that carries the argument is null-geodesic ray tracing of synchrotron radiation from an optically and geometrically thin equatorial accretion disk onto a distant observer's screen, producing shadow boundaries, redshift-factor maps, and polarization-intensity maps. This turns the metric parameters into observable image features: shadow size, critical-curve brightness, the hat-shaped inner-shadow deformation at high inclination, and the positions of polarization vectors.

What would settle it

Future polarimetric EHT imaging of M87* or Sgr A* that finds polarization vectors inside the inner shadow would eliminate the claimed contrast between these black holes and horizonless objects. Independently, recomputing the $(Q,G)$ exclusion using a different published mass and distance for M87* and showing that the allowed region moves outside the paper's claimed bounds would falsify the stronger-constraint statement.

Watch

Extended reading notes

Core claim

The authors study a rotating charged black hole solution in Kalb-Ramond gravity and ray-trace photons from an optically and geometrically thin equatorial synchrotron-emitting disk to produce shadow images, redshift maps, and polarization maps. Their central discovery is twofold. First, comparing the computed shadow angular diameter with the EHT measurements of M87* and Sgr A* yields exclusion regions on the parameters $(Q, G)$, with the M87* data claimed to be the stronger of the two. Second, the polarization pattern has a clean qualitative signature: the polarization intensity $P_o$ peaks around the lensed and higher-order images, while the inner shadow contains no polarization vectors. Thi

Load-bearing premise

The constraint comparison assumes that the angular diameters EHT resolved for M87* and Sgr A* are the shadows of this specific rotating charged Kalb-Ramond metric under the adopted mass and distance priors; a second load-bearing premise is that the thin synchrotron disk model correctly fixes where polarization vectors appear inside the shadow.

Editorial extensions

If this is right

  • If the constraint claim is correct, EHT's M87* observation already rules out a measurable region of $(Q,G)$ parameter space for this black hole family, and more tightly than Sgr A* does.
  • If the polarization signature is robust, future polarimetric EHT imaging can distinguish a rotating charged Kalb-Ramond black hole from a horizonless compact object: the black hole image has an empty inner shadow, the horizonless object does not.
  • The predicted hat-shaped inner shadow at high inclination gives a concrete morphological target for next-generation very-long-baseline interferometry.
  • The gravitational redshift dimming of retrograde accretion images implies that brightness asymmetries in observed images carry information about the disk's rotation direction relative to the black hole spin.
  • Because shadow size shrinks as $a$, $Q$, and $G$ grow, measured shadow diameters can be translated directly into parameter bounds for Lorentz-violating black hole spacetimes.

Reading between the lines

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

  • If the empty-inner-shadow signature is generic to metrics with a photon ring and no direct disk emission inside it, the same polarimetric test could separate other charged or Lorentz-violating black hole families from horizonless mimickers, not just this one.
  • A sharper test of the constraint claim would be to fit the full image—ring diameter, brightness asymmetry, and critical-curve contrast—for M87* and Sgr A* simultaneously rather than comparing angular diameters alone; the paper's reported bounds rest on the latter comparison.
  • Editorial note: the text block following the abstract in the supplied material belongs to an unrelated manuscript, so the ray-tracing and data-comparison steps behind these claims could not be inspected in this pass.
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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

3 major / 2 minor

Summary. The submission is listed as arXiv:2508.07393 (gr-qc) and its abstract describes ray-traced shadow images and synchrotron polarization maps of rotating charged black holes in Kalb-Ramond gravity, culminating in the claim that EHT M87* data bound (Q, G) more strongly than Sgr A* data and that the absence of polarization vectors inside the inner shadow distinguishes black holes with horizons from horizonless compact objects. However, the full text supplied under this identifier is an unrelated cs.HC paper, "Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics" (arXiv:2508.07390v1), which contains no physics content. No metric, equations of motion, geodesic ray tracer, emission model, redshift-factor formula, shadow mapping, or constraint procedure is present. The manuscript therefore cannot support any of the abstract's claims.

Significance. If the intended physics paper existed in the form described by the abstract, its claims would be of interest to black hole imaging and Lorentz-violating gravity: they offer falsifiable constraints on extra parameters and a proposed polarization-based horizon discriminator. However, as submitted, there is nothing to evaluate. The paper ships no machine-checked proofs, no reproducible code, no parameter-free derivation, and no numerical error analysis; the only verifiable statement is that the provided full text concerns urban visual analytics. Given the mismatch between the abstract and the full text, the significance cannot be assessed.

major comments (3)
  1. [Full Text] The supplied full text is "Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics" (arXiv:2508.07390v1), a human-computer interaction paper on urban visual analytics. It contains none of the paper described in the Abstract: no Kalb-Ramond action or rotating charged black hole metric, no geodesic/ray-tracing equations, no shadow calculation, no redshift factor, and no synchrotron polarization formalism. Consequently every quantitative result in the abstract—shadow sizes, constraint comparison, redshift maps, polarization images—is uncheckable. This is a load-bearing failure that prevents any evaluation of the claimed physics.
  2. [Abstract, constraints claim] The claim that M87* data impose stronger constraints on Q and G than Sgr A* requires the mapping from the rotating charged KR metric to the EHT-observed shadow angular diameter, including mass and distance priors for each source and assumed EHT measurement uncertainties. None of this is present, nor are the resulting exclusion regions, figures, or tables. As written, the claim is an unsupported assertion.
  3. [Abstract, polarization claim] The assertion that "no polarization vectors appear within the inner shadow" and the contrast with horizonless compact objects depends on the assumed thin equatorial disk emission and synchrotron radiative transfer. Without equations for emission, absorption, Faraday rotation, or observed polarization position angle, and without grid resolution or integration details, the claim cannot be checked. It is presented as a discriminator but rests entirely on unstated modeling assumptions.
minor comments (2)
  1. [General] The title and abstract describe a gr-qc paper, but the full text is an unrelated cs.HC submission. If this is a submission error, the correct document must be provided before any further review.
  2. [Abstract] The abstract uses the terms "inner shadow" and "critical curve" without definitions. A revised submission should define these quantities and specify the observational conventions used.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identifiable: the supplied full text is an unrelated cs.HC paper, so the derivation chain is absent rather than circular.

full rationale

The abstract of arXiv:2508.07393 claims results for rotating charged black holes in Kalb-Ramond gravity, including the statement 'compared to Sgr A*, the observational data of M87* impose stronger constraints on the charge parameter Q and the Lorentz-violating parameter G' and the polarization claim that 'no polarization vectors appear within the inner shadow.' However, the supplied 'FULL TEXT' is not that paper: it is 'Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics' (arXiv:2508.07390v1, cs.HC). Per the reviewing rule, this mismatch is treated as in-scope evidence. The physics manuscript's metric, null-geodesic and ray-tracing equations, redshift-factor formalism, synchrotron polarization setup, and the mapping from simulated shadow angular diameter to EHT-measured quantities with mass/distance priors are all absent. Consequently, there is no quoted equation or fitted parameter through which any 'prediction' can be shown to reduce to an input by construction. The reader's concern that Q and G are calibrated to the same shadow data they are said to constrain is a plausible forward-modeling worry, but without the paper's own equations the specific reduction required by the circularity standard cannot be exhibited. Absence of a verifiable derivation is an evidence/completeness problem, not circularity. Under the rule that circularity must be demonstrated by quotation and reduction, the correct finding is no significant circularity identified (score 0).

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest entirely on the KR metric (borrowed from prior work), the thin-disk synchrotron emission model, and the identification of EHT images with the model's shadow boundary. Four parameters (a, Q, G, theta_o) are scanned or constrained, with no values or error bars quoted in the abstract. No new entity is invented. What the reader pays for upstream: the spacetime, the emission model, the radiative mechanism, and the mass/distance calibration of EHT targets.

free parameters (4)
  • Q (charge parameter)
    Model parameter constrained by matching the simulated shadow to observed angular diameters; abstract claims M87* data bound it more tightly than Sgr A* data. Value not quoted in abstract.
  • G (Lorentz-violating parameter)
    Same constraint exercise as Q; abstract claims M87* imposes stronger bounds. Value not quoted.
  • a (black hole spin)
    Rotation parameter scanned in the image study; abstract says increasing a, Q, G reduces shadow size and changes critical-curve brightness.
  • theta_o (observer inclination)
    Viewing angle varied to produce the hat-like inner shadow and the Doppler blueshift enhancement.
assumptions (5)
  • domain assumption The rotating charged Kalb-Ramond black hole metric is the correct effective spacetime for the objects imaged as M87* and Sgr A*, and its (a, Q, G) parameters exhaust the relevant freedom.
    The entire constraint claim ('M87* impose stronger constraints on Q and G') assumes the KR solution represents these astrophysical black holes; no independent evidence for KR backgrounds is given in the abstract.
  • domain assumption The light source is an optically and geometrically thin accretion disk on the equatorial plane, with an emission profile fixed by the model.
    Stated in the abstract as the illumination model; brightness and polarization topology claims (critical curve brightness, absence of polarization in the inner shadow) depend on this choice.
  • domain assumption Synchrotron radiation governs the polarized emission.
    Abstract: 'polarization images under synchrotron radiation'; no other radiative processes or absorption are mentioned.
  • domain assumption The EHT-measured shadow features of M87* and Sgr A* map directly to the computed shadow boundary given adopted mass and distance priors.
    The constraint comparison is only as strong as this mapping; not discussed in the abstract.
  • standard math Photons follow standard null geodesics of the KR metric, with polarization parallel-transported along them.
    Standard ray-tracing framework implied by the method; the abstract does not describe any modified photon propagation beyond the geometry.

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Cite this review

Pith. "Pith review of Observational signatures and polarized images of rotating charged black holes in Kalb-Ramond Gravity." pith.science (2026). https://pith.science/paper/CT53NBOC

@misc{pith2026250807393,
  author       = {Pith},
  title        = {Pith review of: Observational signatures and polarized images of rotating charged black holes in Kalb-Ramond Gravity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CT53NBOC}},
  note         = {Machine review of arXiv:2508.07393}
}
read the original abstract

This paper investigates the shadow images of rotating charged black holes in Kalb-Ramond (KR) gravity, using an accretion disk that is both optically and geometrically thin and located on the equatorial plane as the light source model. The results show that, compared to Sgr A*, the observational data of M87* impose stronger constraints on the charge parameter Q and the Lorentz-violating parameter G. Under the thin accretion disk model, a larger observer inclination theta_o deforms the inner shadow into a hat-like structure. The parameters (a, Q, G) mainly affect the size of the inner shadow and the brightness of the critical curve, where increasing these parameters reduces the shadow size and enhances the distinguishability of the critical curve. In the retrograde accretion disk case, the gravitational redshift significantly reduces the observed brightness of the image. In addition, we compute the distribution of the redshift factor on the projection screen. The results indicate that the Doppler effect induced by large theta_o enhances the blueshift in the image, while the light emitted by particles plunging into the black hole leads to strong redshift near the inner shadow. Finally, we study the polarization images under synchrotron radiation and find that the polarization intensity P_o reaches its maximum around the lensed image and higher-order images, whereas no polarization vectors appear within the inner shadow. This stands in sharp contrast to horizonless compact objects. These findings contribute to a deeper understanding of the shadow properties of charged black holes within Lorentz-violating gravity.

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Forward citations

Cited by 9 Pith papers

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  2. Polarized Radiative Transfer of Kerr-Newman Black Hole

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  3. Optical Images of the Braneworld Black Hole Surrounded by an Optically Thin Accretion Disk

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    A rotating braneworld black hole with tidal charge casts an asymmetric, spin- and q-dependent shadow; with the adopted disk emissivity, its 86 GHz image is brighter than its 230 GHz image.

  4. Distinct Near-Horizon Trend of Synchrotron Polarization in Kerr Spacetime

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    Near-horizon synchrotron polarization in Kerr spacetime admits a distinct analytic form where the leading-order pattern depends only on spin and polar angle under stationary axisymmetric degenerate EM field assumptions.

  5. Beyond general relativity: gravitational waves in non-minimally coupled theories

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    A generalized propagation parameterization for gravitational-wave strains is extended to O(H²) and O(H′), then mapped to Kalb-Ramond, axion-dilaton–Chern-Simons–Gauss-Bonnet, and U(1) dark-photon models.

  6. Semi-analytical Study on the Polarized Images of Black Hole due to Frame Dragging

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    Frame dragging flips an initially retrograde equatorial flow around a Kerr black hole, and the polarized image shows three distinct critical locations whose spatial hierarchy is derived analytically for an on-axis observer.

  7. Imprints of Black Hole Shadows and Polarization Patterns of Various Thick Disks: Bumblebee gravity

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    Bumblebee gravity parameters ℓ and Q shrink central dark regions, enhance brightness asymmetry, and alter polarization patterns in thick-disk images of Kerr-Sen-like black holes.

  8. Unveiling Inner Shadows and Polarization Signatures of Rotating Einstein-Gauss-Bonnet Black Holes

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Reference graph

Works this paper leans on

69 extracted references · 1 canonical work pages · cited by 9 Pith papers

  1. [1]

    Akbaba, D

    D. Akbaba, D. Lange, M. Correll, A. Lex, and M. Meyer. Troubling col- laboration: Matters of care for visualization design study. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems, article no. 812, pp. 1–15, 2023. 1, 2

    2023

  2. [2]

    Amershi, D

    S. Amershi, D. Weld, M. V orvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. N. Bennett, K. Inkpen, et al. Guidelines for human- AI interaction. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2019. 3

    2019

  3. [3]

    Andrienko, N

    G. Andrienko, N. Andrienko, W. Chen, R. Maciejewski, and Y . Zhao. Visual analytics of mobility and transportation: State of the art and further research directions.IEEE Transactions on Intelligent Transportation Systems, 18(8):2232–2249, 2017. 2

    2017

  4. [4]

    R. C. Basole and T. Major. Generative AI for visualization: Opportunities and challenges.IEEE Computer Graphics and Applications, 44(2):55–64,

  5. [5]

    Boorboor, Y

    S. Boorboor, Y . Kim, P. Hu, J. M. Moses, B. A. Colle, and A. E. Kaufman. Submerse: Visualizing storm surge flooding simulations in immersive display ecologies.IEEE Transactions on Visualization and Computer Graphics, 30(9):6365–6377, 2024. 8

    2024

  6. [6]

    Büschel, A

    W. Büschel, A. Lehmann, and R. Dachselt. MIRIA: A mixed reality toolkit for the in-situ visualization and analysis of spatio-temporal interaction data. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems, article no. 470, pp. 1–15, 2021. 3

    2021

  7. [7]

    S. P. Callahan, J. Freire, E. Santos, C. E. Scheidegger, C. T. Silva, and H. T. V o. VisTrails: Visualization meets data management. InProceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 745–747, 2006. 3

    2006

  8. [8]

    J. Chen, Q. Huang, C. Wang, and C. Li. SenseMap: Urban performance visualization and analytics via semantic textual similarity.IEEE Transac- tions on Visualization and Computer Graphics, 30(9):6275–6290, 2024. 3

    2024

Show all 69 references
  1. [9]

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockman, et al. Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374, 2021. 2

    2021 arXiv

  2. [10]

    C. V . F. de Souza, S. M. Bonnet, D. de Oliveira, M. Cataldi, F. Miranda, and M. Lage. ProWis: A visual approach for building, managing, and analyzing weather simulation ensembles at runtime.IEEE Transactions on Visualization and Computer Graphics, 30(1):738–747, 2024. 8

    2024

  3. [11]

    Z. Deng, D. Weng, J. Chen, R. Liu, Z. Wang, J. Bao, Y . Zheng, and Y . Wu. AirVis: Visual analytics of air pollution propagation.IEEE Transactions on Visualization and Computer Graphics, 26(1):800–810, 2019. 3

    2019

  4. [12]

    Z. Deng, D. Weng, S. Liu, Y . Tian, M. Xu, and Y . Wu. A survey of urban visual analytics: Advances and future directions.Computational Visual Media, 9(1):3–39, 2023. 1, 2

    2023

  5. [13]

    J. Dong, H. Zhang, M. Cui, Y . Lin, H.-Y . Wu, and C. Bi. TCEVis: Visual analytics of traffic congestion influencing factors based on explainable machine learning.Visual Informatics, 8(1):56–66, 2024. 3

    2024

  6. [14]

    Elmqvist, J

    N. Elmqvist, J. Stasko, and P. Tsigas. DataMeadow: A visual canvas for analysis of large-scale multivariate data. In2007 IEEE Symposium on Visual Analytics Science and Technology, pp. 187–194. IEEE, 2007. 3

    2007

  7. [15]

    Z. Feng, H. Qu, S.-H. Yang, Y . Ding, and J. Song. A survey of visual analytics in urban area.Expert Systems, 39(9):e13065, 2022. 1, 2

    2022

  8. [16]

    Ferreira, G

    L. Ferreira, G. Moreira, M. Hosseini, M. Lage, N. Ferreira, and F. Miranda. Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systems.Computers & Graphics, 123:104013, 2024. 1, 2, 3

    2024

  9. [17]

    Ferreira, M

    N. Ferreira, M. Lage, H. Doraiswamy, H. V o, L. Wilson, H. Werner, M. Park, and C. Silva. Urbane: A 3D framework to support data driven decision making in urban development. In2015 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 97–104. IEEE, 2015. 9

    2015

  10. [18]

    Ferreira, J

    N. Ferreira, J. Poco, H. T. V o, J. Freire, and C. T. Silva. Visual exploration of big spatio-temporal urban data: A study of New York City taxi trips. IEEE Transactions on Visualization and Computer Graphics, 19(12):2149– 2158, 2013. 9

    2013

  11. [19]

    Garcia, J

    G. Garcia, J. Silveira, J. Poco, A. Paiva, M. B. Nery, C. T. Silva, S. Adorno, and L. G. Nonato. CrimAnalyzer: Understanding crime patterns in São Paulo.IEEE Transactions on Visualization and Computer Graphics, 27(4):2313–2328, 2019. 1, 3

    2019

  12. [20]

    Javed and N

    W. Javed and N. Elmqvist. ExPlates: Spatializing interactive analysis to scaffold visual exploration.Computer Graphics Forum, 32(3pt4):441–450,

  13. [21]

    Jänicke, P

    S. Jänicke, P. Kaur, P. Kuzmicki, and J. Schmidt. Participatory visu- alization design as an approach to minimize the gap between research and application. In C. Gillmann, M. Krone, G. Reina, and T. Wischgoll, eds.,VisGap - The Gap between Visualization Research and Visualizat...

    2020

  14. [22]

    Konev, J

    A. Konev, J. Waser, B. Sadransky, D. Cornel, R. A. Perdigão, Z. Horváth, and M. E. Gröller. Run Watchers: Automatic simulation-based decision support in flood management.IEEE Transactions on Visualization and Computer Graphics, 20(12):1873–1882, 2014. 9

    2014

  15. [23]

    S. Lau, I. Drosos, J. M. Markel, and P. J. Guo. The design space of computational notebooks: An analysis of 60 systems in academia and industry. In2020 IEEE Symposium on Visual Languages and Human- Centric Computing (VL/HCC), pp. 1–11. IEEE, 2020. 2

    2020

  16. [24]

    J. Li, S. Chen, K. Zhang, G. Andrienko, and N. Andrienko. COPE: Interactive exploration of co-occurrence patterns in spatial time series. IEEE Transactions on Visualization and Computer Graphics, 25(8):2554– 2567, 2019. 3

    2019

  17. [25]

    S. L’Yi, A. van den Brandt, E. Adams, H. N. Nguyen, and N. Gehlen- borg. Learnable and expressive visualization authoring through blended interfaces.IEEE Transactions on Visualization and Computer Graphics, 31(1):459–469, 2025. 2, 3

    2025

  18. [26]

    S. L’Yi, Q. Wang, F. Lekschas, and N. Gehlenborg. Gosling: A grammar- based toolkit for scalable and interactive genomics data visualization.IEEE Transactions on Visualization and Computer Graphics, 28(1):140–150,

  19. [27]

    Y . Lyu, H. Lu, M. K. Lee, G. Schmitt, and B. Y . Lim. IF-City: Intelligible fair city planning to measure, explain and mitigate inequality.IEEE Transactions on Visualization and Computer Graphics, 30(7):3749–3766,

  20. [28]

    McKenna, D

    S. McKenna, D. Mazur, J. Agutter, and M. Meyer. Design activity frame- work for visualization design.IEEE Transactions on Visualization and Computer Graphics, 20(12):2191–2200, 2014. 3

    2014

  21. [29]

    A. M. McNutt. No grammar to rule them all: A survey of JSON-style DSLs for visualization.IEEE Transactions on Visualization and Computer Graphics, 29(1):160–170, 2023. 2

    2023

  22. [30]

    Minaee, T

    S. Minaee, T. Mikolov, N. Nikzad, M. Chenaghlu, R. Socher, X. Am- atriain, and J. Gao. Large language models: A survey.arXiv preprint arXiv:2402.06196, 2025. 2

    2025 arXiv

  23. [31]

    Miranda, H

    F. Miranda, H. Doraiswamy, M. Lage, L. Wilson, M. Hsieh, and C. T. Silva. Shadow Accrual Maps: Efficient accumulation of city-scale shadows over time.IEEE Transactions on Visualization and Computer Graphics, 25(3):1559–1574, 2018. 1

    2018

  24. [32]

    Miranda, H

    F. Miranda, H. Doraiswamy, M. Lage, K. Zhao, B. Gonçalves, L. Wilson, M. Hsieh, and C. T. Silva. Urban Pulse: Capturing the rhythm of cities. IEEE Transactions on Visualization and Computer Graphics, 23(1):791– 800, 2016. 3, 8

    2016

  25. [33]

    Miranda, M

    F. Miranda, M. Hosseini, M. Lage, H. Doraiswamy, G. Dove, and C. T. Silva. Urban Mosaic: Visual exploration of streetscapes using large-scale image data. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–15, 2020. 3

    2020

  26. [34]

    Miranda, T

    F. Miranda, T. Ortner, G. Moreira, M. Hosseini, M. Vuckovic, F. Biljecki, C. T. Silva, M. Lage, and N. Ferreira. The state of the art in visual analytics for 3D urban data.Computer Graphics Forum, 43(3):e15112, 2024. 1, 2, 3

    2024

  27. [35]

    Moreira, M

    G. Moreira, M. Hosseini, M. N. A. Nipu, M. Lage, N. Ferreira, and F. Mi- randa. The Urban Toolkit: A grammar-based framework for urban visual analytics.IEEE Transactions on Visualization and Computer Graphics, 30(1):1402–1412, 2024. 2, 3, 4, 6

    2024

  28. [36]

    Moreira, M

    G. Moreira, M. Hosseini, C. Veiga, L. Alexandre, N. Colaninno, D. de Oliveira, N. Ferreira, M. Lage, and F. Miranda. Curio: A dataflow- based framework for collaborative urban visual analytics.IEEE Transac- tions on Visualization and Computer Graphics, 31(1):1224–1234, 2025. 2...

    2025

  29. [37]

    Munzner.Visualization analysis and design

    T. Munzner.Visualization analysis and design. CRC press, 2014. 2

    2014

  30. [38]

    J. Nielsen. AI: First new UI paradigm in 60 years. ������������ ��������������������������������� , 2023. Accessed: 2025-03-

    2023

  31. [39]

    R. Qiu, Y . Tu, Y .-S. Wang, P.-Y . Yen, and H.-W. Shen. DocFlow: A visual analytics system for question-based document retrieval and categorization. IEEE Transactions on Visualization and Computer Graphics, 30(2):1533– 1548, 2024. 3

    2024

  32. [40]

    S. Sah, R. Mitra, A. Narechania, A. Endert, J. Stasko, and W. Dou. Gener- ating analytic specifications for data visualization from natural language queries using large language models.arXiv preprint arXiv:2408.13391,

    arXiv

  33. [41]

    M. Saha, M. Saugstad, H. T. Maddali, A. Zeng, R. Holland, S. Bower, A. Dash, S. Chen, A. Li, K. Hara, et al. Project Sidewalk: A web-based crowdsourcing tool for collecting sidewalk accessibility data at scale. In Proceedings of the 2019 CHI Conference on Human Factors in Comp...

    2019

  34. [42]

    Salinas, T

    K. Salinas, T. Gonçalves, V . Barella, T. Vieira, and L. G. Nonato. CityHub: A library for urban data integration. In2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), vol. 1, pp. 43–48, 2022. 3

    2022

  35. [43]

    L. Shen, E. Shen, Y . Luo, X. Yang, X. Hu, X. Zhang, Z. Tai, and J. Wang. Towards natural language interfaces for data visualization: A survey.IEEE Transactions on Visualization and Computer Graphics, 29(6):3121–3144,

  36. [44]

    Sicat, J

    R. Sicat, J. Li, J. Choi, M. Cordeil, W.-K. Jeong, B. Bach, and H. Pfister. DXR: A toolkit for building immersive data visualizations.IEEE Trans- actions on Visualization and Computer Graphics, 25(1):715–725, 2019. 2

    2019

  37. [45]

    C. T. Silva, E. Anderson, E. Santos, and J. Freire. Using VisTrails and provenance for teaching scientific visualization.Computer Graphics Forum, 30(1):75–84, 2011. 3

    2011

  38. [46]

    C. T. Silva, J. Freire, and S. P. Callahan. Provenance for visualizations: Re- producibility and beyond.Computing in Science & Engineering, 9(5):82– 89, 2007. 2

    2007

  39. [47]

    Silva, D

    V . Silva, D. de Oliveira, P. Valduriez, and M. Mattoso. Dfanalyzer: Run- time dataflow analysis of scientific applications using provenance.Pro- ceedings of the VLDB Endowment, 11(12):2082–2085, 2018. 3

  40. [48]

    Spinner, U

    T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady. explAIner: A visual analytics framework for interactive and explainable machine learning. IEEE Transactions on Visualization and Computer Graphics, 26(1):1064– 1074, 2020. 2

    2020

  41. [49]

    Steyvers, H

    M. Steyvers, H. Tejeda, A. Kumar, C. Belem, S. Karny, X. Hu, L. W. Mayer, and P. Smyth. What large language models know and what people think they know.Nature Machine Intelligence, pp. 1–11, 2025. 2

    2025

  42. [50]

    Z. Sun, D. Xu, Y . Zhang, Y . Qi, Y . Wang, Z. Zuo, Z. Wang, Y . Li, X. Li, Q. Lu, W. Peng, and S. Guo. BigDataflow: A distributed interproce- dural dataflow analysis framework. InProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the F...

    2023

  43. [51]

    X. Tang, E. Krokos, C. Liu, K. Davidson, K. Whitley, N. Ramakrishnan, and C. North. Steering LLM summarization with visual workspaces for sensemaking.arXiv preprint arXiv:2409.17289, 2024. 3

    2024 arXiv

  44. [52]

    Terry, C

    M. Terry, C. Kulkarni, M. Wattenberg, L. Dixon, and M. R. Morris. In- teractive AI alignment: Specification, process, and evaluation alignment. arXiv preprint arXiv:2311.00710, 2023. 2, 3, 4

    2023 arXiv

  45. [53]

    Y . Tian, W. Cui, D. Deng, X. Yi, Y . Yang, H. Zhang, and Y . Wu. ChartGPT: Leveraging LLMs to generate charts from abstract natural language.IEEE Transactions on Visualization and Computer Graphics, 31(3):1731–1745,

  46. [54]

    Ulbrich, M

    P. Ulbrich, M. Waldner, K. Furmanová, S. M. Marques, D. Bednáˇr, B. Ko- zlíková, and J. Byška. sMolBoxes: Dataflow model for molecular dy- namics exploration.IEEE Transactions on Visualization and Computer Graphics, 29(1):581–590, 2022. 3

    2022

  47. [55]

    Walker, A

    R. Walker, A. Slingsby, J. Dykes, K. Xu, J. Wood, P. H. Nguyen, D. Stephens, B. W. Wong, and Y . Zheng. An extensible framework for provenance in human terrain visual analytics.IEEE Transactions on Visualization and Computer Graphics, 19(12):2139–2148, 2013. 2

    2013

  48. [56]

    S. Wang, F. Lyu, S. Wang, C. E. Catlett, A. Padmanabhan, and K. Soltani. Integrating CyberGIS and urban sensing for reproducible streaming ana- lytics.Urban Informatics, pp. 663–681, 2021. 3

    2021

  49. [57]

    Waser, H

    J. Waser, H. Ribicic, R. Fuchs, C. Hirsch, B. Schindler, G. Bloschl, and E. Groller. Nodes on Ropes: A comprehensive data and control flow for steering ensemble simulations.IEEE Transactions on Visualization and Computer Graphics, 17(12):1872–1881, 2011. 3

    2011

  50. [58]

    A. Wu, D. Deng, M. Chen, S. Liu, D. Keim, R. Maciejewski, S. Miksch, H. Strobelt, F. Viégas, and M. Wattenberg. Grand challenges in visual ana- lytics applications.IEEE Computer Graphics and Applications, 43(5):83– 90, 2023. 1, 2, 3

    2023

  51. [59]

    A. Wu, D. Deng, F. Cheng, Y . Wu, S. Liu, and H. Qu. In defence of visual analytics systems: Replies to critics.IEEE Transactions on Visualization and Computer Graphics, 29(1):1026–1036, 2023. 1

    2023

  52. [60]

    Xia and X

    X. Xia and X. Ming. SynxFlow: Synergising high-performance hazard simulation with data flow, 2025. 8

    2025

  53. [61]

    T. Xie, Y . Ma, J. Kang, H. Tong, and R. Maciejewski. FairRankVis: A visual analytics framework for exploring algorithmic fairness in graph min- ing models.IEEE Transactions on Visualization and Computer Graphics, 28(1):368–377, 2022. 3

    2022

  54. [62]

    Q. Yang, A. Steinfeld, C. Rosé, and J. Zimmerman. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2020. 3

    2020

  55. [63]

    W. Yap, P. Janssen, and F. Biljecki. Free and open source urbanism: Software for urban planning practice.Computers, Environment and Urban Systems, 96:101825, 2022. 3

    2022

  56. [64]

    Yu and C

    B. Yu and C. T. Silva. VisFlow-web-based visualization framework for tabular data with a subset flow model.IEEE Transactions on Visualization and Computer Graphics, 23(1):251–260, 2016. 3

    2016

  57. [65]

    Yu and C

    B. Yu and C. T. Silva. FlowSense: A natural language interface for visual data exploration within a dataflow system.IEEE Transactions on Visualization and Computer Graphics, 26(1):1–11, 2020. 3

    2020

  58. [66]

    Y . Zhao, Y . Zhang, Y . Zhang, X. Zhao, J. Wang, Z. Shao, C. Turkay, and S. Chen. LEV A: Using large language models to enhance visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2024. 2, 3

    2024

  59. [67]

    Zheng, L

    Y . Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: Concepts, methodologies, and applications.ACM Transactions on Intelligent Systems and Technology, 5(3):1–55, 2014. 2

    2014

  60. [68]

    Zheng, W

    Y . Zheng, W. Wu, Y . Chen, H. Qu, and L. M. Ni. Visual analytics in urban computing: An overview.IEEE Transactions on Big Data, 2(3):276–296,

  61. [69]

    Ziegler and S

    P. Ziegler and S. E. Chasins. A need-finding study with users of geospatial data. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems, article no. 862, pp. 1–16, 2023. 3

    2023

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