Pith. sign in

REVIEW 2 major objections 145 references

Topic models capture either taxonomic similarity or thematic relatedness, and those two scores predict which downstream tasks they help or hurt.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.5

2026-07-14 23:27 UTC pith:ACESX7JK

load-bearing objection Wrong full text was supplied (cosmology 2603.10622 instead of the topic-model paper), so the central claim is unevaluable beyond the abstract. the 2 major comments →

arxiv 2603.10619 v2 pith:ACESX7JK submitted 2026-03-11 cs.CL

Disentangling Similarity and Relatedness in Topic Models

Hanlin Xiao , Yang Wang , Mauricio A. \'Alvarez , Rainer Breitling This is my paper
classification cs.CL
keywords topic modelstaxonomic similaritythematic relatednesspretrained language modelstopic evaluationpsycholinguisticsdownstream taskssemantic structure
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.

Classical co-occurrence topic models and models that fold in large pre-trained language models do not just differ in quality; they capture different kinds of meaning. The paper separates those kinds along two psycholinguistic axes: taxonomic similarity (dog/wolf) and thematic relatedness (dog/bone). The authors build a large synthetic word-pair benchmark with LLM labels, train a neural scorer on it, and use that scorer to place topic models in a joint similarity–relatedness space. Across corpora and model families, different families land in different regions of that space. The two scores then predict task results: similarity-rich topics help similarity tasks and can hurt relatedness tasks, and the reverse holds for relatedness-rich topics. Neither axis is always better, so measuring both becomes a practical, model-agnostic way to diagnose what semantic structure a topic model has actually learned.

Core claim

Different topic-model families occupy distinct positions in a joint taxonomic-similarity and thematic-relatedness space, and those two scores predict downstream performance: tasks that need similarity benefit from similarity-rich topics, tasks that need relatedness benefit from relatedness-rich topics, and excess on either axis degrades performance on tasks aligned with the other.

What carries the argument

A neural scorer trained on an LLM-annotated synthetic word-pair benchmark that scores topic words on two axes—taxonomic similarity and thematic relatedness—and places each topic model in that joint space as a diagnostic.

Load-bearing premise

The method treats LLM labels of synthetic word pairs as reliable ground truth for similarity versus relatedness; if those labels mix the two axes or carry the annotator model’s biases, every placement and every task prediction rests on a skewed yardstick.

What would settle it

Show a topic model that scores high on relatedness and low on similarity yet still beats similarity-rich models on a controlled taxonomic-similarity task (or the reverse on a relatedness task); that would break the claim that the two scores predict task alignment.

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

If this is right

  • Similarity-rich topics improve similarity-aligned tasks and can degrade relatedness-aligned tasks.
  • Relatedness-rich topics improve relatedness-aligned tasks and can degrade similarity-aligned tasks.
  • Neither semantic axis is uniformly beneficial; excess on one axis hurts the other.
  • Model choice can be guided by where a family sits in the joint similarity–relatedness plane rather than by a single quality score.
  • The same two-axis diagnostic applies across classical co-occurrence and PLM-augmented topic models.

Where Pith is reading between the lines

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

  • Applications may need to select or train topic models for the specific semantic axis they care about instead of maximising generic coherence.
  • Because the benchmark is LLM-annotated, the scorer may partly recycle the same preferences as the PLMs under evaluation, so the diagnostic is not fully independent of those models.
  • The same two-axis lens could be applied to other embedding, clustering, or lexicon methods beyond topic models.
  • Pairwise word scores may miss multi-word or hierarchical topic structure that a richer probe could reveal.

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

2 major / 0 minor

Summary. The submission is presented as a computational-linguistics paper (arXiv:2603.10619) that formalizes thematic relatedness versus taxonomic similarity for topic models, builds an LLM-annotated synthetic word-pair benchmark, trains a neural scorer, places topic-model families in a joint similarity–relatedness space, and links those scores to downstream task performance. The body of the manuscript actually supplied, however, is an unrelated cosmology paper on Lagrangian entropy couplings between dark matter and scalar-field dark energy (arXiv:2603.10622). None of the claimed methods, benchmark construction, scorer training, multi-corpus placements, or task results appear in the provided text. The abstract’s central claims are therefore unevaluable from the manuscript as given.

Significance. If the abstract’s program were carried through with a correct, self-contained manuscript—reliable separation of similarity and relatedness, a validated scorer, and predictive links to task performance—it would be a useful model-agnostic diagnostic for PLM-augmented topic models. That significance cannot be assessed here: the supplied full text contains no topic-model experiments, no psycholinguistic axes, and no NLP results. The mismatch is not a minor presentation issue; it prevents any scientific evaluation of the claimed contribution.

major comments (2)
  1. Title, abstract, and paper_id (2603.10619, cs.CL) describe a topic-modeling study on similarity vs. relatedness. The full manuscript text is instead “Interacting dark sector from intrinsic entropy couplings” (astro-ph.CO), with sections on Brown fluid actions, entropic-CDM, CLASS modifications, and CMB/LSS observables. No equations, datasets, baselines, or results for the abstract’s claims exist in the body. The central claim is therefore unsupported by the submitted manuscript and cannot be stress-tested.
  2. Because the body is the wrong paper, load-bearing elements of the abstract—LLM annotation quality for taxonomic similarity vs. thematic relatedness, training of the neural scorer, multi-corpus placement of model families, and prediction of downstream task performance—have zero verifiable support. Circularity risks (LLM labels used to score PLM-based topic models) and reliability of the synthetic benchmark cannot be checked until the correct full text is provided.

Circularity Check

0 steps flagged

No significant circularity in the supplied full text: a Lagrangian construction of pure-entropy dark-sector couplings with freely chosen phenomenological entropy spectrum and numerical exploration, not forced predictions.

full rationale

The CACHEABLE full manuscript is the cosmology paper “Interacting dark sector from intrinsic entropy couplings” (arXiv:2603.10622), not the topic-model paper named in the abstract header (2603.10619). On the text that is actually present, the derivation chain is: Brown perfect-fluid action + algebraic/derivative entropy–scalar couplings → variational EOM and coupling current → background unchanged by construction of pure-entropy f(s,φ,S) → linear pure-momentum exchange in the Euler equation → specialization to barotropic entropic-CDM → free phenomenological Ps(k) for frozen δs → CLASS numerics of P(k), CMB, σ8. Background invariance and pure-momentum transfer are theorems of the chosen Lagrangian, not re-labelings of fitted data. The entropy power spectrum is explicitly phenomenological (amplitude, tilt, UV cut-off chosen by hand), not claimed as a first-principles prediction of observations. Self-citations (Brown, Pourtsidou Type-3, Boehmer algebraic/derivative quintessence, GDM) supply prior frameworks that are extended, not load-bearing uniqueness theorems that force the present results. No step reduces a claimed prediction to its own fitted input or to a self-citation chain. The abstract-level concern about LLM-annotated similarity/relatedness benchmarks for PLM topic models cannot be assessed: that paper’s methods and equations are absent from the supplied manuscript. Honest finding: score 0, no circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 1 invented entities

Abstract-only review of a cs.CL topic-modeling paper whose cached full text is a mismatched cosmology manuscript. Ledger entries are those the abstract’s central claim requires: psycholinguistic axes as the right structure, LLM annotation as valid ground truth, a trainable scorer as a faithful measure, and the existence of multi-corpus multi-family experiments. No free parameters or invented physical entities can be enumerated from the abstract; the main invented construct is the dual-axis scorer/benchmark itself.

axioms (3)
  • domain assumption Thematic relatedness and taxonomic similarity are the two primary, separable semantic axes that distinguish classical co-occurrence topic models from PLM-augmented ones.
    Abstract formalizes the distinction along these two psycholinguistic axes and treats them as the explanatory structure for model-family differences.
  • ad hoc to paper LLM-based annotation of synthetic word pairs yields labels sufficiently accurate to train a neural scorer for similarity and relatedness of topic words.
    Abstract: “construct a large synthetic benchmark of word pairs using LLM-based annotation and train a neural scorer on it.” This is a paper-specific methodological premise, not a standard theorem.
  • domain assumption Downstream tasks can be cleanly classified as requiring similarity versus requiring relatedness so that topic-axis scores predict task performance.
    Abstract claims tasks requiring similarity benefit from similarity-rich topics and the converse for relatedness; that mapping is assumed usable as evaluation.
invented entities (1)
  • Neural similarity–relatedness scorer trained on LLM-annotated synthetic word pairs no independent evidence
    purpose: To place topic models in a joint similarity–relatedness space and predict downstream task performance from topic word lists.
    The scorer/benchmark is the paper’s central measurement instrument; independent evidence of validity (e.g., human correlation) is not available from the abstract.

pith-pipeline@v1.1.0-grok45 · 41197 in / 2979 out tokens · 33092 ms · 2026-07-14T23:27:16.579335+00:00 · methodology

0 comments
read the original abstract

The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occurrence models such as Latent Dirichlet Allocation (LDA) not only in performance, but also in the type of semantic structure they capture. We formalize this distinction along two psycholinguistic axes: thematic relatedness (dog/bone) and taxonomic similarity (dog/wolf). To measure both axes over topic words, we construct a large synthetic benchmark of word pairs using LLM-based annotation and train a neural scorer on it. Across multiple corpora and model families, the scorer places different topic-model families at distinct positions within the joint similarity-relatedness space. The two scores further predict downstream task performance: tasks requiring similarity benefit from similarity-rich topics, whereas tasks requiring relatedness benefit from the converse, and excessive emphasis on either axis degrades performance on tasks aligned with the opposing semantic structure. Neither axis is uniformly beneficial. Measuring both therefore provides a practical, model-agnostic diagnostic for evaluating the semantic structure captured by topic models.

Figures

Figures reproduced from arXiv: 2603.10619 by Hanlin Xiao, Mauricio A. \'Alvarez, Rainer Breitling, Yang Wang.

Figure 1
Figure 1. Figure 1: FIG. 1: Comparison of the entropy-induced source terms entering the dark matter growth equation in the [PITH_FULL_IMAGE:figures/full_fig_p015_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: Relative deviations of the entropic-CDM perturbations and clustering amplitude with respect to ΛCDM [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: Relative deviations in the matter power spectrum [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: Early-time scaling of the numerical solutions for [PITH_FULL_IMAGE:figures/full_fig_p027_4.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

145 extracted references · 127 linked inside Pith

  1. [1]

    (38) for cosmological perturbations

    Coupling current and evolution equations Let us now examine the coupling currentJ µ of Eq. (38) for cosmological perturbations. It is straightforward to calculate this to linear order in cosmological perturbations δJ0 = 0, δJ i = h0 ˆV,ϕ −g ,s ∂iδs ,(63) where we have used the entropy equation, Eq. (52), to eliminates ′ andδs ′. We therefore see that the ...

  2. [2]

    fifth-force

    Dynamical equations For the linear perturbations of the scalar field’s equation of motion, we find additional source terms resulting from the entropy couplings δϕ′′ + 2Hδϕ′ + (k2 +a 2 ˆV,ϕϕ)δϕ=−2a 2Φ ˆV,ϕ +ϕ ′(Φ′ + 3Ψ′)− a2g,sϕ +k 2h0 δs ,(67) where we have expanded in terms of Fourier modes and used the background solutions in Eq. (42). The new modified ...

    2022

  3. [3]

    Perlmutteret al.(Supernova Cosmology Project), Astrophys

    S. Perlmutteret al.(Supernova Cosmology Project), Astrophys. J.517, 565 (1999), arXiv:astro-ph/9812133

    Pith/arXiv arXiv 1999

  4. [4]

    A. G. Riesset al.(Supernova Search Team), Astron. J.116, 1009 (1998), arXiv:astro-ph/9805201

    Pith/arXiv arXiv 1998

  5. [5]

    Weinberg, Rev

    S. Weinberg, Rev. Mod. Phys.61, 1 (1989)

    1989

  6. [6]

    Martin, Comptes Rendus Physique13, 566 (2012), arXiv:1205.3365 [astro-ph.CO]

    J. Martin, Comptes Rendus Physique13, 566 (2012), arXiv:1205.3365 [astro-ph.CO]

    Pith/arXiv arXiv 2012

  7. [7]

    A. G. Riesset al., Astrophys. J. Lett.934, L7 (2022), arXiv:2112.04510 [astro-ph.CO]

    Pith/arXiv arXiv 2022

  8. [8]

    Casertanoet al.(H0DN), (2025), arXiv:2510.23823 [astro-ph.CO]

    S. Casertanoet al.(H0DN), (2025), arXiv:2510.23823 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  9. [9]

    Heymanset al., Astron

    C. Heymanset al., Astron. Astrophys.646, A140 (2021), arXiv:2007.15632 [astro-ph.CO]

    Pith/arXiv arXiv 2021

  10. [10]

    A. H. Wrightet al., Astron. Astrophys.703, A158 (2025), arXiv:2503.19441 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  11. [11]

    T. M. C. Abbottet al.(DES), Phys. Rev. D112, 083535 (2025), arXiv:2503.13632 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  12. [12]

    Verde, T

    L. Verde, T. Treu, and A. G. Riess, Nature Astron.3, 891 (2019), arXiv:1907.10625 [astro-ph.CO]

    Pith/arXiv arXiv 2019

  13. [13]

    Abdallaet al., JHEAp34, 49 (2022), arXiv:2203.06142 [astro-ph.CO]

    E. Abdallaet al., JHEAp34, 49 (2022), arXiv:2203.06142 [astro-ph.CO]

    Pith/arXiv arXiv 2022

  14. [14]

    Di Valentinoet al.(CosmoVerse Network), Phys

    E. Di Valentinoet al.(CosmoVerse Network), Phys. Dark Univ.49, 101965 (2025), arXiv:2504.01669 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  15. [15]

    B. Wang, E. Abdalla, F. Atrio-Barandela, and D. Pav´ on, Rept. Prog. Phys.87, 036901 (2024), arXiv:2402.00819 [astro-ph.CO]

    Pith/arXiv arXiv 2024

  16. [16]

    S. M. Carroll, Phys. Rev. Lett.81, 3067 (1998), arXiv:astro-ph/9806099

    Pith/arXiv arXiv 1998

  17. [17]

    Amendola, Phys

    L. Amendola, Phys. Rev. D62, 043511 (2000), arXiv:astro-ph/9908023

    Pith/arXiv arXiv 2000

  18. [18]

    B. Wang, E. Abdalla, F. Atrio-Barandela, and D. Pavon, Rept. Prog. Phys.79, 096901 (2016), arXiv:1603.08299 [astro-ph.CO]

    Pith/arXiv arXiv 2016

  19. [19]

    K. Aoki, J. Beltr´ an Jim´ enez, M. C. Pookkillath, and S. Tsujikawa, Phys. Rev. D113, 044053 (2026), arXiv:2504.17293 [astro-ph.CO]

    arXiv 2026

  20. [20]

    Poulot, E

    G. Poulot, E. M. Teixeira, C. van de Bruck, and N. J. Nunes, JCAP11, 005 (2025), arXiv:2404.10524 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  21. [21]

    van de Bruck, G

    C. van de Bruck, G. Poulot, and E. M. Teixeira, JCAP07, 019 (2023), arXiv:2211.13653 [hep-th]

    Pith/arXiv arXiv 2023

  22. [22]

    Bettoni and S

    D. Bettoni and S. Liberati, Phys. Rev. D88, 084020 (2013), arXiv:1306.6724 [gr-qc]

    Pith/arXiv arXiv 2013

  23. [23]

    Zumalac´ arregui and J

    M. Zumalac´ arregui and J. Garc´ ıa-Bellido, Phys. Rev. D89, 064046 (2014), arXiv:1308.4685 [gr-qc]

    Pith/arXiv arXiv 2014

  24. [24]

    Gubitosi, F

    G. Gubitosi, F. Piazza, and F. Vernizzi, JCAP02, 032 (2013), arXiv:1210.0201 [hep-th]

    Pith/arXiv arXiv 2013

  25. [25]

    Frusciante and L

    N. Frusciante and L. Perenon, Phys. Rept.857, 1 (2020), arXiv:1907.03150 [astro-ph.CO]

    Pith/arXiv arXiv 2020

  26. [26]

    Smith, M

    A. Smith, M. Mylova, P. Brax, C. van de Bruck, C. P. Burgess, and A.-C. Davis, (2024), 10.1088/1475- 7516/2025/07/023, arXiv:2410.11099 [hep-th]

    Pith/arXiv arXiv doi:10.1088/1475- 2024

  27. [27]

    van de Bruck and J

    C. van de Bruck and J. Morrice, JCAP04, 036 (2015), arXiv:1501.03073 [gr-qc]

    Pith/arXiv arXiv 2015

  28. [28]

    M. S. Linton, R. Crittenden, and A. Pourtsidou, JCAP08, 075 (2022), arXiv:2107.03235 [astro-ph.CO]

    Pith/arXiv arXiv 2022

  29. [29]

    Amendola and S

    L. Amendola and S. Tsujikawa, JCAP06, 020 (2020), arXiv:2003.02686 [gr-qc]

    Pith/arXiv arXiv 2020

  30. [30]

    B. J. Barros, Phys. Rev. D99, 064051 (2019), arXiv:1901.03972 [gr-qc]

    Pith/arXiv arXiv 2019

  31. [31]

    Van De Bruck and J

    C. Van De Bruck and J. Mifsud, Phys. Rev. D97, 023506 (2018), arXiv:1709.04882 [astro-ph.CO]

    Pith/arXiv arXiv 2018

  32. [32]

    Di Valentino, A

    E. Di Valentino, A. Melchiorri, and O. Mena, Phys. Rev. D96, 043503 (2017), arXiv:1704.08342 [astro-ph.CO]

    Pith/arXiv arXiv 2017

  33. [33]

    Poulin, K

    V. Poulin, K. K. Boddy, S. Bird, and M. Kamionkowski, Phys. Rev. D97, 123504 (2018), arXiv:1803.02474 [astro-ph.CO]. 28

    Pith/arXiv arXiv 2018

  34. [34]

    Di Valentino, A

    E. Di Valentino, A. Melchiorri, O. Mena, and S. Vagnozzi, Phys. Rev. D101, 063502 (2020), arXiv:1910.09853 [astro-ph.CO]

    Pith/arXiv arXiv 2020

  35. [35]

    E. M. Teixeira, G. Poulot, C. van de Bruck, E. Di Valentino, and V. Poulin, Phys. Rev. D113, 023514 (2026), arXiv:2412.14139 [astro-ph.CO]

    Pith/arXiv arXiv 2026

  36. [36]

    Di Valentino, A

    E. Di Valentino, A. Melchiorri, O. Mena, and S. Vagnozzi, Phys. Dark Univ.30, 100666 (2020), arXiv:1908.04281 [astro-ph.CO]

    Pith/arXiv arXiv 2020

  37. [37]

    G´ omez-Valent, V

    A. G´ omez-Valent, V. Pettorino, and L. Amendola, Phys. Rev. D101, 123513 (2020), arXiv:2004.00610 [astro- ph.CO]

    Pith/arXiv arXiv 2020

  38. [38]

    Pourtsidou and T

    A. Pourtsidou and T. Tram, Phys. Rev. D94, 043518 (2016), arXiv:1604.04222 [astro-ph.CO]

    Pith/arXiv arXiv 2016

  39. [39]

    Lucca and D

    M. Lucca and D. C. Hooper, Phys. Rev. D102, 123502 (2020), arXiv:2002.06127 [astro-ph.CO]

    Pith/arXiv arXiv 2020

  40. [40]

    Becker, D

    N. Becker, D. C. Hooper, F. Kahlhoefer, J. Lesgourgues, and N. Sch¨ oneberg, JCAP02, 019 (2021), arXiv:2010.04074 [astro-ph.CO]

    Pith/arXiv arXiv 2021

  41. [41]

    Castello, M

    S. Castello, M. Mancarella, N. Grimm, D. Sobral-Blanco, I. Tutusaus, and C. Bonvin, JCAP05, 003 (2024), arXiv:2311.14425 [astro-ph.CO]

    Pith/arXiv arXiv 2024

  42. [42]

    Tsedrik and B

    M. Tsedrik and B. Bose, (2025), arXiv:2512.17684 [astro-ph.CO]

    Pith/arXiv arXiv 2025

  43. [43]

    L. W. K. Goh, I. Ocampo, S. Nesseris, and V. Pettorino, Astron. Astrophys.692, A101 (2024), arXiv:2411.04058 [astro-ph.CO]

    Pith/arXiv arXiv 2024

  44. [44]

    E. M. Teixeira, B. J. Barros, V. M. C. Ferreira, and N. Frusciante, JCAP11, 059 (2022), arXiv:2207.13682 [astro-ph.CO]

    Pith/arXiv arXiv 2022

  45. [45]

    Pourtsidou, C

    A. Pourtsidou, C. Skordis, and E. J. Copeland, Phys. Rev. D88, 083505 (2013), arXiv:1307.0458 [astro-ph.CO]

    Pith/arXiv arXiv 2013

  46. [46]

    Beltr´ an Jim´ enez, E

    J. Beltr´ an Jim´ enez, E. Di Dio, and D. Figueruelo, JCAP11, 088 (2023), arXiv:2212.08617 [astro-ph.CO]

    Pith/arXiv arXiv 2023

  47. [47]

    Poulin, J

    V. Poulin, J. L. Bernal, E. D. Kovetz, and M. Kamionkowski, Phys. Rev. D107, 123538 (2023), arXiv:2209.06217 [astro-ph.CO]

    Pith/arXiv arXiv 2023

  48. [48]

    Kumar and R

    S. Kumar and R. C. Nunes, Eur. Phys. J. C77, 734 (2017), arXiv:1709.02384 [astro-ph.CO]

    Pith/arXiv arXiv 2017

  49. [49]

    Baldi and F

    M. Baldi and F. Simpson, Mon. Not. Roy. Astron. Soc.449, 2239 (2015), arXiv:1412.1080 [astro-ph.CO]

    Pith/arXiv arXiv 2015

  50. [50]

    Simpson, Phys

    F. Simpson, Phys. Rev. D82, 083505 (2010), arXiv:1007.1034 [astro-ph.CO]

    Pith/arXiv arXiv 2010

  51. [51]

    Asghari, J

    M. Asghari, J. Beltr´ an Jim´ enez, S. Khosravi, and D. F. Mota, JCAP04, 042 (2019), arXiv:1902.05532 [astro- ph.CO]

    Pith/arXiv arXiv 2019

  52. [52]

    Beltr´ an Jim´ enez, D

    J. Beltr´ an Jim´ enez, D. Bettoni, D. Figueruelo, F. A. Teppa Pannia, and S. Tsujikawa, JCAP03, 085 (2021), arXiv:2012.12204 [astro-ph.CO]

    Pith/arXiv arXiv 2021

  53. [53]

    Beltr´ an Jim´ enez, D

    J. Beltr´ an Jim´ enez, D. Bettoni, D. Figueruelo, F. A. Teppa Pannia, and S. Tsujikawa, Phys. Rev. D104, 103503 (2021), arXiv:2106.11222 [astro-ph.CO]

    Pith/arXiv arXiv 2021

  54. [54]

    Bean and O

    R. Bean and O. Dore, Phys. Rev. D69, 083503 (2004), arXiv:astro-ph/0307100

    Pith/arXiv arXiv 2004

  55. [55]

    Hu, Phys

    W. Hu, Phys. Rev. D71, 047301 (2005), arXiv:astro-ph/0410680

    Pith/arXiv arXiv 2005

  56. [56]

    Cicoli, V

    M. Cicoli, V. Guidetti, F. Muia, F. G. Pedro, and G. P. Vacca, Class. Quant. Grav.39, 195012 (2022), arXiv:2107.12392 [hep-th]

    Pith/arXiv arXiv 2022

  57. [57]

    Arjona, J

    R. Arjona, J. Garc´ ıa-Bellido, and S. Nesseris, Phys. Rev. D102, 103526 (2020), arXiv:2006.01762 [astro-ph.CO]

    Pith/arXiv arXiv 2020

  58. [58]

    Wands, K

    D. Wands, K. A. Malik, D. H. Lyth, and A. R. Liddle, Phys. Rev. D62, 043527 (2000), arXiv:astro-ph/0003278

    Pith/arXiv arXiv 2000

  59. [59]

    K. A. Malik, D. Wands, and C. Ungarelli, Phys. Rev. D67, 063516 (2003), arXiv:astro-ph/0211602

    Pith/arXiv arXiv 2003

  60. [60]

    K. A. Malik and D. Wands, JCAP02, 007 (2005), arXiv:astro-ph/0411703

    Pith/arXiv arXiv 2005

  61. [61]

    Bartolo, P

    N. Bartolo, P. S. Corasaniti, A. R. Liddle, and M. Malquarti, Phys. Rev. D70, 043532 (2004), arXiv:astro- ph/0311503

    arXiv 2004

  62. [62]

    F. S. Bemfica, M. M. Disconzi, and J. Noronha, Phys. Rev. X12, 021044 (2022), arXiv:2009.11388 [gr-qc]

    Pith/arXiv arXiv 2022

  63. [63]

    M. Kopp, C. Skordis, and D. B. Thomas, Phys. Rev. D94, 043512 (2016), arXiv:1605.00649 [astro-ph.CO]

    Pith/arXiv arXiv 2016

  64. [64]

    Valiviita, E

    J. Valiviita, E. Majerotto, and R. Maartens, JCAP07, 020 (2008), arXiv:0804.0232 [astro-ph]

    Pith/arXiv arXiv 2008

  65. [65]

    Hu, Astrophys

    W. Hu, Astrophys. J.506, 485 (1998), arXiv:astro-ph/9801234

    Pith/arXiv arXiv 1998

  66. [66]

    D. B. Thomas, M. Kopp, and C. Skordis, Astrophys. J.830, 155 (2016), arXiv:1601.05097 [astro-ph.CO]

    Pith/arXiv arXiv 2016

  67. [67]

    B. F. Schutz and R. Sorkin, Annals Phys.107, 1 (1977)

    1977

  68. [68]

    J. D. Brown, Class. Quant. Grav.10, 1579 (1993), arXiv:gr-qc/9304026

    Pith/arXiv arXiv 1993

  69. [69]

    Skordis, A

    C. Skordis, A. Pourtsidou, and E. J. Copeland, Phys. Rev. D91, 083537 (2015), arXiv:1502.07297 [astro-ph.CO]

    Pith/arXiv arXiv 2015

  70. [70]

    T. S. Koivisto, E. N. Saridakis, and N. Tamanini, JCAP09, 047 (2015), arXiv:1505.07556 [astro-ph.CO]

    Pith/arXiv arXiv 2015

  71. [71]

    C. G. Boehmer, N. Tamanini, and M. Wright, Phys. Rev. D91, 123002 (2015), arXiv:1501.06540 [gr-qc]

    Pith/arXiv arXiv 2015

  72. [72]

    C. G. Boehmer, N. Tamanini, and M. Wright, Phys. Rev. D91, 123003 (2015), arXiv:1502.04030 [gr-qc]

    Pith/arXiv arXiv 2015

  73. [73]

    Dutta, W

    J. Dutta, W. Khyllep, and N. Tamanini, Phys. Rev. D95, 023515 (2017), arXiv:1701.00744 [gr-qc]

    Pith/arXiv arXiv 2017

  74. [74]

    C. G. Boehmer and A. d. del Sordo, Gen. Rel. Grav.56, 75 (2024), arXiv:2404.05301 [gr-qc]

    Pith/arXiv arXiv 2024

  75. [75]

    H. A. Ashi, C. G. Boehmer, A. d. del Sordo, and E. Jensko, Gen. Rel. Grav.57, 167 (2025), arXiv:2509.14440 [gr-qc]

    arXiv 2025

  76. [76]

    Koivisto, Phys

    T. Koivisto, Phys. Rev. D72, 043516 (2005), arXiv:astro-ph/0504571

    Pith/arXiv arXiv 2005

  77. [77]

    R. Bean, E. E. Flanagan, I. Laszlo, and M. Trodden, Phys. Rev. D78, 123514 (2008), arXiv:0808.1105 [astro-ph]

    Pith/arXiv arXiv 2008

  78. [78]

    M. B. Gavela, D. Hernandez, L. Lopez Honorez, O. Mena, and S. Rigolin, JCAP07, 034 (2009), [Erratum: JCAP 05, E01 (2010)], arXiv:0901.1611 [astro-ph.CO]

    Pith/arXiv arXiv 2009

  79. [79]

    Martinelli, N

    M. Martinelli, N. B. Hogg, S. Peirone, M. Bruni, and D. Wands, Mon. Not. Roy. Astron. Soc.488, 3423 (2019), arXiv:1902.10694 [astro-ph.CO]

    Pith/arXiv arXiv 2019

  80. [80]

    Ballesteros and B

    G. Ballesteros and B. Bellazzini, JCAP04, 001 (2013), arXiv:1210.1561 [hep-th]

    Pith/arXiv arXiv 2013

Showing first 80 references.