Pith. sign in

REVIEW 3 major objections 4 minor 48 references

Averaging a concept's activations across languages extracts its meaning

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 ↗

The abstract claims multilingual averaging of Gemma Scope activations aligns with ontology ground truth better than any single language, but the provided full text is an unrelated paper and contains no supporting evidence.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The abstract describes a plausible but incremental SAE interpretability experiment, yet the supplied full text is an unrelated 3D-vision paper, so the central claim cannot be checked anywhere in the document. the 3 major comments →

arxiv 2508.14275 v1 pith:GDG7FUV5 submitted 2025-08-19 cs.CL cs.AI

Disentangling concept semantics via multilingual averaging in Sparse Autoencoders

Cliff O'Reilly , Ernesto Jimenez-Ruiz , Tillman Weyde This is my paper
classification cs.CL cs.AI
keywords sparse autoencodersLLM interpretabilitymultilingual averagingconcept semanticsontology alignmentGemma Scopecross-lingual representationsmechanistic interpretability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

This paper tries to show that language-specific noise can be stripped away from a large language model's internal concept representations by averaging sparse-autoencoder (SAE) activations across translations. The authors render ontology class labels as English prompts, translate them into French and Chinese, and read off the Gemma 2B model's SAE activations for each language. Averaging the three activation vectors yields what they call a conceptual average, and they argue this average tracks the true relationships between classes better than any single language does. If right, this gives a straightforward, training-free way to sharpen mechanistic interpretation of LLM internals and to connect them to formal knowledge structures like OWL.

Core claim

The central claim is that multilingual averaging isolates concept semantics in LLM internal states. For each OWL class, English text is generated, machine-translated into French and Chinese, then passed as prompts to Gemma 2B; the Gemma Scope sparse autoencoders produce an activation vector per class per language. The arithmetic mean of the three language vectors is treated as the class's conceptual average. Correlating these averages against an OWL ground-truth mapping between classes, the paper reports that the multilingual average aligns with the true class relationships more closely than any single language's activations. The intended upshot is a new interpretability technique: average a

What carries the argument

The conceptual average: the arithmetic mean of sparse-autoencoder activation vectors for the same ontology class prompted in English, French, and Chinese. The work it does is to cancel language-specific components of the activation while preserving the component shared across languages, under the assumption that SAE features decompose that way. The evaluation machinery is the OWL class hierarchy, used as ground truth for which classes are truly related.

Load-bearing premise

The method assumes that sparse-autoencoder activations decompose into a shared conceptual component plus language-specific noise, so averaging three languages cancels the noise; it also treats the OWL hierarchy as the unbiased ground truth for true class relationships.

What would settle it

Run the same pipeline but average, say, twenty English paraphrases of each ontology class instead of three languages; if the monolingual average matches or beats the English–French–Chinese average in correlation with the OWL hierarchy, the claim that cross-language averaging specifically isolates concepts loses its support.

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

If this is right

  • If the claim holds, SAE activations contain a language-invariant conceptual component that simple averaging can expose.
  • Concept representations extracted this way could be matched against formal ontologies, giving LLMs a route to ontology-grounded reasoning without fine-tuning.
  • Mechanistic interpretability analyses that target concept-level features may become more reliable when run on multilingual averages rather than monolingual activations.
  • The technique is training-free and uses only open components, so it can be applied to any LLM with a public SAE suite.

Where Pith is reading between the lines

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

  • The paper does not run the control of averaging many paraphrases in one language; that control would determine whether the gain comes from multilingual diversity or simply from averaging over prompt variation.
  • The gap between a single language's activation and the multilingual average could be read as a per-language bias signal, giving a measurement tool for how differently a model encodes the same concept in different languages.
  • The same averaging move might transfer to other axes—modalities, domains, or registers—wherever a shared semantic core is buried in source-specific variation.
Share X Bluesky LinkedIn Reddit HN

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

3 major / 4 minor

Summary. The manuscript as submitted consists of an abstract and a full text that do not correspond. The abstract (arXiv:2508.14275) proposes to isolate concept semantics in LLMs by averaging Gemma Scope sparse-autoencoder activations computed from English, French, and Chinese paraphrases of OWL ontology class labels, and claims that these 'conceptual averages' correlate better with ontology ground truth than single-language activations. The full text supplied under the manuscript is arXiv:2508.14278v2, 'GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting,' a 3D scene understanding paper. The body contains no sparse autoencoders, no Gemma, no multilingual prompts, no OWL ontology, no averaging across languages, and no evaluation of the claimed correlation. The paper's central claim is therefore not supported by any locatable evidence in the manuscript under review.

Significance. Were it established, the proposed multilingual averaging of SAE activations would be a useful technique for cross-lingual concept-level interpretability, potentially reducing language-specific confounds and enabling higher-accuracy mechanistic interpretation. The manuscript's strength is that the idea is clearly stated in the abstract and is falsifiable: one can test whether averaging across languages outperforms monolingual baselines against held-out ontology relations. However, no such test, derivation, dataset, or code is present in the full text. There are no machine-checked proofs, reproducible experiments, or parameter-free derivations to credit. As it stands, the paper provides a promising hypothesis rather than an evaluated method.

major comments (3)
  1. [Title/Abstract vs. Full Text] The central claim of the paper is that averaging SAE activations across English, French, and Chinese improves alignment with OWL ground-truth relations. The full text supplied for review, however, is arXiv:2508.14278v2, 'GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting,' and contains none of the claimed components: no sparse autoencoders, no Gemma/Gemma Scope, no multilingual prompt construction, no OWL ontology, no language-averaging operation, and no correlation analysis. Sections 1-6 and Tables 1-7 of the body report 3D Gaussian Splatting segmentation results. Consequently, the abstract's empirical claim cannot be checked, replicated, or even located in the manuscript. This is a load-bearing verifiability failure.
  2. [Abstract: averaging premise] The method's engine is the premise that SAE activations decompose into a concept component plus language-specific noise, and that averaging English, French, and Chinese activations cancels the noise while preserving the concept. The abstract states this procedure but provides no derivation, no controls (e.g., averaging repeated prompts in a single language, or averaging different prompts within one language), and no evidence that such a decomposition holds for Gemma Scope features. Without a control of this kind, the reported advantage of multilingual averaging over a single language would be confounded by prompt variation and pooling effects.
  3. [Evaluation: OWL ground truth] The abstract says English text representations are created from OWL ontology classes, and the conceptual averages are correlated with a 'ground truth mapping between ontology classes.' If the same OWL hierarchy provides both the prompts and the ground-truth relations, high correlation may reflect lexical/string overlap between class labels and prompt texts rather than recovered concept semantics. The abstract does not mention held-out classes, external benchmarks, or controls for label-lexical consistency. This potential circularity must be addressed before the central claim can be evaluated.
minor comments (4)
  1. [Abstract] Typo: 'Large Langue Models' should be 'Large Language Models.'
  2. [Full text title] The title 'Open V ocabulary' contains an erroneous space; it should be 'Open Vocabulary.'
  3. [Full text Section 4.3, Eq. (7)] The text 'layer normalization opertor' contains a typo; it should be 'operator.'
  4. [References] No references to Gemma Scope, sparse autoencoders, OWL, or multilingual LLM evaluation appear in the bibliography. If a corrected full text is resubmitted, the relevant prior work must be cited.

Circularity Check

1 steps flagged

Partial circularity: OWL ontology is both the prompt source and the ground truth; supplied full text is a different paper, so the claimed SAE method cannot be located or checked.

specific steps
  1. self definitional [Abstract (arXiv:2508.14275) — note: supplied full text is actually arXiv:2508.14278v2 (GALA), not this paper]
    "We create English text representations from OWL ontology classes, translate the English into French and Chinese and then pass these texts as prompts to the Gemma 2B LLM. ... We then correlate the conceptual averages with a ground truth mapping between ontology classes."

    The ground-truth mapping between ontology classes is derived from the same OWL ontology whose class labels generated the English, French, and Chinese prompts. Thus the evaluation compares SAE activations of OWL-derived prompts against OWL-derived relations; a positive correlation can reflect label-lexical consistency rather than a language-independent 'conceptual average.' No held-out ontology, external benchmark, or control such as averaging repeated prompts within one language is described, so the key claim that the conceptual average 'aligns to the true relationship between classes' is not independent of its own evaluation input.

full rationale

The supplied full text is the unrelated GALA 3D-vision paper (arXiv:2508.14278v2) and contains no sparse autoencoders, Gemma, multilingual prompt construction, OWL ground truth, averaging operation, or correlation analysis; this is a verifiability failure and is flagged separately, but it is not itself circularity. Within the abstract, the only identifiable self-referential loop is that the OWL ontology is used both to generate the concept prompts and as the ground-truth mapping, so the evaluation partially reduces to checking whether activations of OWL-derived labels correlate with OWL-derived relations. The averaging across languages is an empirical operation that could fail, and no fitted parameter is renamed as a prediction, so the circularity is partial rather than total. No load-bearing self-citation or imported uniqueness theorem appears. Score 4 reflects this partial self-definitional evaluation, not a full reduction of the result to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 5 axioms · 0 invented entities

No free parameters can be identified: the abstract reports no fitted numbers, layers, thresholds, or correlation procedures, and the body is an unrelated manuscript, so nothing can be audited. The axioms above are the load-bearing premises of the abstract's method. No new entities are introduced: the 'conceptual average' is a derived statistic, not a postulated object.

axioms (5)
  • ad hoc to paper SAE activations decompose into language-specific and concept-specific components that are separable.
    Core premise of the averaging method, introduced by this paper without independent evidence.
  • ad hoc to paper Averaging activations across English, French, and Chinese cancels language-specific noise while preserving the concept signal.
    This is the method's engine (abstract); it assumes noise cancellation and signal preservation with no stated conditions.
  • domain assumption Machine translation preserves the concept denoted by each OWL class label.
    Freely translated French and Chinese prompts are assumed to denote the same classes.
  • domain assumption The OWL ontology's class relations form a valid ground truth for concept semantics.
    Used as the correlation target; ontology hierarchies are formal artifacts and may not match psychological or distributional semantics.
  • domain assumption Correlation between averaged activations and ontology relations indicates semantic alignment.
    The reported 'strong indication' rests on this statistical interpretation; no effect size or significance is given in the abstract.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Disentangling concept semantics via multilingual averaging in Sparse Autoencoders." pith.science (2026). https://pith.science/paper/GDG7FUV5

@misc{pith2026250814275,
  author       = {Pith},
  title        = {Pith review of: Disentangling concept semantics via multilingual averaging in Sparse Autoencoders},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GDG7FUV5}},
  note         = {Machine review of arXiv:2508.14275}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Connecting LLMs with formal knowledge representation and reasoning is a promising approach to address their shortcomings. Embeddings and sparse autoencoders are widely used to represent textual content, but the semantics are entangled with syntactic and language-specific information. We propose a method that isolates concept semantics in Large Langue Models by averaging concept activations derived via Sparse Autoencoders. We create English text representations from OWL ontology classes, translate the English into French and Chinese and then pass these texts as prompts to the Gemma 2B LLM. Using the open source Gemma Scope suite of Sparse Autoencoders, we obtain concept activations for each class and language version. We average the different language activations to derive a conceptual average. We then correlate the conceptual averages with a ground truth mapping between ontology classes. Our results give a strong indication that the conceptual average aligns to the true relationship between classes when compared with a single language by itself. The result hints at a new technique which enables mechanistic interpretation of internal network states with higher accuracy.

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

48 extracted references · 37 canonical work pages

  1. [1]

    Vision-and-language navigation: In- terpreting visually-grounded navigation instructions in real environments

    Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko S ¨underhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-language navigation: In- terpreting visually-grounded navigation instructions in real environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3674–3683,

    work page

  2. [2]

    Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields. In Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 5855–5864,

  3. [3]

    Lang, et al

    Holger Caesar, Varun Bankiti, Alex H. Lang, et al. nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), 2020. 1

    work page 2020

  4. [4]

    Seg- ment anything in 3d with nerfs

    Jiazhong Cen, Zanwei Zhou, Jiemin Fang, Wei Shen, Lingxi Xie, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, et al. Seg- ment anything in 3d with nerfs. Advances in Neural Infor- mation Processing Systems, 36:25971–25990, 2023. 2

    work page 2023

  5. [5]

    Scenear: Learning to recon- struct 3d indoor scenes for augmented reality

    Jiaqi Chen, Ruoxi Zhao, et al. Scenear: Learning to recon- struct 3d indoor scenes for augmented reality. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 1

    work page 2023

  6. [6]

    Occam’s lgs: An efficient ap- proach for language gaussian splatting

    Jiahuan Cheng, Jan-Nico Zaech, Luc Van Gool, and Danda Pani Paudel. Occam’s lgs: An efficient ap- proach for language gaussian splatting. arXiv preprint arXiv:2412.01807, 2024. 2, 3

    Pith/arXiv arXiv 2024

  7. [7]

    Scannet: Richly-annotated 3d reconstructions of indoor scenes

    Angela Dai, Angel X Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5828–5839, 2017. 2, 5, 7, 8, 1, 4

    work page 2017

  8. [8]

    Are we ready for autonomous driving? the kitti vision benchmark suite

    Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2012. 1

    work page 2012

  9. [9]

    2d gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically ac- curate radiance fields. In ACM SIGGRAPH 2024 conference papers, pages 1–11, 2024. 1

    work page 2024

  10. [10]

    Visual language maps for robot navigation

    Chenguang Huang, Oier Mees, Andy Zeng, and Wolfram Burgard. Visual language maps for robot navigation. arXiv preprint arXiv:2210.05714, 2022. 2

    Pith/arXiv arXiv 2022

  11. [11]

    Foundations of spatial perception for robotics: Hierarchical representations and real-time systems

    Nathan Hughes, Yun Chang, Siyi Hu, Rajat Talak, Rumaia Abdulhai, Jared Strader, and Luca Carlone. Foundations of spatial perception for robotics: Hierarchical representations and real-time systems. The International Journal of Robotics Research, 43(10):1457–1505, 2024. 1

    work page 2024

  12. [12]

    Kim Jun-Seong, GeonU Kim, Kim Yu-Ji, Yu-Chiang Frank Wang, Jaesung Choe, and Tae-Hyun Oh. Dr. splat: Directly referring 3d gaussian splatting via direct language embed- ding registration. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 14137–14146, 2025. 1, 2, 3, 4

    work page 2025

  13. [13]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  14. [14]

    Lerf: Language embedded radiance fields

    Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa, and Matthew Tancik. Lerf: Language embedded radiance fields. In Proceedings of the IEEE/CVF interna- tional conference on computer vision , pages 19729–19739,

    work page

  15. [15]

    Garfield: Group anything with radiance fields

    Chung Min Kim, Mingxuan Wu, Justin Kerr, Ken Gold- berg, Matthew Tancik, and Angjoo Kanazawa. Garfield: Group anything with radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21530–21539, 2024. 1, 2

    work page 2024

  16. [16]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF international con- ference on computer vision, pages 4015–4026, 2023. 2, 4, 6, 1

    work page 2023

  17. [17]

    Gaussnav: Gaussian splatting for visual navigation

    Xiaohan Lei, Min Wang, Wengang Zhou, and Houqiang Li. Gaussnav: Gaussian splatting for visual navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence,

    work page

  18. [18]

    Instancegaussian: Appearance-semantic joint gaussian representation for 3d instance-level perception

    Haijie Li, Yanmin Wu, Jiarui Meng, Qiankun Gao, Zhiyao Zhang, Ronggang Wang, and Jian Zhang. Instancegaussian: Appearance-semantic joint gaussian representation for 3d instance-level perception. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 14078– 14088, 2025. 2, 3, 4, 5

    work page 2025

  19. [19]

    Dns-slam: Dense neural semantic-informed slam

    Kunyi Li, Michael Niemeyer, Nassir Navab, and Federico Tombari. Dns-slam: Dense neural semantic-informed slam. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7839–7846. IEEE, 2024. 1

    work page 2024

  20. [20]

    Geogaussian: Geometry-aware gaussian splatting for scene rendering

    Yanyan Li, Chenyu Lyu, Yan Di, Guangyao Zhai, Gim Hee Lee, and Federico Tombari. Geogaussian: Geometry-aware gaussian splatting for scene rendering. In European confer- ence on computer vision, pages 441–457. Springer, 2024. 1

    work page 2024

  21. [21]

    Su- pergseg: Open-vocabulary 3d segmentation with structured super-gaussians

    Siyun Liang, Sen Wang, Kunyi Li, Michael Niemeyer, Ste- fano Gasperini, Nassir Navab, and Federico Tombari. Su- pergseg: Open-vocabulary 3d segmentation with structured super-gaussians. arXiv preprint arXiv:2412.10231, 2024. 1, 3, 4, 5, 6

    arXiv 2024

  22. [22]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. InEuro- pean conference on computer vision, pages 38–55. Springer,

  23. [23]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20654–20664, 2024. 1, 3, 4

    work page 2024

  24. [24]

    Clio: Real-time task- driven open-set 3d scene graphs

    Dominic Maggio, Yun Chang, Nathan Hughes, Matthew Trang, Dan Griffith, Carlyn Dougherty, Eric Cristofalo, 9 Lukas Schmid, and Luca Carlone. Clio: Real-time task- driven open-set 3d scene graphs. IEEE Robotics and Au- tomation Letters, 2024. 1

    work page 2024

  25. [25]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 1

    2021

  26. [26]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 1

    work page 2022

  27. [27]

    Radsplat: Radiance field-informed gaussian splat- ting for robust real-time rendering with 900+ fps

    Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto- saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radsplat: Radiance field-informed gaussian splat- ting for robust real-time rendering with 900+ fps. In Inter- national Conference on 3D Vision 2025. 1

    work page 2025

  28. [28]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Transactions on Machine Learning Research Journal, 2024. 2

    2024

  29. [29]

    Openscene: 3d scene understanding with open vocabularies

    Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser, et al. Openscene: 3d scene understanding with open vocabularies. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 815–824, 2023. 2

    work page 2023

  30. [30]

    Gags: Granularity-aware feature distillation for language gaussian splatting

    Yuning Peng, Haiping Wang, Yuan Liu, Chenglu Wen, Zhen Dong, and Bisheng Yang. Gags: Granularity-aware feature distillation for language gaussian splatting. arXiv preprint arXiv:2412.13654, 2024. 6

    Pith/arXiv arXiv 2024

  31. [31]

    Langsplat: 3d language gaussian splatting

    Minghan Qin, Wanhua Li, Jiawei Zhou, Haoqian Wang, and Hanspeter Pfister. Langsplat: 3d language gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20051–20060, 2024. 1, 2, 3, 4, 5, 6, 7, 8

    work page 2024

  32. [32]

    Goi: Find 3d gaussians of interest with an optimizable open-vocabulary semantic-space hyperplane

    Yansong Qu, Shaohui Dai, Xinyang Li, Jianghang Lin, Liu- juan Cao, Shengchuan Zhang, and Rongrong Ji. Goi: Find 3d gaussians of interest with an optimizable open-vocabulary semantic-space hyperplane. In Proceedings of the 32nd ACM international conference on multimedia , pages 5328–5337,

    work page

  33. [33]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, pages 8748–8763. PmLR, 2021. 2, 6, 1

    work page 2021

  34. [34]

    What is xr? towards a framework for augmented and virtual reality

    Philipp A Rauschnabel, Reto Felix, Chris Hinsch, Hamza Shahab, and Florian Alt. What is xr? towards a framework for augmented and virtual reality. Computers in human be- havior, 133:107289, 2022. 1

    work page 2022

  35. [35]

    Grounded sam: Assembling open-world models for diverse visual tasks

    Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14159 ,

    Pith/arXiv arXiv

  36. [36]

    Aligning and prompting everything all at once for univer- sal visual perception

    Yunhang Shen, Chaoyou Fu, Peixian Chen, Mengdan Zhang, Ke Li, Xing Sun, Yunsheng Wu, Shaohui Lin, and Rongrong Ji. Aligning and prompting everything all at once for univer- sal visual perception. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 13193–13203, 2024. 2

    work page 2024

  37. [37]

    Language embedded 3d gaussians for open- vocabulary scene understanding

    Jin-Chuan Shi, Miao Wang, Hao-Bin Duan, and Shao- Hua Guan. Language embedded 3d gaussians for open- vocabulary scene understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5333–5343, 2024. 5, 6

    work page 2024

  38. [38]

    Ccl-lgs: Con- trastive codebook learning for 3d language gaussian splat- ting

    Lei Tian, Xiaomin Li, Liqian Ma, Hefei Huang, Zirui Zheng, Hao Yin, Taiqing Li, Huchuan Lu, and Xu Jia. Ccl-lgs: Con- trastive codebook learning for 3d language gaussian splat- ting. arXiv preprint arXiv:2505.20469, 2025. 3

    Pith/arXiv arXiv 2025

  39. [39]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 5

    work page 2017

  40. [40]

    Drivedreamer: Towards real-world- drive world models for autonomous driving

    Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jia- gang Zhu, and Jiwen Lu. Drivedreamer: Towards real-world- drive world models for autonomous driving. In European conference on computer vision, pages 55–72. Springer, 2024. 1

    work page 2024

  41. [41]

    Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving

    Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, and Zhaoxiang Zhang. Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 14749–14759, 2024. 1

    work page 2024

  42. [42]

    Opengaussian: Towards point-level 3d gaussian-based open vocabulary understanding

    Yanmin Wu, Jiarui Meng, Haijie Li, Chenming Wu, Yahao Shi, Xinhua Cheng, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, et al. Opengaussian: Towards point-level 3d gaussian-based open vocabulary understanding. Advances in Neural Information Processing Systems, 37:19114–19138,

    work page

  43. [43]

    Point- nerf: Point-based neural radiance fields

    Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann. Point- nerf: Point-based neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5438–5448, 2022. 1

    work page 2022

  44. [44]

    Gaussian grouping: Segment and edit anything in 3d scenes

    Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke. Gaussian grouping: Segment and edit anything in 3d scenes. In European conference on computer vision, pages 162–179. Springer, 2024. 5, 6

    work page 2024

  45. [45]

    Omniseg3d: Omniversal 3d segmentation via hierarchical contrastive learning

    Haiyang Ying, Yixuan Yin, Jinzhi Zhang, Fan Wang, Tao Yu, Ruqi Huang, and Lu Fang. Omniseg3d: Omniversal 3d segmentation via hierarchical contrastive learning. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20612–20622, 2024. 1, 2, 4

    work page 2024

  46. [46]

    Mip-splatting: Alias-free 3d gaussian splat- ting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 19447–19456,

    work page

  47. [47]

    Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields

    Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Ze- hao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, and Achuta Kadambi. Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21676–21685, 2024. 1, 2, 3, 4, 5, 6 11...

    work page 2024

  48. [2024]

    2, 3, 4, 5, 6, 7, 8, 1

    work page

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.