Pith. sign in

REVIEW 3 major objections 3 minor 39 references

When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning

T0 review · 3 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A stable compression score can still select a worse model for the worst group.

desk verdict Novel information-interface framing for why compression scores can diverge from worst-group performance, but the sequential-pruning guarantees rest on an unverified state-sufficiency assumption and the abstract alone cannot carry the empirical claims. read the letter →

arxiv 2608.02940 v2 pith:IB753FVT submitted 2026-08-03 cs.AI

Andrew Zhang This is my paper · ORCID
classification cs.AI
keywords LLMpruninggrouprobustnesscompressionscoresworst-groupriskinformationinterfacestatisticalidentificationsequentialmodelselection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to establish that compression scores, the numbers usually used to choose between pruned language models, cannot by themselves justify a choice when deployment cares about the worst-performing group. A statistic is modeled as an information interface: observing its value leaves a whole fiber of endpoint-risk tables that are compatible with that observation, and only endpoint orders that are the same across the entire fiber are genuinely identified. The paper shows this is not a theoretical footnote: a split-half reliable path-quadratic score predicted a 16.1% gain while the endpoints it selected were 6.0–7.7% worse than two controls. If the claim is right, group-robust pruning has to control worst-group risk explicitly rather than trust stable-looking compression statistics.

What carries the argument

The central object is the fiber of compatible endpoint-risk tables left by a compression statistic treated as an information interface. A statistic's observed value does not pin down one risk table; it leaves a set, and only endpoint orders that survive across the whole set are identified. Cone and fiber identities express how much uncertainty remains after the observation. For sequential composition the machinery adds the slack vector—the distance from each group's risk to the current maximum—as a sufficient state for one-step responses, plus a margin condition that keeps the active group fixed along bounded-drift paths.

What would settle it

Find a real pruning trajectory on an LLM where the worst group flips between steps even though the slack vector stays inside the stated margin, and show that a policy using only the slack vector selects a different endpoint than one with full group-risk information; that would break the sequential composition claim. Alternatively, a corpus of endpoints where a split-half-stable compression score orders worst-group risk correctly in every case would refute the fiber claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that a compression statistic used to choose among pruned LLMs is an information interface: its observed value leaves a set (a fiber) of endpoint-risk tables that are all compatible with that observation. Only an ordering of endpoints that is identical across the whole fiber is actually identified by the statistic. The authors show that stable-looking scores can reverse this ordering: a split-half reliable path-quadratic score predicted a 16.1% gain while its chosen endpoints were 6.0–7.7% worse than two controls. For sequential pruning, the needed state is the slack vector of each group's risk to the current maximum; this vector determines every unrestricted one-step response, and a margin condition keeps the active group fixed along paths with bounded relative drift. The conclusion is that group-robust pruning cannot rely on high-scoring compression statistics alone: local evidence narrows the menu, complete endpoints rank it, and multistep claims need control of the evolving active face and future candidates.

Load-bearing premise

The multistep argument assumes that a one-step pruning decision depends only on the vector of distances from each group's current risk to the current maximum risk, and that along paths with bounded relative drift the identity of the maximum-risk group stays fixed.

Editorial extensions

If this is right

  • Model selection with compression scores should check worst-group risk explicitly, because a score can be stable and still select an endpoint that is worse for the worst group.
  • Reported gains from stable compression scores are not guarantees; an endpoint order is only justified if it is fixed across the entire fiber of risk tables compatible with the score.
  • Sequential pruning policies need the slack vector and a margin condition; ignoring them can make adaptive trajectories worse than a fixed pooled endpoint, as in the 32.7% worse hard-max trajectory.
  • Pooled endpoint refresh can improve worst-group held-out performance (15.8% over the best static score in OLMoE), suggesting complete-menu evaluation rather than single-path scoring.
  • Group-local and pooled moment scores can reverse endpoint order under matched observations, so the identity of the best endpoint depends on which statistic is used.

Reading between the lines

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

  • The fiber argument applies to any model-selection signal, not just compression scores; any scalar summary that discards group-level detail can leave worst-group ordering unidentified.
  • A natural extension is to construct confidence sets over endpoints from the fiber and choose the endpoint whose worst-case group risk is minimal, turning identification into robust optimization.
  • The margin condition suggests a testable diagnostic: monitor slack along real pruning trajectories and flag steps where the active group changes without crossing the stated margin.
  • The 16.1% predicted gain versus 6.0–7.7% worse endpoints implies split-half reliability measures score stability, not decision safety; reporting decision-regret intervals would be a concrete next step.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper argues that a compression score, viewed as an information interface, leaves a fiber of compatible endpoint-risk tables, and therefore only endpoint orders fixed across that fiber are identifiable. It further claims a sequential composition result in which the slack vector—each group's risk distance to the current maximum—determines every unrestricted one-step pruning response, subject to a margin condition that keeps the active group fixed along paths with bounded relative drift. The abstract reports empirical results on three dense LLMs and OLMoE, including worst-group perplexity inflation reductions of 12.6–20.9%, complete-menu selection improvements of 2.7–8.0%, and a 15.8% held-out worst-group teacher KL improvement for pooled endpoint refresh on OLMoE. A compute-matched hard-max trajectory is reported to end 32.7% worse than pooled. The paper's central conceptual claim—that score-stable choices can still be worst-group-worse—is stated clearly, but the abstract provides no derivation, experimental protocol, baselines, or uncertainty quantification.

Significance. If the claims hold, the paper would make a useful conceptual contribution by showing that compression statistics, viewed as information interfaces, cannot by themselves certify worst-group performance, and that explicit control of worst-group risk is needed in pruning. The concrete percentage improvements across multiple models are potentially valuable evidence. However, because this review is based only on the abstract, the significance cannot be fully assessed; the paper appears to contain falsifiable predictions and an explicit mathematical framework, which are strengths, but the abstract alone does not allow verification of the derivation or the experiments.

major comments (3)
  1. [Abstract, experimental claims] The abstract reports percentage improvements across three dense LLMs and OLMoE without specifying the experimental protocol, baseline definitions, number of runs, or error bars. As written, claims such as 'reduces worst-group perplexity inflation by 12.6–20.9%' and 'held-out worst-group teacher KL by 15.8%' cannot be checked. Please provide a complete experimental section with model versions, dataset splits, calibration of 'worst-group,' the exact compared baselines, and measures of variance.
  2. [Abstract, sequential composition] The sequential composition guarantee rests on the statement that the slack vector 'determines every unrestricted one-step response' and that a margin condition keeps the active group fixed. The abstract gives no derivation or formal statement of these conditions. If the one-step pruning action depends on the full risk table rather than only on slack—for example, if risk is measured as perplexity and pruning costs scale multiplicatively—then two states with identical slack but different absolute risks could require different actions, invalidating the multistep guarantee. Please state the conditions precisely and prove that they hold for the reported trajectories.
  3. [Abstract, margin condition and hard-max result] The margin condition must hold along the actual trajectories of compared methods, including the hard-max and pooled adaptive trajectories in OLMoE, for the reported 32.7% gap to be interpretable as a consequence of the information boundary. The abstract does not indicate whether this condition was verified for those trajectories or whether the gap might be an artifact of margin violation. Please provide an analysis of the margin condition along the empirically followed paths.
minor comments (3)
  1. [Abstract, terminology] The terms 'split-half reliable path-quadratic score,' 'target-matched complete-menu selection,' and 'pooled endpoint refresh' are used without definition; please introduce them in the main text before they appear in the abstract or abstract-like summary.
  2. [Abstract, metrics] The abstract mentions 'worst-group perplexity inflation' and 'held-out worst-group teacher KL' but does not define the reference values or the aggregation over groups; please clarify these metrics.
  3. [Abstract, structure] The sentence 'The experiments follow the same ladder' is unclear; please spell out the ladder structure explicitly in the introduction or methods.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified in the abstract; the reported score reversals are empirical failures of predictions, and the slack-vector sufficiency is an assumption rather than a circular reduction.

full rationale

The abstract contains no fitted-to-prediction circularity. The reported reversals—a path-quadratic score predicting a 16.1% gain while the selected endpoints were 6.0–7.7% worse than controls, and a hard-max trajectory ending 32.7% worse than pooled—are cases where a score's prediction fails against held-out or control outcomes, not cases where the outcome is derived from the score. The information-interface and fiber argument is presented as an identifiability characterization: an observation leaves a fiber of compatible endpoint-risk tables, and only orders fixed across that fiber are identified. That is a non-circular mathematical statement about what a statistic cannot distinguish. The sequential-composition claim rests on an explicit assumption that the slack vector determines every unrestricted one-step response and that a margin condition keeps the active group fixed; this is a fragility or validity concern about the theorem's hypotheses, not a circular reduction visible in the abstract. No self-citation chain, imported uniqueness theorem, or ansatz-via-citation is present in the abstract, and no equation-level equivalence can be exhibited from abstract-only text. Therefore, no significant circularity can be identified.

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

All entries are extracted from the abstract; the full text may reveal additional assumptions or free parameters. No physical invented entities are evident from the abstract.

assumptions (4)
  • domain assumption Endpoint-risk tables are well-defined and worst-group risk is the maximum over groups of these tables.
    The identification framework requires a complete set of group risk tables compatible with the observed statistic; the abstract assumes such tables exist and carry the deployment objective.
  • ad hoc to paper The slack vector (distance from each group risk to the current maximum) is a sufficient state for every unrestricted one-step pruning response.
    The abstract states 'This vector determines every unrestricted one-step response.' This is a strong Markov assumption; if pruning steps also depend on other features, the sequential guarantee fails.
  • ad hoc to paper A margin condition keeps the active group fixed along paths with bounded relative drift.
    The abstract asserts this condition but does not show it holds for actual LLM trajectories; the multistep conclusions depend on it.
  • domain assumption Held-out worst-group teacher KL and perplexity inflation are valid proxies for deployment worst-group performance.
    Empirical claims are measured on these proxies; the abstract does not justify the proxy choice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning." pith.science (2026). https://pith.science/paper/IB753FVT

@misc{pith2026260802940,
  author       = {Pith},
  title        = {Pith review of: When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IB753FVT}},
  note         = {Machine review of arXiv:2608.02940}
}
read the original abstract

A stable compression score can still select the worse model. In our dense study, a split-half reliable path-quadratic score predicted a 16.1\% gain, while the selected endpoints were 6.0--7.7% worse than two controls. We ask what a compression statistic can justify when deployment cares about the worst supplied group. We treat each statistic as an information interface. Its observation leaves a fiber of compatible endpoint-risk tables, and only orders fixed across that fiber are identified. Cone and fiber identities quantify the remaining uncertainty, while matched observations reverse endpoint order for pooled moments, group-local moments, and reference-path curvature. Sequential composition adds one state variable: the slack from each group risk to the current maximum. This vector determines every unrestricted one-step response, and a margin condition keeps the active group fixed along paths with bounded relative drift. The experiments follow the same ladder. Across three dense LLMs, an early-preserving allocation reduces worst-group perplexity inflation by 12.6--20.9%; target-matched complete-menu selection improves over its references by 2.7--8.0%. Across all 16 routed layers of OLMoE, pooled endpoint refresh lowers held-out worst-group teacher KL by 15.8% over the best static score. A compute-matched hard-max trajectory ends 32.7% worse than pooled, and neither adaptive trajectory improves excess NLL. Local evidence can narrow a menu. Complete endpoints rank that menu, while multistep claims also require control of the evolving active face and future candidates.

Figures

Figures reproduced from arXiv: 2608.02940 by the authors.

Figure 1
Figure 1. Interfaces authorize different decisions. An average can hide group structure (A); group-resolved [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Empirical overview of the dense-model boundary. Group-resolved local evidence recovers broad [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Visibility does not determine leverage or endpoint order. (A) Exposure occurs only in o/down [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Router traces expose group structure without resolving set order. (A) Pooled load remains near [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Complete-candidate endpoints recover both set-level decisions missed by singleton ranking. (A) [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Full-model composition. (A) Pooled refresh lowers held-out worst-group forward KL by 15.8% [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 26 canonical work pages

  1. [1]

    Liu , title =

    Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu , title =. Journal of Machine Learning Research , volume =. 2020 , url =

    work page 2020

  2. [2]

    Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

    Alex Mallen and Akari Asai and Victor Zhong and Rajarshi Das and Daniel Khashabi and Hannaneh Hajishirzi , title =. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =. 2023 , publisher =. doi:10.18653/v1/2023.acl-long.546 , url =

    doi:10.18653/v1/2023.acl-long.546 2023

  3. [3]

    Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =

    Christopher Sciavolino and Zexuan Zhong and Jinhyuk Lee and Danqi Chen , title =. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =. 2021 , publisher =. doi:10.18653/v1/2021.emnlp-main.496 , url =

    doi:10.18653/v1/2021.emnlp-main.496 2021

  4. [4]

    Are Compressed Language Models Less Subgroup Robust?

    Gee, Leonidas and Zugarini, Andrea and Quadrianto, Novi. Are Compressed Language Models Less Subgroup Robust?. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.983

    work page doi:10.18653/v1/2023.emnlp-main.983 2023

  5. [5]

    Why Do Some Inputs Break Low-Bit LLM Quantization?

    Chang, Ting-Yun and Zhang, Muru and Thomason, Jesse and Jia, Robin. Why Do Some Inputs Break Low-Bit LLM Quantization?. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.168

    work page doi:10.18653/v1/2025.emnlp-main.168 2025

  6. [6]

    and Jaiswal, Ajay Kumar and Xu, Kaidi and Kailkhura, Bhavya and Hendrycks, Dan and Song, Dawn and Wang, Zhangyang and Li, Bo , booktitle =

    Hong, Junyuan and Duan, Jinhao and Zhang, Chenhui and Li, Zhangheng and Xie, Chulin and Lieberman, Kelsey and Diffenderfer, James and Bartoldson, Brian R. and Jaiswal, Ajay Kumar and Xu, Kaidi and Kailkhura, Bhavya and Hendrycks, Dan and Song, Dawn and Wang, Zhangyang and Li, Bo , booktitle =. Decoding Compressed Trust: Scrutinizing the Trustworthiness of...

    work page 2024

  7. [7]

    PKU - S afe RLHF : Towards Multi-Level Safety Alignment for LLM s with Human Preference

    Ji, Jiaming and Hong, Donghai and Zhang, Borong and Chen, Boyuan and Dai, Josef and Zheng, Boren and Qiu, Tianyi Alex and Zhou, Jiayi and Wang, Kaile and Li, Boxun and Han, Sirui and Guo, Yike and Yang, Yaodong. PKU - S afe RLHF : Towards Multi-Level Safety Alignment for LLM s with Human Preference. Proceedings of the 63rd Annual Meeting of the Associatio...

    doi:10.18653/v1/2025.acl-long.1544 2025

  8. [8]

    Zico Kolter , title =

    Mingjie Sun and Zhuang Liu and Anna Bair and J. Zico Kolter , title =. International Conference on Learning Representations , volume =. 2024 , url =

    work page 2024

Show all 39 references
  1. [9]

    M - W anda: Improving One-Shot Pruning for Multilingual LLM s

    Choenni, Rochelle and Titov, Ivan. M - W anda: Improving One-Shot Pruning for Multilingual LLM s. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.1369

    2025 doi

  2. [10]

    2023 , editor =

    Frantar, Elias and Alistarh, Dan , booktitle =. 2023 , editor =

    2023

  3. [11]

    Advances in Neural Information Processing Systems , volume =

    Sidak Pal Singh and Dan Alistarh , title =. Advances in Neural Information Processing Systems , volume =. 2020 , publisher =

    2020

  4. [12]

    Advances in Neural Information Processing Systems , volume =

    Diyuan Wu and Ionut-Vlad Modoranu and Mher Safaryan and Denis Kuznedelev and Dan Alistarh , title =. Advances in Neural Information Processing Systems , volume =. 2024 , doi =

    2024

  5. [13]

    Bartlett , title =

    Hossein Mobahi and Peter L. Bartlett , title =. arXiv preprint arXiv:2607.21366 , year =

    arXiv

  6. [14]

    2024 , howpublished =

    2024

  7. [15]

    2025 , howpublished =

    2025

  8. [16]

    2023 , note =

    GitHub Code Haskell File dataset , howpublished =. 2023 , note =

    2023

  9. [17]

    Proceedings of the 41st International Conference on Machine Learning , volume =

    Lu Yin and You Wu and Zhenyu Zhang and Cheng-Yu Hsieh and Yaqing Wang and Yiling Jia and Gen Li and Ajay Kumar Jaiswal and Mykola Pechenizkiy and Yi Liang and Michael Bendersky and Zhangyang Wang and Shiwei Liu , title =. Proceedings of the 41st International Conference on Mac...

    2024

  10. [18]

    International Conference on Learning Representations , volume =

    Peng Xu and Wenqi Shao and Mengzhao Chen and Shitao Tang and Kaipeng Zhang and Peng Gao and Fengwei An and Yu Qiao and Ping Luo , title =. International Conference on Learning Representations , volume =. 2024 , url =

    2024

  11. [19]

    Mahoney and Yaoqing Yang , title =

    Haiquan Lu and Yefan Zhou and Shiwei Liu and Zhangyang Wang and Michael W. Mahoney and Yaoqing Yang , title =. Advances in Neural Information Processing Systems , volume =. 2024 , doi =

    2024

  12. [20]

    On the Limitations of Language-targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning

    Kurz, Simon and Chen, Jian-Jia and Flek, Lucie and Zhao, Zhixue. On the Limitations of Language-targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning. Transactions of the Association for Computational Linguistics. 2026. doi:10.1162/tacl.a.599

    2026 doi

  13. [21]

    arXiv preprint arXiv:2607.07557 , year =

    Yazdan Jamshidi and Alexey Shvets , title =. arXiv preprint arXiv:2607.07557 , year =

    arXiv

  14. [22]

    Smith and Pang Wei Koh and Amanpreet Singh and Hannaneh Hajishirzi , title =

    Niklas Muennighoff and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Jacob Morrison and Sewon Min and Weijia Shi and Pete Walsh and Oyvind Tafjord and Nathan Lambert and Yuling Gu and Shane Arora and Akshita Bhagia and Dustin Schwenk and David Wadden and Alexander Wettig a...

    2025

  15. [23]

    A Survey on Mixture of Experts in Large Language Models , year=

    Cai, Weilin and Jiang, Juyong and Wang, Fan and Tang, Jing and Kim, Sunghun and Huang, Jiayi , journal=. A Survey on Mixture of Experts in Large Language Models , year=

  16. [24]

    2026 , issue_date =

    Liu, Jiacheng and Tang, Peng and Wang, Wenfeng and Ren, Yuhang and Hou, Xiaofeng and Heng, Pheng Ann and Guo, Minyi and Li, Chao , title =. 2026 , issue_date =. doi:10.1145/3794845 , journal =

    2026 doi

  17. [25]

    Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety

    Qiu, Mufan and Shen, Zheyu and Li, Pingzhi and Li, Ang and Chen, Tianlong. Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. doi:10.18653/v1/2025.findi...

    2025 doi

  18. [26]

    arXiv preprint arXiv:2606.15716 , year =

    Zongfang Liu and Jinghui Zhang and Zijian Ma and Guangyi Chen and Xin Yuan , title =. arXiv preprint arXiv:2606.15716 , year =

  19. [27]

    arXiv preprint arXiv:2504.05586 , year =

    Ajay Jaiswal and Jianyu Wang and Yixiao Li and Pingzhi Li and Tianlong Chen and Zhangyang Wang and Chong Wang and Ruoming Pang and Xianzhi Du , title =. arXiv preprint arXiv:2504.05586 , year =

    arXiv

  20. [28]

    Conference on Language Modeling , year =

    Zehua Pei and Ying Zhang and Hui-Ling Zhen and Tao Yuan and Xianzhi Yu and Zhenhua Dong and Sinno Jialin Pan and Mingxuan Yuan and Bei Yu , title =. Conference on Language Modeling , year =

  21. [29]

    arXiv preprint arXiv:2606.09886 , year =

    Yuhao Zhang , title =. arXiv preprint arXiv:2606.09886 , year =

    arXiv

  22. [30]

    arXiv preprint arXiv:2607.16721 , year =

    Anik Jha , title =. arXiv preprint arXiv:2607.16721 , year =

    arXiv

  23. [31]

    2005 , isbn =

    Cohen-Steiner, David and Edelsbrunner, Herbert and Harer, John , title =. 2005 , isbn =. doi:10.1145/1064092.1064133 , booktitle =

    2005

  24. [32]

    The Structure and Stability of Persistence Modules , publisher =

    Fr. The Structure and Stability of Persistence Modules , publisher =. 2016 , doi =

    2016

  25. [33]

    Documenta Mathematica , volume =

    Mike Develin and Bernd Sturmfels , title =. Documenta Mathematica , volume =. 2004 , url =

    2004

  26. [34]

    arXiv preprint arXiv:2510.13999 , year =

    Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa , title =. arXiv preprint arXiv:2510.13999 , year =

    arXiv

  27. [35]

    Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

    Lu, Xudong and Liu, Qi and Xu, Yuhui and Zhou, Aojun and Huang, Siyuan and Zhang, Bo and Yan, Junchi and Li, Hongsheng. Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models. Proceedings of the 62nd Annual Meeting of the ...

    2024 doi

  28. [36]

    2024 , eprint=

    Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs , author=. 2024 , eprint=

    2024

  29. [37]

    arXiv preprint arXiv:2604.06542 , year =

    Zeliang Zhang and Nikhil Ghosh and Jiani Liu and Bin Yu and Xiaodong Liu , title =. arXiv preprint arXiv:2604.06542 , year =

    arXiv

  30. [38]

    Partial Prescriptions For Decisions With Partial Knowledge

    Manski, Charles F. Partial Prescriptions For Decisions With Partial Knowledge. 2008

    2008

  31. [39]

    arXiv preprint arXiv:2111.04926 , year =

    Kohei Yata , title =. arXiv preprint arXiv:2111.04926 , year =

    arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.