pith. machine review for the scientific record. sign in

arxiv: 2604.17384 · v1 · submitted 2026-04-19 · 💻 cs.LG

Recognition: unknown

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion

Ou Wu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:04 UTC · model grok-4.3

classification 💻 cs.LG
keywords large language modelsdata-parameter correspondencestatistical manifoldFisher-Rao metricLegendre dualityin-context learningLoRA adaptationadversarial attacks
0
0 comments X

The pith

Data operations and parameter changes in large language models are dual aspects of the same geometric structure on a statistical manifold.

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

The paper establishes a data-parameter correspondence that unifies historically separate data-centric and model-centric optimization approaches for large language models. It frames operations like sample selection and weight tuning as equivalent manifestations of constraints on the statistical manifold, using the Fisher-Rao metric and Legendre duality between parameter types. A sympathetic reader would care because this link suggests that techniques developed in one community can transfer directly to the other, and that optimizing data and parameters together could yield gains in efficiency, robustness, and privacy across the model lifecycle. The framework covers training, compression, and inference stages.

Core claim

The paper establishes a unified data-parameter correspondence revealing these seemingly disparate operations as dual manifestations of the same geometric structure on the statistical manifold M. Grounded in the Fisher-Rao metric g_ij(θ) and Legendre duality between natural (θ) and expectation (η) parameters, it identifies three fundamental correspondences: geometric equivalence between data pruning and parameter sparsification that reduces manifold volume via dual coordinate constraints; low-rank equivalence where k-shot in-context learning and LoRA adaptation explore identical subspaces on the Grassmannian with k-shot samples equivalent to rank-r updates; and security-privacy equivalence in

What carries the argument

The data-parameter correspondence, which treats data manipulations and parameter adjustments as dual coordinate constraints on the statistical manifold via the Fisher-Rao metric and Legendre duality between natural and expectation parameters.

Load-bearing premise

The abstract geometric correspondences on the statistical manifold translate directly into practical performance gains for real large language models without additional empirical validation or hidden assumptions about model scale and data distribution.

What would settle it

Apply the proposed equivalence by using data pruning to mimic parameter sparsification on a real large language model and measure whether the resulting change in effective manifold volume or task performance matches the predicted dual reduction.

Figures

Figures reproduced from arXiv: 2604.17384 by Ou Wu.

Figure 1
Figure 1. Figure 1: Unified Data-Parameter Correspondence: Overview of dual operational spaces on statistical manifold M. (Left) η-space (expectation parameters): Data-centric operations across three lifecycle stages—training (selection, augmentation), post-training (distillation), and inference (ICL, prompting). (Right) θ-space (natural parameters): Parameter-centric operations—training (LoRA, masking), post-training (quanti… view at source ↗
Figure 2
Figure 2. Figure 2: Geometric Correspondence: Dual Riemannian structure on the statistical manifold M. Left panel: Parameter-space operations (masking Sp, pruning Pp, quantization Q) acting on θ-coordinates (natural parameters), reducing the manifold volume via constraints on the Fisher-Rao metric gij (θ). Right panel: Data-space operations (selection Sd, pruning Pd, augmentation A) perturbing η-coordinates (expectation param… view at source ↗
Figure 3
Figure 3. Figure 3: Augmentation Correspondence: Data augmentation (left) and param￾eter augmentation/dropout (right) as dual mechanisms for Fisher-Rao volume reduction on statistical manifold M. (Left) Data augmentation expands the empirical distribution support in η-space (perturbing sufficient statistics ∆η), which induces an implicit geometric compression in the dual θ-coordinates via the inverse Fisher metric g ij (θ). (… view at source ↗
Figure 4
Figure 4. Figure 4: illustrates the three-stage SDFT mechanism that bridges data-space conditioning and parameter-space adapta￾tion. Top panel: Demonstrations c act as a perturbation in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Jacobian Image Space Framework: Local linear duality between data-space and parameter-space operations under frozen weights (Stage 3). The input Jacobian Jx and parameter Jacobian Jθ map perturbations from their respective tangent spaces onto the shared output tangent space TyM. The intersection Im(Jx) ∩ Im(Jθ) (teal region) represents the functional equivalence set where targets can be achieved via either… view at source ↗
Figure 6
Figure 6. Figure 6: The Low-Rank Correspondence: ICL (left) induces low-rank updates [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The Continual Learning Correspondence: Data replay (left) constrains [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Conceptual Correspondence: Cooperative Data-Parameter Attacks. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Privacy Correspondence: Information-Theoretic Duality. The mu [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Testing-Time Security Correspondence: The product constraint [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The Composition Correspondence: Multiple data distributions [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Unified view of data-parameter correspondences on statistical mani [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
read the original abstract

Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved samples through selection, augmentation, or poisoning, while the latter tunes model weights via masking, quantization, or low-rank adaptation. This paper establishes a unified \emph{data-parameter correspondence} revealing these seemingly disparate operations as dual manifestations of the same geometric structure on the statistical manifold $\mathcal{M}$. Grounded in the Fisher-Rao metric $g_{ij}(\theta)$ and Legendre duality between natural ($\theta$) and expectation ($\eta$) parameters, we identify three fundamental correspondences spanning the model lifecycle: 1. Geometric correspondence: data pruning and parameter sparsification equivalently reduce manifold volume via dual coordinate constraints; 2. Low-rank correspondence: in-context learning (ICL) and LoRA adaptation explore identical subspaces on the Grassmannian $\mathcal{G}(r,d)$, with $k$-shot samples geometrically equivalent to rank-$r$ updates; 3. Security-privacy correspondence: adversarial attacks exhibit cooperative amplification between data poisoning and parameter backdoors, whereas protective mechanisms follow cascading attenuation where data compression multiplicatively enhances parameter privacy. Extending from training through post-training compression to inference, this framework provides mathematical formalization for cross-community methodology transfer, demonstrating that cooperative optimization integrating data and parameter modalities may outperform isolated approaches across efficiency, robustness, and privacy dimensions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 0 minor

Summary. The paper proposes a unified data-parameter correspondence for LLMs, framing data-centric operations (pruning, poisoning, ICL) and parameter-centric operations (sparsification, LoRA, backdoors) as dual manifestations of the same geometric structures on the statistical manifold M, grounded in the Fisher-Rao metric and Legendre duality between natural and expectation parameters. It identifies three correspondences across the model lifecycle and concludes that cooperative optimization integrating both modalities may outperform isolated approaches in efficiency, robustness, and privacy.

Significance. If the claimed geometric dualities can be rigorously derived and shown to yield measurable synergies, the framework could provide a unifying language for transferring techniques between data and model optimization communities, potentially inspiring new hybrid algorithms. The preliminary discussion highlights an underexplored connection but currently offers no derivations, bounds, or experiments to substantiate the performance claims.

major comments (4)
  1. [Abstract] Abstract: The central claim that the three correspondences constitute a 'mathematical formalization' is unsupported, as the manuscript provides no equations, volume elements, subspace mappings, or inequalities; all statements remain at the level of conceptual assertion without explicit use of g_ij(θ) or Legendre duality.
  2. [Abstract] Abstract (geometric correspondence): The assertion that data pruning and parameter sparsification 'equivalently reduce manifold volume via dual coordinate constraints' lacks any derivation of the volume form under the Fisher-Rao metric or the explicit dual constraints in θ and η coordinates that would establish equivalence.
  3. [Abstract] Abstract (low-rank correspondence): The claim that k-shot ICL samples and rank-r LoRA updates explore 'identical subspaces on the Grassmannian G(r,d)' is stated without constructing the embedding or proving the subspace identification, leaving the equivalence unverified.
  4. [Abstract] Abstract (security-privacy correspondence): The statements on cooperative amplification for attacks and cascading attenuation for protection provide no quantitative bounds, multiplicative factors, or inequalities demonstrating that joint operations exceed the sum of isolated effects, undermining the outperformance conclusion.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. Our manuscript is a preliminary discussion that proposes a conceptual framework rather than a fully derived theory. We acknowledge the lack of explicit equations and quantitative results and will revise to clarify the scope of our claims, temper overstated conclusions, and add outlines of the geometric concepts where feasible. Point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the three correspondences constitute a 'mathematical formalization' is unsupported, as the manuscript provides no equations, volume elements, subspace mappings, or inequalities; all statements remain at the level of conceptual assertion without explicit use of g_ij(θ) or Legendre duality.

    Authors: We agree that the manuscript remains at the conceptual level and does not contain explicit derivations or equations. The phrase 'mathematical formalization' refers to framing the correspondences within the established language of information geometry rather than providing a complete axiomatic treatment. In revision we will update the abstract and introduction to explicitly state the preliminary nature of the work, add a short section recalling the Fisher-Rao metric and Legendre duality with standard references, and remove any implication of full rigor. revision: yes

  2. Referee: [Abstract] Abstract (geometric correspondence): The assertion that data pruning and parameter sparsification 'equivalently reduce manifold volume via dual coordinate constraints' lacks any derivation of the volume form under the Fisher-Rao metric or the explicit dual constraints in θ and η coordinates that would establish equivalence.

    Authors: We acknowledge the absence of a derivation. The claimed equivalence is motivated by the standard duality between natural and expectation parameters, where a constraint in one coordinate system corresponds to a dual constraint in the other. For the revision we will insert a brief explanatory paragraph describing how the volume element induced by the Fisher-Rao metric is affected by coordinate restrictions in each parameterization, citing classical results, while noting that a full proof of equivalence is left for subsequent work. revision: yes

  3. Referee: [Abstract] Abstract (low-rank correspondence): The claim that k-shot ICL samples and rank-r LoRA updates explore 'identical subspaces on the Grassmannian G(r,d)' is stated without constructing the embedding or proving the subspace identification, leaving the equivalence unverified.

    Authors: The identification is offered as a geometric analogy based on both operations restricting the effective dimensionality of updates on the manifold. We will revise the relevant section to sketch the conceptual mapping (ICL samples influencing the posterior via a low-dimensional span, LoRA via column space of the update) and will explicitly label the claim as a conjecture requiring future verification rather than an established result. revision: partial

  4. Referee: [Abstract] Abstract (security-privacy correspondence): The statements on cooperative amplification for attacks and cascading attenuation for protection provide no quantitative bounds, multiplicative factors, or inequalities demonstrating that joint operations exceed the sum of isolated effects, undermining the outperformance conclusion.

    Authors: We agree that no quantitative bounds or inequalities are supplied. The statements are qualitative observations suggested by the duality framework. In the revision we will rephrase these passages to present the amplification and attenuation effects as hypothesized interactions rather than demonstrated superior performance, and we will add a short discussion of possible future approaches to obtaining bounds using information-geometric divergences. revision: yes

Circularity Check

0 steps flagged

No significant circularity; conceptual unification via external information geometry

full rationale

The paper proposes a data-parameter correspondence by applying standard, externally established tools (Fisher-Rao metric and Legendre duality on the statistical manifold) to map existing operations. These geometric primitives are not defined in terms of the claimed correspondences, nor do any equations reduce the outperformance suggestion to a tautology or fitted input. No self-citations, uniqueness theorems from the authors, or ansatzes smuggled via prior work appear in the abstract or described structure. The three correspondences are presented as identifications rather than derivations that presuppose their own conclusions, and the 'may outperform' statement is an interpretive extension, not a forced equality by construction. This is a normal, non-circular preliminary discussion.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on standard information geometry applied to LLMs; no new free parameters are introduced in the abstract, but the data-parameter correspondence itself is postulated without independent grounding.

axioms (2)
  • standard math Fisher-Rao metric defines distances on the statistical manifold M
    Invoked to ground geometric correspondence between data and parameter operations
  • standard math Legendre duality between natural parameters theta and expectation parameters eta
    Used to identify dual manifestations of data and parameter manipulations
invented entities (1)
  • data-parameter correspondence no independent evidence
    purpose: Unify data-centric and parameter-centric operations as dual geometric structures
    Postulated as the core new framework linking pruning, sparsification, ICL, LoRA, and security mechanisms

pith-pipeline@v0.9.0 · 5533 in / 1307 out tokens · 32063 ms · 2026-05-10T06:04:34.380396+00:00 · methodology

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

84 extracted references · 15 canonical work pages · 4 internal anchors

  1. [1]

    Data optimization for llms: A survey,

    O. Wu, “Data optimization for llms: A survey,”TechXiv, 2025

    2025

  2. [2]

    Yu, and Jianfeng Gao

    G. Tie, Z. Zhao, D. Songet al., “A survey on post-training of large language models,”arXiv:2503.06072, 2025

    work page arXiv 2025

  3. [3]

    Efficient large language models: A survey,

    Z. Wan, X. Li, H. Zhanget al., “Efficient large language models: A survey,”Transactions on Machine Learning Research, 2024

    2024

  4. [4]

    Efficient attention mechanisms for large language models: A survey

    Y . Sun, Z. Li, Y . Zhang, T. Pan, B. Donget al., “Efficient attention mechanisms for large language models: A survey,”arXiv:2507.19595, 2025

    work page arXiv 2025

  5. [5]

    Parameter-efficient fine-tuning of large-scale pre-trained language models,

    N. Ding, Y . Qin, G. Yang, F. Wei, Z. Yanget al., “Parameter-efficient fine-tuning of large-scale pre-trained language models,”Nature Machine Intelligence, vol. 5, no. 3, pp. 220–235, 2023

    2023

  6. [6]

    LoRA: Low-Rank Adaptation of Large Language Models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Liet al., “LoRA: Low-rank adaptation of large language models,”arXiv:2106.09685, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  7. [7]

    Blockpruner: Fine- grained pruning for large language models,

    L. Zhong, F. Wan, R. Chen, X. Quan, and L. Li, “Blockpruner: Fine- grained pruning for large language models,” inFindings of ACL, 2025, pp. 4562–4574

    2025

  8. [8]

    LRQuant: Learn- able and robust post-training quantization for large language models,

    J. Zhao, M. Zhang, C. Zeng, M. Wang, X. Liuet al., “LRQuant: Learn- able and robust post-training quantization for large language models,” inACL, 2024

    2024

  9. [9]

    Security and privacy challenges of large language models: A survey,

    B. C. Das, M. H. Amini, and Y . Wu, “Security and privacy challenges of large language models: A survey,”ACM Comput. Surv., vol. 57, no. 6, 2025

    2025

  10. [10]

    Influence functions for efficient data selection in reasoning,

    P. Humane, P. Cudrano, D. Z. Kaplan, M. Matteucci, S. Chakraborty et al., “Influence functions for efficient data selection in reasoning,” 2025

    2025

  11. [11]

    A survey on data selection for llm instruction tuning,

    B. Zhang, J. Wang, Q. Du, J. Zhang, Z. Tuet al., “A survey on data selection for llm instruction tuning,”Journal of Artificial Intelligence Research, vol. 83, no. 32, pp. 1–45, 2025

    2025

  12. [12]

    Efficient prompting methods for large language models: A survey,

    K. Chang, S. Xu, C. Wang, Y . Luo, T. Xiaoet al., “Efficient prompting methods for large language models: A survey,” 2024

    2024

  13. [13]

    On large language models safety, security, and privacy: A survey,

    R. Zhang, H.-W. Li, X.-Y . Qian, W.-B. Jiang, and H.-X. Chen, “On large language models safety, security, and privacy: A survey,”Journal of Electronic Science and Technology, vol. 23, no. 1, p. 100301, 2025

    2025

  14. [14]

    A survey of recent backdoor attacks and defenses in large language models,

    S. Zhao, M. Jia, Z. Guo, L. Gan, X. Xuet al., “A survey of recent backdoor attacks and defenses in large language models,”Transactions on Machine Learning Research, 2025

    2025

  15. [15]

    Ai security in the foundation model era: A comprehensive survey from a unified perspective,

    S. Luan and Z. Wang, “Ai security in the foundation model era: A comprehensive survey from a unified perspective,”TechRxiv, 2025

    2025

  16. [16]

    LowRA: Accurate and efficient LoRA fine-tuning of LLMs under 2 bits,

    Z. Zhou, Q. Zhang, H. Kumbong, and K. Olukotun, “LowRA: Accurate and efficient LoRA fine-tuning of LLMs under 2 bits,” inICML, 2025, pp. 79 570–79 594

    2025

  17. [17]

    Adalora: Adaptive budget allocation for parameter-efficient fine- tuning,

    Q. Zhang, M. Chen, A. Bukharin, N. Karampatziakis, P. Heet al., “Adalora: Adaptive budget allocation for parameter-efficient fine- tuning,” inICLR, 2023

    2023

  18. [18]

    Sbfa: Single sneaky bit flip attack to break large language models,

    J. Guo, C. Chakrabarti, and D. Fan, “Sbfa: Single sneaky bit flip attack to break large language models,”arXiv:2509.21843, 2025

    work page arXiv 2025

  19. [19]

    Fast model editing at scale,

    E. Mitchell, C. Lin, A. Bosselut, C. Finn, and C. D. Manning, “Fast model editing at scale,” inICLR, 2022

    2022

  20. [20]

    Automatic chain of thought prompting in large language models,

    Z. Zhang, A. Zhang, M. Li, and A. Smola, “Automatic chain of thought prompting in large language models,” inICLR, 2023

    2023

  21. [21]

    Information geometry of neural network pruning,

    I. Sato, M. Yamada, and M. Sugiyama, “Information geometry of neural network pruning,”Transactions on Machine Learning Research, 2023, also available as arXiv:2108.03783

    work page arXiv 2023

  22. [22]

    Dropout as data augmentation,

    X. Bouthillier, K. Konda, P. Vincent, and R. Memisevic, “Dropout as data augmentation,”arXiv:1506.08700, 2015

    work page arXiv 2015

  23. [23]

    Deep augmentation: Dropout as augmentation for self-supervised learning,

    R. Br ¨uel-Gabrielsson, T. Wang, M. Baradad, and J. Solomon, “Deep augmentation: Dropout as augmentation for self-supervised learning,” Transactions on Machine Learning Research, 2025

    2025

  24. [24]

    Regularizing deep networks with semantic data augmentation,

    Y . Wang, G. Huang, S. Song, X. Pan, Y . Xiaet al., “Regularizing deep networks with semantic data augmentation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3733– 3748, 2021

    2021

  25. [25]

    Self-Distillation Enables Continual Learning

    I. Shenfeld, M. Damani, J. H ¨ubotter, and P. Agrawal, “Self-distillation enables continual learning,”arXiv:2601.19897, 2026

    work page internal anchor Pith review arXiv 2026

  26. [26]

    Transformers learn in-context by gradient descent,

    J. von Oswald, E. Niklasson, E. Randazzo, J. Sacramento, A. Mord- vintsevet al., “Transformers learn in-context by gradient descent,” inInternational Conference on Machine Learning, 2023, pp. 35 151– 35 174

    2023

  27. [27]

    Why can GPT learn in-context? language models secretly perform gradient descent as meta- optimizers,

    D. Dai, Y . Sun, L. Dong, Y . Hao, S. Maet al., “Why can GPT learn in-context? language models secretly perform gradient descent as meta- optimizers,” inFindings of ACL, 2023, pp. 4005–4019

    2023

  28. [28]

    What learning algorithm is in-context learning? investigations with linear models,

    E. Aky ¨urek, D. Schuurmans, J. Andreas, T. Ma, and D. Zhou, “What learning algorithm is in-context learning? investigations with linear models,” inThe Eleventh International Conference on Learning Rep- resentations, 2023

    2023

  29. [29]

    Language models are few-shot learners,

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplanet al., “Language models are few-shot learners,” inNeurIPS, vol. 33, 2020, pp. 1877–1901

    2020

  30. [30]

    Rethinking the role of demonstrations: What makes in-context learning work?

    S. Min, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the role of demonstrations: What makes in-context learning work?” in EMNLP, 2022, pp. 11 048–11 064

    2022

  31. [31]

    Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

    P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashiet al., “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,”ACM Computing Surveys, vol. 55, no. 9, pp. 1–35, 2023

    2023

  32. [32]

    Parameter-efficient transfer learning for NLP,

    N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe et al., “Parameter-efficient transfer learning for NLP,” inICML, 2019, pp. 2790–2799

    2019

  33. [33]

    AdapterFu- sion: Non-destructive task composition for transfer learning,

    J. Pfeiffer, A. R ¨uckl´e, C. Poth, A. Kamath, I. Vuli ´cet al., “AdapterFu- sion: Non-destructive task composition for transfer learning,” inEACL, 2021, pp. 487–503

    2021

  34. [34]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Joneset al., “Attention is all you need,” inNeurIPS, vol. 30, 2017

    2017

  35. [35]

    Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

    Z. Dai, Z. Yang, Y . Yang, J. Carbonell, Q. V . Leet al., “Transformer- XL: Attentive language models beyond a fixed-length context,” arXiv:1901.02860, 2019

    work page Pith review arXiv 1901

  36. [36]

    Classifier-free diffusion guidance,

    J. Ho and T. Salimans, “Classifier-free diffusion guidance,” inNeurIPS 2022 Workshop on Deep Generative Models and Downstream Applica- tions, 2022

    2022

  37. [37]

    Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP,

    T. Schick, S. Udupa, and H. Sch ¨utze, “Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP,” inEMNLP, 2021, pp. 2128–2142

    2021

  38. [38]

    Energy-based out- of-distribution detection,

    Y . Du, H. Zhang, Y . Yang, J. Feng, N. Chenet al., “Energy-based out- of-distribution detection,” inNeurIPS, vol. 33, 2020, pp. 21 464–21 475

    2020

  39. [39]

    Energy-based text generation with contrastive learning,

    Y . Xiao, Y . Meng, P. Xie, Y . Liu, J. Zhanget al., “Energy-based text generation with contrastive learning,”arXiv:2106.10820, 2021

    work page arXiv 2021

  40. [40]

    Flashsampling: Fast and memory-efficient exact sampling,

    T. Ruiz, Z. Qin, Y . Zhang, X. Shen, Y . Zhonget al., “Flashsampling: Fast and memory-efficient exact sampling,”arXiv:2603.15854, 2026

    work page internal anchor Pith review arXiv 2026

  41. [41]

    Compiling machine learning programs via high-level tracing,

    R. Frostig, M. J. Johnson, and C. Leary, “Compiling machine learning programs via high-level tracing,”Systems for Machine Learning, pp. 23–24, 2018

    2018

  42. [42]

    Overcoming catastrophic forgetting in neural networks,

    J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardinset al., “Overcoming catastrophic forgetting in neural networks,”Proceedings of the National Academy of Sciences, vol. 114, no. 13, pp. 3521–3526, 2017

    2017

  43. [43]

    Amari,Information Geometry and Its Applications, ser

    S.-i. Amari,Information Geometry and Its Applications, ser. Applied Mathematical Sciences. Tokyo: Springer, 2016, vol. 194. IEEE JOURNALS & CONFERENCES 25

    2016

  44. [44]

    New insights and perspectives on the natural gradient method,

    J. Martens, “New insights and perspectives on the natural gradient method,”Journal of Machine Learning Research, vol. 21, no. 1, pp. 1–76, 2020

    2020

  45. [45]

    Gradient based sample selection for online continual learning,

    R. Aljundi, M. Lin, B. Goujaud, and Y . Bengio, “Gradient based sample selection for online continual learning,” inAdvances in Neural Information Processing Systems, vol. 32, 2019

    2019

  46. [46]

    Badclip++: Stealthy and persistent backdoors in multimodal contrastive learning,

    S. Liang, Y . Jing, Y . Wang, J. Huang, E. chien Changet al., “Badclip++: Stealthy and persistent backdoors in multimodal contrastive learning,” arXiv:2602.17168, 2026

    work page arXiv 2026

  47. [47]

    Fisher information guided purification against backdoor attacks,

    N. Karim, A. Al Arafat, A. S. Rakin, Z. Guo, and N. Rahnavard, “Fisher information guided purification against backdoor attacks,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024, pp. 4435–4449

    2024

  48. [48]

    Parameter disparities dissection for backdoor defense in heterogeneous federated learning,

    W. Huang, M. Ye, Z. Shi, G. Wan, H. Liet al., “Parameter disparities dissection for backdoor defense in heterogeneous federated learning,” in Advances in Neural Information Processing Systems, vol. 37, 2024

    2024

  49. [49]

    Dwork and A

    C. Dwork and A. Roth,The Algorithmic Foundations of Differential Privacy, ser. Foundations and Trends in Theoretical Computer Science. Hanover, MA: Now Publishers, 2014, vol. 9, no. 3–4

    2014

  50. [50]

    What can we learn privately?

    S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, “What can we learn privately?”SIAM Journal on Computing, 2011

    2011

  51. [51]

    Subsampled R ´enyi differential privacy and analytical moments accountant,

    Y .-X. Wang, B. Balle, and S. P. Kasiviswanathan, “Subsampled R ´enyi differential privacy and analytical moments accountant,” inAISTATS, 2019

    2019

  52. [52]

    Scalable extraction of training data from (production) language models: 2025 update and mitigations,

    M. Nasr, N. Carlini, J. Hayaseet al., “Scalable extraction of training data from (production) language models: 2025 update and mitigations,” inIEEE Symposium on Security and Privacy (S&P). IEEE, 2025

    2025

  53. [53]

    Parameter inversion attacks against foundation models via gradient matching and information leak- age,

    Y . Zhang, Z. Li, H. Wang, and J. Chen, “Parameter inversion attacks against foundation models via gradient matching and information leak- age,” inACM Conference on Computer and Communications Security (CCS). ACM, 2025

    2025

  54. [54]

    Information-theoretic privacy bounds for machine learning: Unified framework and applications to llms,

    Z. Wang, Y . Liu, and M. Li, “Information-theoretic privacy bounds for machine learning: Unified framework and applications to llms,”IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 2345–2360, 2025

    2025

  55. [55]

    Sufficient statistics privacy: Dimension- ality reduction and optimal noise calibration,

    X. Li, W. Chen, and T. Liu, “Sufficient statistics privacy: Dimension- ality reduction and optimal noise calibration,”IEEE Transactions on Information Theory, vol. 71, no. 2, pp. 1234–1250, 2025

    2025

  56. [56]

    Membership inference attacks against machine learning models,

    R. Shokri, M. Stronati, C. Song, and V . Shmatikov, “Membership inference attacks against machine learning models,” inIEEE Symposium on Security and Privacy (S&P). IEEE, 2017, pp. 3–18

    2017

  57. [57]

    Cascading privacy protection for distributed deep learning,

    Y . Guo, Y . Liu, and K. Zhang, “Cascading privacy protection for distributed deep learning,” inInternational Conference on Learning Representations (ICLR). OpenReview, 2025

    2025

  58. [58]

    Communication-efficient federated learning with sketching and privacy guarantees,

    Y . Duan, H. Chen, and Y . Xu, “Communication-efficient federated learning with sketching and privacy guarantees,” inACM Conference on Computer and Communications Security (CCS). ACM, 2025

    2025

  59. [59]

    Practical secure aggregation for privacy-preserving machine learning,

    K. Bonawitz, V . Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan et al., “Practical secure aggregation for privacy-preserving machine learning,” inProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS). Dallas, TX, USA: ACM, 2017, pp. 1175–1191

    2017

  60. [60]

    Protocols for secure computations,

    A. C.-C. Yao, “Protocols for secure computations,” inProceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science (FOCS), 1982, pp. 160–164

    1982

  61. [61]

    Baseline defenses for adversarial attacks against aligned language models,

    N. Jain, A. Schwarzschild, Y . Wen, J. Kirchenbauer, J. Geipinget al., “Baseline defenses for adversarial attacks against aligned language models,” inProc. NeurIPS Workshop on Socially Responsible Language Modelling Research, 2023

    2023

  62. [62]

    Robust large margin deep neural networks,

    J. Sokolic, R. Giryes, G. Sapiro, and M. R. D. Rodrigues, “Robust large margin deep neural networks,”IEEE Transactions on Signal Processing, vol. 65, no. 16, pp. 4265–4280, 2017

    2017

  63. [63]

    Pay attention to attention distribution: A new lo- cal lipschitz bound for transformers

    N. Yudin, A. Gaponov, S. Kudriashov, and M. Rakhuba, “Pay attention to attention distribution: A new local lipschitz bound for transformers,” arXiv:2507.07814, 2025

    work page arXiv 2025

  64. [64]

    Understanding and improving continuous llm adversarial training via in-context learning theory,

    S. Fu and D. Wang, “Understanding and improving continuous llm adversarial training via in-context learning theory,” inInternational Conference on Learning Representations, 2026

    2026

  65. [65]

    The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?

    M. Bhatt, S. Munshi, V . S. Narajala, I. Habler, A. Al-Kahfahet al., “The defense trilemma: Why prompt injection defense wrappers fail?” arXiv:2604.06436, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  66. [66]

    Linear mode connectivity and the lottery ticket hypothesis,

    J. Frankle, G. K. Dziugaite, D. M. Roy, and M. Carbin, “Linear mode connectivity and the lottery ticket hypothesis,” inInternational Conference on Machine Learning. PMLR, 2020, pp. 3259–3269

    2020

  67. [67]

    The role of permutation invariance in linear mode connectivity of neural networks,

    R. Entezari, H. Sedghi, O. Saukh, and B. Neyshabur, “The role of permutation invariance in linear mode connectivity of neural networks,” inInternational Conference on Learning Representations, 2022

    2022

  68. [68]

    Git re-basin: Merging models modulo permutation symmetries,

    S. K. Ainsworth, J. Hayase, and S. Srinivasa, “Git re-basin: Merging models modulo permutation symmetries,” inInternational Conference on Learning Representations, 2023

    2023

  69. [69]

    Data mixing laws: Op- timizing data mixtures by predicting language modeling performance,

    J. Ye, P. Liu, T. Sun, Y . Zhou, J. Zhanet al., “Data mixing laws: Op- timizing data mixtures by predicting language modeling performance,” inICLR, 2025

    2025

  70. [70]

    Adaptive data mixing for domain- specific fine-tuning of large language models,

    X. Zhang, Y . Liu, and W. Chen, “Adaptive data mixing for domain- specific fine-tuning of large language models,” inInternational Confer- ence on Learning Representations (ICLR), 2025

    2025

  71. [71]

    Ties-merging: Resolving interference when merging models,

    P. Yadav, T. Yu, M. Li, D. Chen, D. Jacobset al., “Ties-merging: Resolving interference when merging models,” inAdvances in Neural Information Processing Systems (NeurIPS), vol. 37, 2024

    2024

  72. [72]

    Language models are super mario: Absorbing abilities from homologous models as a free lunch,

    L. Yu, B. Yu, H. Yu, F. Huang, and Y . Li, “Language models are super mario: Absorbing abilities from homologous models as a free lunch,” inInternational Conference on Machine Learning (ICML), 2024, pp. 41 506–41 527

    2024

  73. [73]

    Model breadcrumbs: Scaling multi-task model merging with sparse masks,

    M. Davari and E. Belilovsky, “Model breadcrumbs: Scaling multi-task model merging with sparse masks,” inECCV, 2024, pp. 270–287

    2024

  74. [74]

    Mix data or merge models? balancing the helpfulness, honesty, and harmlessness of large language model via model merging,

    J. Yang, D. Jin, A. Tang, L. Shen, D. Zhuet al., “Mix data or merge models? balancing the helpfulness, honesty, and harmlessness of large language model via model merging,” inAdvances in Neural Information Processing Systems, 2025

    2025

  75. [75]

    Multi-task code llms: Data mix or model merge?

    Y . Zhang, M. Liu, Y . Wang, J. Chen, Y . Zhaoet al., “Multi-task code llms: Data mix or model merge?” inICSE 2026 LLM4Code Workshop, 2026

    2026

  76. [76]

    Mergemix: Optimizing mid-training data mixtures via learnable model merging,

    J. Wang, C. Tian, K. Chen, Z. Liu, J. Maoet al., “Mergemix: Optimizing mid-training data mixtures via learnable model merging,” arXiv:2601.17858, 2026

    work page arXiv 2026

  77. [77]

    Decouple searching from training: Scaling data mixing via model merging for large language model pre-training,

    S. Li, F. Zhao, K. Zhao, J. Ye, H. Liuet al., “Decouple searching from training: Scaling data mixing via model merging for large language model pre-training,”arXiv:2602.00747, 2026

    work page arXiv 2026

  78. [78]

    Model soups: averaging weights of multiple fine-tuned models improves accu- racy without increasing inference time,

    M. Wortsman, G. Ilharco, M. Li, J. W. Kim, H. Hajishirziet al., “Model soups: averaging weights of multiple fine-tuned models improves accu- racy without increasing inference time,” inICML, 2022, pp. 23 965– 23 998

    2022

  79. [79]

    Editing models with task arithmetic,

    G. Ilharco, M. T. Ribeiro, M. Wortsman, S. Gururangan, L. Schmidt et al., “Editing models with task arithmetic,”ICLR, 2023

    2023

  80. [80]

    Task vectors in the wild: Analyzing and controlling model merging interference,

    J. Ortiz, P. Liang, and T. Hashimoto, “Task vectors in the wild: Analyzing and controlling model merging interference,” inProceedings of the 13th International Conference on Learning Representations (ICLR 2025), 2025

    2025

Showing first 80 references.