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REVIEW 3 major objections 5 minor 4 cited by

Position: Foundation Models Need Digital Twin Representations

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper argues that foundation models should abandon token representations for digital twin representations—outcome-driven encodings of physical entities and interactions—because tokenization's limits cannot be fixed by scaling alone.

desk verdict A clear, honest position paper whose agenda is reasonable but whose load-bearing claim that token limitations cannot be scaled away is asserted, not shown. read the letter →

arxiv 2505.03798 v1 pith:7EQON5KG submitted 2025-05-01 cs.LG cs.AI

Yiqing Shen , Hao Ding , Lalithkumar Seenivasan , Tianmin Shu , Mathias Unberath This is my paper · ORCID
classification cs.LGcs.AI
keywords foundationmodelstokenrepresentationsdigitaltwinmultimodallearningphysicalgroundingcausalreasoninginterpretabilityrepresentation
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

This is a position paper, not an experimental study. It argues that the discrete tokens used by current multimodal foundation models—image patches, video frames, subword units—fragment continuous physical processes and force models to learn world knowledge, causality, and cross-modal semantics purely from statistical correlation. The paper's central claim is that these limitations are inherent to tokenization and cannot be overcome by scaling model size or data, so the field should adopt digital twin (DT) representations: outcome-driven digital encodings of task-specific entities, properties, and interactions, grounded in physical and domain constraints. If the claim is right, foundation models built on DT representations would be more data-efficient, physically consistent, causally reliable, and interpretable. The paper supports the position with five assumptions and existing examples, but it does not run a head-to-head comparison between token and DT representations.

What carries the argument

The carrying mechanism is the digital twin representation, formalized as a set $R=\{r_0,\dots,r_m\}$ of outcome-driven digital representations derived from raw sensor data $S=\{s_0,\dots,s_n\}$ through a pipeline $R=\Phi(P,S)=\Phi_e(S,\Phi_d(P))$, where $\Phi_d$ is outcome-driven representation design and $\Phi_e$ is representation extraction; the foundation model $\Theta$ then produces target outputs $T=\Theta(R)$. This formalization does the argument's work by contrasting explicit, physically grounded construction with token representations' 'direct vectorization' of raw data into uniform patches, frames, or subwords. The paper also leans on a cited scaling law $S(N)\approx (c/N)^\alpha$ for vision-token counts to argue that adding more tokens cannot recover fine-grained world knowledge.

What would settle it

A matched experiment on the same benchmark suite—fine-grained spatio-temporal video reasoning, counterfactual physical prediction, and cross-modal semantic alignment—with a token-based model and a DT-representation model trained on identical data and compute would settle the claim; if the token model performs comparably or better, the assertion that tokenization is the bottleneck is falsified.

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Extended reading notes

Core claim

The central claim is that token representations have inherent limitations that scaling cannot repair, and that digital twin representations offer a paradigm shift. A DT representation is defined as an outcome-driven digital representation extracted from raw data that serves as a building block of a digital twin, capturing task-specific entities, their geometric and physical properties, and their interactions under domain constraints. The paper proposes that foundation models should consume DT representations rather than tokens, which would preserve the continuous nature of physical processes, encode explicit causal mechanisms and physical laws, provide a unified semantic space across modalities, and make reasoning chains transparent. It organizes this into five assumptions—better real-world knowledge encoding, practical data synthesis, causal reasoning, unified semantics, and interpretability—each illustrated with existing systems, but the paper is explicit that these are assumptions forming a position rather than demonstrated results.

Load-bearing premise

The paper assumes the failures it blames on tokens are caused by the representation itself and would not disappear with better training objectives, larger data, or new architectures; if any token-based model overcomes them, the central motivation for switching to digital twins collapses.

Editorial extensions

If this is right

  • If the claim is right, multimodal foundation models could be built to use physical laws and domain constraints directly, so they would not need to rediscover geometry, kinematics, or conservation laws from data.
  • DT-based synthetic data generation could produce physically valid long-tail scenarios—like adverse weather driving conditions—and close part of the sim-to-real gap for robotics and autonomous driving.
  • Encoding causal structure into representations could improve interventional, attributional, and counterfactual reasoning, reducing hallucination and spurious correlations.
  • A unified semantic space grounded in shared physical and geometric properties could make cross-modal alignment more robust than aligning visual patches with text tokens.
  • Physically grounded internal states would make model decisions traceable, improving verification and safety in medical, robotic, and industrial applications.

Reading between the lines

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

  • Editorial inference: the strongest test of the paper's position is a hybrid architecture that keeps tokens but injects structured physical or causal constraints, because if that matches DT representations, the bottleneck is the objective rather than the representation.
  • Editorial inference: DT representations are defined as outcome-driven and task-specific, which may tension with foundation-model generality; a key open question is whether one DT representation can serve many downstream tasks without being redesigned per task.
  • Editorial inference: the paper's examples mostly encode the answer into the representation (e.g., object masks plus geometry), so a fair benchmark must separate the value of the representation from the value of the task-specific information already contained in it.
  • Editorial inference: a concrete next step is to measure whether DT representations reduce the data needed to reach a given level of physical or causal reasoning, since the paper claims data efficiency but offers no quantitative comparison.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper argues that token representations in foundation models have inherent limitations that cannot be overcome by scaling, and proposes digital twin (DT) representations as an alternative. It defines DT representations, sketches a formalization, and lists five assumptions about how DT representations would improve knowledge encoding, data synthesis, causal reasoning, semantic unification, and interpretability. The paper is an explicit position paper, with the five advantages labeled as assumptions and an 'Alternative Views' section acknowledging counterarguments.

Significance. If the central claim were established, the paper would motivate a significant reorientation of multimodal foundation model research. The paper is honest in labeling its five advantages as assumptions, and it provides concrete examples from robotics, surgery, and autonomous driving. However, the key premise that token limitations are insurmountable by scaling is asserted rather than demonstrated, and the formalization is too loose to carry the argument. As a position paper, it has the merit of opening a debate, but it currently overstates its case.

major comments (3)
  1. [Abstract; §1; §4.1] The central claim that token limitations 'cannot be overcome by simply scaling up model size or expanding datasets' is load-bearing but unsupported. The only cited evidence is the token-count scaling law S(N) ≈ (c/N)^α in §4.1, which concerns the number of vision tokens N, not model parameters or dataset size. This does not test the scaling claim, and no experiment controls for architecture or training objective. The Alternative Views section even concedes that token models might overcome these limitations through better architectures and training methods. The paper should either provide direct evidence against model/data scaling or reframe the claim as a hypothesis.
  2. [§3.2 and §4.1/§4.3] There is a circularity between the definition of DT representations and the claimed advantages. Definition 3.2 defines DT representations as 'outcome-driven digital representations' that capture task-specific properties such as geometric and physical constraints, and Assumptions 1 and 3 then attribute to DT representations the ability to encode exactly these physical and causal properties. The advantages are thus partly built into the definition. The paper should define DT representations in a way that does not presuppose the benefits, or the assumptions should be stated independently of the definition.
  3. [§3.2] The attempted formalization is not actually used. The equation R = Φ(P, S) = Φe(S, Φd(P)) is never employed to derive any of the five assumptions, and the functions Φd and Φe are not specified with any formal semantics. This makes the formalization decorative rather than load-bearing. Either the formalization should be developed into a framework with stated properties, or it should be removed and the position stated directly.
minor comments (5)
  1. [§2, 'Token Representation for Text'] Typo: 'Charecter-level' should be 'Character-level'; also 'WorkPiece' should be 'WordPiece'.
  2. [Figure 1 and §3.1] The figure caption contains 'Phyical Process' instead of 'Physical Process', and §3.1 refers to 'Fig. 3.1' although the figure is numbered Figure 1.
  3. [§3.2] The variables m and n are not defined; the paper should specify that m and n are the numbers of representations and raw data modalities, respectively.
  4. [Throughout] The paper alternates between 'FMs' and 'LLMs' without a clear distinction; both terms should be defined at first use and used consistently.
  5. [§5] The conclusion lists future research directions but gives no concrete evaluation benchmarks or success criteria for DT representations, which would strengthen the argument as a position paper.

Circularity Check

1 steps flagged · score 4.0 of 10

One claimed DT advantage is definitional (DT representations are stipulated to encode physical constraints, then credited with encoding world knowledge), but the paper labels its benefits as assumptions and its central recommendation does not reduce to a fitted result or self-citation chain.

  1. self definitional [Definition 3.2 / Section 3.2 formalization; Section 4.1 Assumption 1]
    "where r_i is one modality of outcome-driven digital representation of the physical process that encodes task-specific properties, such as identification, geometric property, or physical constraint of entities of interest or interactions among them ... We assume that FMs building upon DT representations can achieve higher-level reasoning by explicitly incorporating world knowledge into the representation rather than forcing them to learn from scratch as statistical patterns."

    The formalization stipulates that DT representations encode task-specific physical/geometric constraints and interactions. Assumption 1 then credits DT representations with 'explicitly encoding real-world knowledge and domain-specific constraints' and with 'explicitly incorporating world knowledge into the representation.' That property is the defining property, so the descriptive half of the claimed advantage is true by stipulation, not by empirical result. The paper mitigates this by calling it an 'assumption' and the empirical half—that FMs actually reason better when consuming such representations—is not entailed by the definition. This is therefore partial, definitional circularity rather than a forced derivation of the whole position.

full rationale

This is a position paper, not an empirical derivation chain. Its central recommendation—that the community should consider DT representations—rests on an asserted impossibility claim about token scaling plus five explicitly labeled assumptions. The impossibility claim is supported only by a token-count scaling law S(N)≈(c/N)^α (Section 4.1, ref [49]) and is weakened by the paper's own Alternative Views paragraph, but that is an evidence-quality problem, not circularity. The authors' self-citations [22,23,24,71,72] are used as illustrative examples of DT representations and are not the unique load-bearing justification for the position. The concrete circularity is the definitional overlap noted above: the formalization builds 'physical constraint' and 'interactions' into the definition of r_i, so Assumption 1's claim that DT representations encode world knowledge is partly true by construction. Because the paper labels this as an assumption and the core recommendation would stand or fall on empirical evaluation rather than on this definitional entailment, the circularity score is moderate rather than severe.

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

The paper's central position rests on unverified domain assumptions about the causal role of tokenization and the feasibility of outcome-driven representation construction. There are no free parameters because there is no empirical fitting. No entities are invented; the digital twin concept is repurposed from prior literature. The axioms below are the load-bearing premises that would need to be tested or shown for the position to be more than an agenda.

assumptions (4)
  • domain assumption Tokenization is the primary cause of the listed foundation model limitations, and scaling cannot overcome them.
    Stated in the Abstract and Section 1; supported by a cited weak scaling law S(N) approximately (c/N)^alpha for vision tokens (Section 4.1, reference 49) but not by a controlled comparison.
  • domain assumption Explicit domain knowledge (physical laws, causal mechanisms, semantic relationships) can be encoded into representations without losing information or introducing errors.
    Invoked in Section 4.1 (encode real-world knowledge) and Section 4.3 (encode causal mechanisms). No proof or example shows this is feasible for general tasks at scale.
  • ad hoc to paper An outcome-driven representation design process Phi_d exists for arbitrary tasks and produces representations that preserve task-relevant information.
    Introduced in Definition 3.2 and the formalization R = Phi_e(S, Phi_d(P)) in Section 3.2. The paper does not show how such a design process is constructed for a general domain.
  • domain assumption The five claimed advantages of DT representations (Assumptions 1 through 5) would hold in practice if the representations were adopted.
    Section 4 explicitly labels these as assumptions and provides only anecdotal or self-cited evidence (references 23, 24, 71, 72).

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Cite this review

Pith. "Pith review of Position: Foundation Models Need Digital Twin Representations." pith.science (2026). https://pith.science/paper/7EQON5KG

@misc{pith2026250503798,
  author       = {Pith},
  title        = {Pith review of: Position: Foundation Models Need Digital Twin Representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7EQON5KG}},
  note         = {Machine review of arXiv:2505.03798}
}
read the original abstract

Current foundation models (FMs) rely on token representations that directly fragment continuous real-world multimodal data into discrete tokens. They limit FMs to learning real-world knowledge and relationships purely through statistical correlation rather than leveraging explicit domain knowledge. Consequently, current FMs struggle with maintaining semantic coherence across modalities, capturing fine-grained spatial-temporal dynamics, and performing causal reasoning. These limitations cannot be overcome by simply scaling up model size or expanding datasets. This position paper argues that the machine learning community should consider digital twin (DT) representations, which are outcome-driven digital representations that serve as building blocks for creating virtual replicas of physical processes, as an alternative to the token representation for building FMs. Finally, we discuss how DT representations can address these challenges by providing physically grounded representations that explicitly encode domain knowledge and preserve the continuous nature of real-world processes.

Figures

Figures reproduced from arXiv: 2505.03798 by the authors.

Figure 1
Figure 1. The illustration of the DT paradigm as well as the difference and relation between DT [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.