Everything Tokens: Multifaceted Token Systems

• Everything Tokens are tokenized interfaces that compress complex systems—such as AI workflows, multimodal data, and financial assets—into unified, manipulable units.
• They enable specialized applications in AI by facilitating efficient multimodal grounding, retrieval, and optimization using engineered token vocabularies.
• They function as accounting units linking computation, memory, latency, energy, and economic value, thus optimizing both technical and financial processes.

Searching arXiv for papers on “Everything Tokens” and closely related uses of the term across AI and tokenization. “Everything Tokens” denotes no single canonical construct. In contemporary research, the expression is used across several technical literatures to describe at least three distinct but related ideas: a composite token that represents an entire heterogeneous asset as a fixed bundle of component claims; a special or learned token vocabulary that makes multimodal perception, grounding, or generation tractable inside Transformer architectures; and a system-level accounting unit through which AI computation, memory, latency, energy, pricing, and workflow allocation are measured and optimized. Taken together, these usages suggest that tokens are increasingly treated not merely as representational atoms, but as interfaces through which whole assets, workflows, images, and economic systems are made composable, measurable, and governable (Borjigin et al., 15 Aug 2025, Wang et al., 2024, Ren et al., 11 Dec 2025, Zhu, 10 Jun 2026).

1. Polysemy and recurrent structure

The term’s polysemy is itself technically significant. In one line of work, an “Everything Token” is literally the whole-asset token in a two-tier tokenization architecture; in another, a global token such as $[CLS]$ is argued to “tell everything needed” for efficient token selection; in a third, tokens are the practical accounting unit for AI systems and enterprises; and in yet another, a single token-based architecture is presented as handling “everything” within a task family such as zero-shot sketch-based image retrieval (Borjigin et al., 15 Aug 2025, Wang et al., 2024, Zhu, 10 Jun 2026, Lin et al., 2023).

Domain	Meaning of “Everything Tokens”	Representative papers
Foundation-model economics	Tokens as the accounting unit linking computation, memory, energy, pricing, and allocation	(Zhu, 10 Jun 2026, Salim et al., 20 Jan 2026)
Multimodal grounding and compression	Special tokens or spatial vocabularies that organize 2D reasoning and token selection	(Wang et al., 2024, Ren et al., 11 Dec 2025, Bousselham et al., 2023)
Cross-modal retrieval and video generation	Token correspondences or condensed clip tokens used as reusable task-wide abstractions	(Lin et al., 2023, Ouyang et al., 21 Jul 2025)
Real-world asset tokenization and DeFi	Composite whole-asset token backed by a bundle of lower-tier claims; recursively nested token claims	(Borjigin et al., 15 Aug 2025, Harrigan et al., 2024)
Payments and authentication	Bearer or capability tokens replacing identity-centric credential models	(Goodell, 2022, Dykstra et al., 31 Mar 2025, Bockelman et al., 31 Mar 2025)

A recurrent structural pattern nonetheless appears across these disparate contexts. Tokens are repeatedly used to compress a complex substrate into a manipulable interface: the substrate may be a long prompt history, an image plane, a video clip, a heterogeneous project, a payment claim, or an authorization policy. This suggests that the unifying theme is less “everything becomes one token” than “whole systems are increasingly administered through tokenized interfaces.”

2. Tokens as accounting units in AI systems and software engineering

The most explicit generalization appears in AI tokenomics, where tokens are formalized as the practical accounting unit of foundation-model systems. A tokenizer is written as $\tau:\mathcal X\rightarrow V^{*}$, with $\tau(x)=(t_1,\ldots,t_n)$ and $n=|\tau(x)|$, and the total token footprint is decomposed as

$T_{\mathrm{tot}} = T_I + T_C + T_R + T_O + T_H,$

where $T_I$, $T_C$, $T_R$, $T_O$, and $T_H$ denote input, context, retrieval, output, and hidden reasoning tokens. The same framework links token counts to compute, memory, latency, and energy through simplified mappings such as $\tau:\mathcal X\rightarrow V^{*}$0, $\tau:\mathcal X\rightarrow V^{*}$1, and $\tau:\mathcal X\rightarrow V^{*}$2, while also insisting that token expenditure and economic value are distinct: value depends on marginal productivity, workflow position, hidden reasoning activity, risk, and downstream propagation effects (Zhu, 10 Jun 2026).

This accounting perspective becomes concrete in agentic software engineering. In a trace-based study of 30 ChatDev software-development tasks executed with the GPT-5 reasoning model gpt-5-2025-08-07, LLM calls were mapped from ChatDev phases onto six SDLC stages—Design, Coding, Code Completion, Code Review, Testing, and Documentation—and token usage was aggregated by stage and token type. The headline result was that Code Review accounted for 59.4% of tokens on average, while input tokens accounted for 53.9% overall, compared with 24.4% output and 21.6% reasoning. Coding was the outlier stage: it was output-heavy, with 58.0% output and only 6.9% input, whereas Documentation was the most input-heavy stage at 80.2% input. The paper interprets this pattern as the “Cost of Conversation”: in current LLM multi-agent software engineering, the dominant expense lies not in initial code generation but in repeated context transmission, refinement, and verification loops (Salim et al., 20 Jan 2026).

This empirical result matters because it corrects an intuitive but incomplete view of AI cost. High token consumption is not automatically concentrated where visible content is produced. In the ChatDev study, Design averaged only 2.4% of tokens and Coding 8.6%, whereas Code Completion averaged 26.8% in the runs where it occurred and Documentation 20.1%. A plausible implication is that token-aware optimization in agentic systems must focus less on isolated generation kernels and more on workflow orchestration, context compression, and verification protocols.

3. Global tokens, spatial vocabularies, and grounding in multimodal models

A second major meaning of “Everything Tokens” concerns the claim that a specially structured token can organize a large multimodal computation. In training-free multimodal LLM compression, the visual encoder’s $\tau:\mathcal X\rightarrow V^{*}$3 token is argued to provide a strong prior over which visual patch tokens matter for downstream generation. VTC-CLS computes importance from $\tau:\mathcal X\rightarrow V^{*}$4-to-patch attention inside the visual encoder, optionally ensembles the last $\tau:\mathcal X\rightarrow V^{*}$5 layers, and prunes low-scoring visual tokens before they enter the LLM. In LLaVA-1.5-7B, this produces state-of-the-art training-free compression among the compared methods: at 256 visual tokens the average score is 61.7 versus 60.5 for FastV and 60.6 for PruMerge+; at 128 tokens it is 60.5 versus 56.6 and 58.6; and at 64 tokens it is 58.4 versus 45.3 and 56.3. The paper is careful, however, not to prove that $\tau:\mathcal X\rightarrow V^{*}$6 literally contains all image information; rather, it shows that $\tau:\mathcal X\rightarrow V^{*}$7-derived attention is sufficient to preserve most downstream performance after aggressive token pruning (Wang et al., 2024).

A more explicitly spatial construction appears in GETok, which extends an MLLM vocabulary with learnable 2D tokens:

$\tau:\mathcal X\rightarrow V^{*}$8

$\tau:\mathcal X\rightarrow V^{*}$9

and

$\tau(x)=(t_1,\ldots,t_n)$0

Grid tokens provide coarse anchors on an $\tau(x)=(t_1,\ldots,t_n)$1 lattice, while offset tokens support local refinement. Under supervised fine-tuning, GETok improves Qwen2.5-VL-7B on referring expression comprehension from 86.6 to 88.2 average [email protected] and from 69.3 to 72.6 average [email protected]; on referring expression segmentation, the average rises from 65.7 for Qwen2.5-VL-7B to 68.2 for full GETok-SFT. A notable ablation shows that a $\tau(x)=(t_1,\ldots,t_n)$2 grid plus offsets yields REC/RES scores of 72.6/68.2 with average mask token length 9.2, outperforming a pure $\tau(x)=(t_1,\ldots,t_n)$3 grid at lower vocabulary cost (Ren et al., 11 Dec 2025).

These approaches should be distinguished from methods that keep all patch tokens but alter their interaction geometry. GEM, the “Grounding Everything Module,” does not collapse perception into a single summary token. Instead it reuses pretrained $\tau(x)=(t_1,\ldots,t_n)$4 projections to compute self-self attention over all patch tokens, interprets iterative self-self attention as clustering, and improves zero-shot open-vocabulary localization without finetuning. With ViT-B/16 CLIP, GEM reaches 46.2 mIoU on PascalVOC, 32.6 on Pascal Context, and 15.7 on ADE20K; with MetaCLIP the corresponding scores are 46.8, 34.5, and 17.1. On OpenImages-V7 point prediction, GEM-CLIP reaches 50.9 p-mIoU and GEM-MetaCLIP 51.9, exceeding CLIPSurgery’s 47.8 (Bousselham et al., 2023).

The common lesson is not that one token replaces all others. Rather, different multimodal papers show that token design can encode either global saliency, native 2D topology, or object-level grouping. “Everything” here refers to the scope of what a token mechanism can organize: compression, grounding, and open-vocabulary localization.

4. Tokens as correspondence structures and condensed plans

In some literatures, “everything” refers neither to a global summary token nor to a system-level accounting unit, but to a tokenized mechanism that generalizes across a family of tasks. Zero-shot sketch-based image retrieval provides a clear example. The “everything” model in this setting uses one transformer-based architecture for inter-category zero-shot retrieval, fine-grained zero-shot retrieval, and cross-dataset transfer. Its learnable tokenizer converts a $\tau(x)=(t_1,\ldots,t_n)$5 image into 196 visual tokens of dimension $\tau(x)=(t_1,\ldots,t_n)$6; a learnable retrieval token $\tau(x)=(t_1,\ldots,t_n)$7 guides self-attention; cross-attention establishes local sketch–photo correspondences; a cosine kernel matrix preserves token-pair similarity structure; and a kernel-based relation network aggregates these local matches into a retrieval score. On cross-dataset zero-shot SBIR, the relation-network variant is best in all four reported transfer directions, including S$\tau(x)=(t_1,\ldots,t_n)$8T(21): 0.476 / 0.590 and T$\tau(x)=(t_1,\ldots,t_n)$9S(8): 0.746 / 0.816. The architecture’s “everything” claim is therefore architectural universality across variants, not token omnipotence (Lin et al., 2023).

Long-video generation offers another variant, in which tokens act as compact plans rather than discrete symbols. TokensGen introduces condensed video tokens as continuous clip-level semantic embeddings. A short clip is compressed into $n=|\tau(x)|$0, with the selected shape $n=|\tau(x)|$1; these tokens are generated jointly across an entire long video by T2To, then rendered clip by clip by To2V, and stitched with adaptive FIFO-Diffusion. The framework supports up to 24 chunks of 49 frames, or 1176 frames in total. In human study, TokensGen reaches 75.69% on text-visual alignment and 75.74% on motion/content consistency, compared with 24.31% and 22.57% for AP-FIFO+CogVideoX. Here the token is neither a wordpiece nor a quantized codebook entry, but a condensed continuous planning state for long-horizon generation (Ouyang et al., 21 Jul 2025).

These cases broaden the technical meaning of tokenization. A token may be a retrieval primitive, a sparse explanation interface, or a clip-level latent plan. This suggests that “Everything Tokens” frequently names a design ambition: one token formalism should cover a larger task surface than earlier, more specialized parameterizations.

5. Financial, payment, and authentication meanings

In blockchain and asset-tokenization research, “Everything Token” acquires a literal legal-economic meaning. A two-tier architecture for alternative assets introduces Element Tokens $n=|\tau(x)|$2 for standardized, fully collateralized components of an asset, and an Everything Token $n=|\tau(x)|$3 for the whole asset:

$n=|\tau(x)|$4

The composite token is redeemable in both directions: depositing the exact basket mints one $n=|\tau(x)|$5, and burning one $n=|\tau(x)|$6 releases the same basket. Pricing is linked to the basket’s net asset value,

$n=|\tau(x)|$7

with ETF-style creation/redemption arbitrage keeping the composite approximately aligned with constituent value. This construction is presented for heterogeneous assets such as mines, power plants, agri-processing facilities, and data centers, and differs from opaque one-token-one-asset schemes precisely because the internal composition remains visible and tradable (Borjigin et al., 15 Aug 2025).

A related but more descriptive DeFi literature shows that real token ecosystems already exhibit “matryoshkian” composition. From Ethereum mainnet Transfer logs up to block 16,685,101, a graph of tokenization relations was constructed using deposit/mint and withdraw/burn meta-events. The filtered graph contains 8,424 vertices and 7,536 edges; its longest directed path has nine vertices, running from renBTC through sBTC, crvRenWSBTC, tbtc/sbtcCrv, btbtc/sbtcCrv, ibBTC, wibBTC, ibbtc/sbtcCRV-f, to bibbtc/sbtcCRV-f. The graph has no directed cycles or loops in the filtered case, which indicates deep but acyclic recursive tokenization in practice (Harrigan et al., 2024).

Payment and authentication systems add yet another meaning. In token-based payment systems, the key distinction is between account-based and bearer-like models, and between endogenous tracking, where the ledger tracks token state, and oblivious tracking, where assets track their own state and the ledger merely anchors commitments (Goodell, 2022). In large-scale scientific computing, bearer tokens replace identity-centric certificate chains with explicit capabilities. Fermilab’s production transition stores token attributes in FERRY, issues JWT access tokens via CILogon under the WLCG Common JWT Profile, stores high-value refresh tokens in Vault, and distributes short-lived access tokens through HTCondor integration. The reported lifetimes are 4 weeks for refresh tokens, 7 days for Vault tokens, and 3 hours for JWT access tokens (Dykstra et al., 31 Mar 2025). CMS describes the same conceptual shift as moving from X.509 identity plus VOMS-derived group/role authorization to a capability-centric bearer-token model; its target access-token lifetime is 1 hour, and the paper states that token infrastructure should be available 99.7% of the time for that regime (Bockelman et al., 31 Mar 2025).

These financial and infrastructural uses clarify an important misconception. An Everything Token in the asset-tokenization sense is not a universal medium of exchange, and an authentication token is not an abstract semantic token. The shared property is narrower: each token is a portable, machine-checkable claim on a larger substrate, whether that substrate is a project balance sheet, a recursively wrapped asset stack, or an authorization policy.

6. Conceptual tensions and open problems

The proliferation of meanings around “Everything Tokens” has produced several recurring tensions. First, token counts are not values. AI tokenomics states this explicitly: token expenditure and economic value are distinct, and optimal allocation requires equating risk-adjusted marginal productivity rather than minimizing raw volume (Zhu, 10 Jun 2026). The same lesson is visible in software engineering tokenomics, where high token consumption in Code Review indicates overhead, but not necessarily dispensable overhead, because refinement and verification exist to improve correctness (Salim et al., 20 Jan 2026).

Second, “everything as tokens” is not the only unification program. PixelWorld advances the alternative idea of Perceive Everything as Pixels, rendering text, tables, code, and diagrams into a single pixel space. The benchmark shows competitive performance on semantic-understanding tasks, but sharp drops on reasoning-intensive tasks such as MMLU-Pro, GSM8K, and MBPP; one explicit example is that GPT-4o drops by more than 25 points on MMLU-Pro under image input, while Chain-of-Thought helps image-based inference more than text-based inference on SuperGLUE. This constitutes a direct counterpoint to token-centric unification: representation unification can occur in pixel space as well, but current systems still rely on token-like structure for difficult compositional reasoning (Lyu et al., 31 Jan 2025).

Third, there remains a narrower but conceptually important literature in which tokens are units of computation itself. In the Truebit economy, CPU is defined so that 1 CPU always pays for 1 computation step, while TRU is the staking and reward token and DAO is a temporary governance token. The conversion rule mints CPU from burned TRU at the median bonded local price, and the design aims to stabilize computational consumption without external oracles, exchanges, or hierarchical nodes (Teutsch et al., 2019). This use is orthogonal to language-model tokenization, yet it reinforces the broader pattern that token schemes repeatedly emerge where a system needs a standardized unit of work.

Open problems therefore cut across domains. Hidden-token measurement, empirical calibration of token demand functions, token productivity metrics, dynamic allocation, congestion pricing, and token-based market design remain active research directions in AI systems (Zhu, 10 Jun 2026). In multimodal models, questions remain about whether global-summary tokens can preserve prompt-specific evidence under extreme compression, how spatial token vocabularies interact with external decoders such as SAM, and how far continuous condensed tokens can scale before they lose fine detail (Wang et al., 2024, Ren et al., 11 Dec 2025, Ouyang et al., 21 Jul 2025). In asset tokenization and payments, legal enforceability, oracle integrity, liquidity fragmentation, and governance of disaggregated claims remain unresolved (Borjigin et al., 15 Aug 2025, Goodell, 2022).

Taken together, the literature suggests that “Everything Tokens” is best understood as a family of technical programs that seek to make complex wholes administrable through tokenized interfaces. The whole may be an SDLC workflow, an image plane, a cross-modal correspondence space, a long video, a heterogeneous asset, a payment instrument, or an authorization regime. What unifies the family is not a common token type, but a common wager: once the right token interface is found, large heterogeneous systems become easier to optimize, compare, exchange, and control.