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arxiv: 2605.01134 · v1 · submitted 2026-05-01 · 💻 cs.AI

To Use AI as Dice of Possibilities with Timing Computation

Jia Li , Vipin Kumar , Rui Zhang This is my paper

Pith reviewed 2026-05-09 18:57 UTC · model grok-4.3

classification 💻 cs.AI
keywords verb-based paradigmtiming computationpossibilitylongitudinal EHRpatient trajectoriescounterfactual deductiondata-driven discoverybreast cancer data
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The pith

A verb-based paradigm with timing computation lets AI discover patient trajectories and deduce counterfactual timings purely from data.

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

The paper argues that the prevailing noun-centered way of building AI models has blocked any proper handling of time as an open dimension filled with possibilities. It proposes instead a verb-based framework that defines timing computation and possibility in precise terms so AI can act as a tool for capturing the grammar of thought about the future. When tested on electronic health records from thousands of breast cancer patients, the approach automatically surfaces clinically meaningful trajectories and counterfactual timing deductions. These outcomes emerge without any supplied medical knowledge or manual tuning, which would matter because it shows AI can extract actionable patterns from raw longitudinal records in domains where expert input is costly or incomplete.

Core claim

The paper claims that replacing noun-based modeling with a verb-based paradigm, together with explicit definitions of timing computation and possibility, enables AI to treat the future as an open temporal space. When applied to longitudinal EHR data from 3,276 breast cancer patients, this framework produces automatic discovery of clinically significant patient trajectories and counterfactual timing deductions, all in a purely data-driven manner that requires no prior domain knowledge.

What carries the argument

The verb-based paradigm equipped with timing computation and a formal definition of possibility, which shifts AI from static object descriptions to dynamic action sequences that carry temporal structure.

If this is right

  • Longitudinal health records can yield meaningful patient pathways without expert annotation.
  • Counterfactual timing deductions become feasible directly from observed sequences.
  • The same data-driven process could apply to any other longitudinal dataset for trajectory analysis.
  • AI systems could model future possibilities more explicitly by treating verbs and their timings as primary elements.

Where Pith is reading between the lines

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

  • The approach might reduce dependence on labeled training data in other sequential domains such as finance or logistics.
  • Extending the timing definitions to non-medical time series could test whether the paradigm generalizes beyond healthcare.
  • If the verb-based structure holds, it would imply that many current AI limitations stem from representational choices rather than data volume.

Load-bearing premise

That the verb-based paradigm and timing computation produce clinically significant trajectories and counterfactuals from raw data without any hidden domain knowledge or post-hoc adjustments.

What would settle it

Running the same method on the 3,276 breast cancer EHR records and finding that the discovered trajectories lack clinical significance or that the process implicitly relies on domain knowledge would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.01134 by Jia Li, Rui Zhang, Vipin Kumar.

Figure 1
Figure 1. Figure 1: Two representative trajectory clusters automatically discovered by the model. Red values denote view at source ↗
Figure 2
Figure 2. Figure 2: KDE plots of model-derived relative and counterfactual timing distributions for three events view at source ↗
read the original abstract

The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension. This paper introduces a verb-based paradigm, together with precise definitions of \emph{timing computation} and \emph{possibility}, that enables AI to function as an effective instrument for realizing the grammar of our thought. Applied to longitudinal EHR data from 3,276 breast cancer patients, the framework empirically demonstrates: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction. Both results are purely data-driven, require no prior domain knowledge, and, to our knowledge, represent the first such demonstrations in the machine learning literature.

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

3 major / 2 minor

Summary. The paper introduces a verb-based paradigm for AI modeling, along with definitions of timing computation and possibility, to represent the future as an open temporal dimension. Applied to longitudinal EHR data from 3,276 breast cancer patients, it claims to empirically demonstrate (1) automatic discovery of clinically significant patient trajectories and (2) counterfactual timing deduction, with both results being purely data-driven, requiring no prior domain knowledge, and representing the first such demonstrations in the ML literature.

Significance. If the methods and formal definitions can be provided and validated, the work could introduce a novel paradigm shift in temporal reasoning for AI, with implications for dynamic modeling in healthcare analytics. The purely data-driven claim for trajectory discovery and counterfactuals, if substantiated without embedded knowledge, would be noteworthy for its potential to reduce reliance on curated features in EHR analysis.

major comments (3)
  1. [Abstract] Abstract: The abstract asserts empirical results on 3,276 patients demonstrating automatic discovery of clinically significant trajectories and counterfactual timing deduction, but supplies no methods, validation details, error bars, baselines, or statistical tests. This prevents verification of the claims against the data.
  2. [Framework description] Framework description (verb-based paradigm, timing computation, and possibility): The central results are described as purely data-driven, yet they rest on newly introduced definitions whose precise form, equations, or algorithms are not shown. Without these, it is impossible to assess whether the definitions of timing computation and possibility embed the desired outcomes by construction, as is common in custom temporal formalisms.
  3. [Empirical demonstration] Empirical demonstration section: No algorithm for trajectory extraction from raw EHR events, no procedure for counterfactual deduction, and no mapping from events to 'possibility' or 'timing' are provided. Labeling trajectories as 'clinically significant' without explicit clinical features or post-hoc interpretation typically requires domain knowledge; the absence of this concrete mapping undermines the no-prior-domain-knowledge claim.
minor comments (2)
  1. The manuscript would benefit from pseudocode, formal mathematical definitions, or a reproducibility appendix for the key components to allow independent verification.
  2. Consider adding references and comparisons to existing literature on temporal data mining, patient trajectory modeling in EHR, and counterfactual reasoning in ML to contextualize the novelty claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments and the recognition of the potential significance of the verb-based paradigm. We agree that the current manuscript version requires expanded detail on the formal definitions, algorithms, and empirical procedures to enable verification. We will revise accordingly and address each point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts empirical results on 3,276 patients demonstrating automatic discovery of clinically significant trajectories and counterfactual timing deduction, but supplies no methods, validation details, error bars, baselines, or statistical tests. This prevents verification of the claims against the data.

    Authors: We acknowledge that the abstract, due to length limits, omits methodological specifics and statistical information. In the revised manuscript we will augment the abstract with a brief outline of the timing computation approach, the validation strategy, and references to the statistical tests, baselines, and any error measures reported in the main text. revision: yes

  2. Referee: [Framework description] Framework description (verb-based paradigm, timing computation, and possibility): The central results are described as purely data-driven, yet they rest on newly introduced definitions whose precise form, equations, or algorithms are not shown. Without these, it is impossible to assess whether the definitions of timing computation and possibility embed the desired outcomes by construction, as is common in custom temporal formalisms.

    Authors: The initial submission presented the definitions at a conceptual level. We will revise the framework section to supply the explicit mathematical formulations and algorithmic descriptions of timing computation and possibility. These additions will make clear that the definitions provide a general mechanism applied to data rather than presupposing specific trajectories or counterfactuals. revision: yes

  3. Referee: [Empirical demonstration] Empirical demonstration section: No algorithm for trajectory extraction from raw EHR events, no procedure for counterfactual deduction, and no mapping from events to 'possibility' or 'timing' are provided. Labeling trajectories as 'clinically significant' without explicit clinical features or post-hoc interpretation typically requires domain knowledge; the absence of this concrete mapping undermines the no-prior-domain-knowledge claim.

    Authors: We will add a dedicated subsection detailing the algorithm that extracts trajectories by applying timing computation directly to raw event sequences. The counterfactual deduction procedure and the event-to-possibility/timing mapping will be specified step by step. Clinical significance will be justified through purely data-driven criteria (e.g., recurrence patterns and outcome correlations observable in the 3,276-patient cohort) without the use of external clinical features or prior knowledge during discovery; any post-hoc interpretation will be clearly separated from the automated process. revision: yes

Circularity Check

0 steps flagged

No circularity identified; derivation chain not exhibited in accessible text.

full rationale

The paper introduces a verb-based paradigm along with definitions of timing computation and possibility, then reports empirical results on EHR data as purely data-driven and free of prior domain knowledge. No specific equations, algorithms, or derivation steps are quoted in the abstract or referenced full-text placeholder that would allow inspection for self-definition, fitted-input prediction, or reduction of outputs to inputs by construction. The central claims therefore remain self-contained assertions without any load-bearing step that reduces to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

Only the abstract is available, so the ledger is reconstructed from stated claims. The approach rests on two new definitions and a paradigm shift whose details and grounding are not provided.

axioms (1)
  • domain assumption A verb-based paradigm with precise definitions of timing computation and possibility enables AI to represent the future as an open temporal dimension.
    Presented in the abstract as the foundational shift that overcomes noun-based limitations.
invented entities (3)
  • verb-based paradigm no independent evidence
    purpose: To replace noun-based modeling and allow representation of time and possibility.
    Introduced as the core new framework.
  • timing computation no independent evidence
    purpose: Precise mechanism for handling when possibilities occur.
    Defined in the paper as part of the new paradigm.
  • possibility no independent evidence
    purpose: To model open future branches in AI reasoning.
    Defined alongside timing computation.

pith-pipeline@v0.9.0 · 5406 in / 1492 out tokens · 47874 ms · 2026-05-09T18:57:01.732907+00:00 · methodology

discussion (0)

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Reference graph

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