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arxiv: 2512.13618 · v3 · submitted 2025-12-15 · 💻 cs.CL · cs.LG

Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models

Zefang Liu , Nam H. Nguyen , Yinzhu Quan , Shi-Xiong Zhang This is my paper

Pith reviewed 2026-05-16 21:59 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords temporal tokenizationevent sequence modelinglarge language modelstime encodingstatistical alignmentfine-tuningsequence predictiontemporal data
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The pith

No single temporal tokenization strategy works best for all event sequences in large language models.

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

The paper compares five ways to encode time information when fine-tuning large language models on sequences of events: plain numeric strings, byte-level numbers, calendar tokens, uniform bins, and adaptive quantization. It runs these on real datasets that show different time patterns, from smooth to spiky distributions. The central result is that each strategy only performs well when its encoding matches the specific statistical shape of the data. This matters because event modeling in domains like user behavior or sensor logs depends on accurate time handling, and a mismatch can degrade predictions even with powerful models.

Core claim

The analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, highlighting temporal tokenization as a critical yet often overlooked design dimension in LLM-based event modeling.

What carries the argument

The match between a chosen temporal tokenization method and the statistical distribution of event times in the training data.

If this is right

  • Designers must test multiple tokenizers against a dataset's time statistics before selecting one.
  • Models trained on smooth time patterns will favor different encodings than those on discrete spiky patterns.
  • Standard benchmarks for temporal LLMs need to include distribution-aware tokenizer comparisons.
  • Adaptive quantization shows promise when event times follow non-uniform distributions.

Where Pith is reading between the lines

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

  • A system could inspect incoming data statistics first and route to the matching tokenizer automatically.
  • The same alignment principle may apply to other sequence tasks that embed continuous values like prices or measurements.
  • Controlled synthetic datasets with known distributions could isolate exactly which statistical features drive the performance gaps.

Load-bearing premise

The selected real-world datasets capture the main statistical distributions that appear in practice and the fine-tuning results hold for other models and datasets.

What would settle it

Demonstrating one fixed tokenization method that achieves top performance across all tested distributions without any alignment step would falsify the central claim.

Figures

Figures reproduced from arXiv: 2512.13618 by Nam H. Nguyen, Shi-Xiong Zhang, Yinzhu Quan, Zefang Liu.

Figure 1
Figure 1. Figure 1: Log-scale distributions of relative time inter [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of relative time intervals ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of relative time intervals ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of relative time intervals ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of relative time intervals ( [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of relative time intervals ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level representations or calendar tokens have been proposed. However, the optimal approach remains unclear, especially given the diverse statistical distributions of real-world event data, which range from smooth log-normal to discrete, spiky patterns. This paper presents a systematic empirical study of temporal tokenization for modeling event sequences with LLMs, comparing distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. We evaluate these strategies by fine-tuning LLMs on real-world datasets that exemplify these diverse distributions. Our analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, highlighting temporal tokenization as a critical yet often overlooked design dimension in LLM-based event modeling.

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

1 major / 1 minor

Summary. The paper presents a systematic empirical study of temporal tokenization strategies for modeling event sequences with LLMs. It compares five approaches—naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization—by fine-tuning LLMs on real-world datasets chosen to exemplify diverse statistical distributions ranging from smooth log-normal to discrete spiky patterns. The central finding is that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties.

Significance. If the empirical results hold under controlled conditions, the work usefully identifies temporal tokenization as an important and often overlooked design dimension for LLM-based event sequence modeling. The multi-strategy, multi-dataset comparison provides practical evidence against one-size-fits-all solutions and emphasizes matching tokenizer inductive bias to data statistics. The purely empirical framing avoids circularity but makes the strength of the conclusions rest on the quality of the controls and statistical analysis.

major comments (1)
  1. [Results and Analysis] The central claim that performance 'depends heavily on aligning the tokenizer with the data's statistical properties' is load-bearing yet rests on observational comparisons across a small number of real-world datasets. No quantitative correlation is reported between measured distributional statistics (e.g., skewness, kurtosis, or log-normality tests) and the observed tokenizer rankings, nor are synthetic controls used to isolate the effect of the distribution from confounders such as sequence length, vocabulary size, or fine-tuning hyperparameters. This leaves open the possibility that the reported differences arise from dataset-specific artifacts rather than the hypothesized alignment mechanism.
minor comments (1)
  1. [Abstract] The abstract and introduction would benefit from explicit statements of the evaluation metrics (e.g., next-event prediction accuracy, perplexity) and the precise fine-tuning protocol used for each tokenizer.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The feedback highlights an important opportunity to strengthen the evidential basis for our central claim. We address the major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

  1. Referee: [Results and Analysis] The central claim that performance 'depends heavily on aligning the tokenizer with the data's statistical properties' is load-bearing yet rests on observational comparisons across a small number of real-world datasets. No quantitative correlation is reported between measured distributional statistics (e.g., skewness, kurtosis, or log-normality tests) and the observed tokenizer rankings, nor are synthetic controls used to isolate the effect of the distribution from confounders such as sequence length, vocabulary size, or fine-tuning hyperparameters. This leaves open the possibility that the reported differences arise from dataset-specific artifacts rather than the hypothesized alignment mechanism.

    Authors: We agree that adding quantitative support would strengthen the manuscript. In the revision we will compute and report standard distributional statistics (skewness, kurtosis, Shapiro-Wilk or similar log-normality tests) for each dataset and include Spearman rank correlations between these measures and the relative performance ranking of each tokenizer. We will also add an explicit statement of the controls already applied: all experiments used identical sequence-length padding, the same fine-tuning hyperparameters, and the same base LLM. We maintain that real-world datasets provide stronger ecological validity than synthetic controls for this domain; however, we will add a limitations paragraph acknowledging that fully isolating distributional shape from other dataset idiosyncrasies would benefit from future synthetic experiments. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical comparison with no derivations or self-referential reductions

full rationale

The paper performs a systematic empirical evaluation of five temporal tokenization strategies (naive numeric strings, byte-level, calendar tokens, uniform binning, adaptive quantization) by fine-tuning LLMs on real-world event datasets chosen to represent different statistical distributions. No equations, derivations, or predictions are presented that reduce to fitted parameters, self-definitions, or self-citations. The central claim—that performance depends on alignment with data statistics—is an observational conclusion drawn from experimental results rather than a mathematical identity or load-bearing self-reference. All steps are externally falsifiable via replication on the same or new datasets, satisfying the criteria for a self-contained empirical study with no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that fine-tuning performance differences reflect tokenizer quality rather than confounding factors such as token vocabulary size or optimization dynamics; no new mathematical axioms or invented entities are introduced.

pith-pipeline@v0.9.0 · 5467 in / 1031 out tokens · 27934 ms · 2026-05-16T21:59:36.315990+00:00 · methodology

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Reference graph

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12 extracted references · 12 canonical work pages · 3 internal anchors

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