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arxiv: 2606.11371 · v1 · pith:474MA7UTnew · submitted 2026-06-09 · 💻 cs.CL · cs.AI· eess.AS· eess.SP

The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales

Han-Jen Chang , Yasir \c{C}atal , Angelika Wolman , Agust\'in Ib\'a\~nez , David Smith , I-Wen Su , Kai-Yuan Cheng , Georg Northoff This is my paper

Pith reviewed 2026-06-27 13:25 UTC · model grok-4.3

classification 💻 cs.CL cs.AIeess.ASeess.SP
keywords semantic time seriesautocorrelationWordNet depthSBERT embeddingshuman-AI language comparisontemporal semantic dynamicsnarrative analysis
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The pith

Autocorrelation on semantic time series shows generic vocabulary persists over longer timescales than specific words in both human and AI speech.

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

The paper develops a pipeline that converts timed word transcripts into time series of semantic specificity and similarity. It applies autocorrelation window measures to these series and compares them against shuffled versions that break temporal order. The analysis finds that longer autocorrelation windows align with more generic words while shorter windows align with specific words. This link disappears under randomization, suggesting the measures detect genuine temporal structure in how meaning unfolds. The approach offers a way to compare the dynamics of language produced by people and by large language models.

Core claim

The central discovery is that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words, and these associations are strongly attenuated when word order and timing are randomized.

What carries the argument

The autocorrelation-window measure ACW-0 computed on time series derived from WordNet word depth for specificity and SBERT embeddings for contextual similarity.

If this is right

  • ACW-based features can be used to analyze and compare the temporal structure of human and AI-generated speech.
  • The associations between ACW-0 and vocabulary specificity depend on the original sequential organization of the narrative.
  • Shuffled controls confirm that the measures go beyond static lexical distributions.
  • The pipeline works across human-read narratives, TTS readings, and LLM-generated texts.

Where Pith is reading between the lines

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

  • If the measures generalize, they could help identify when AI language fails to replicate human-like semantic pacing.
  • Applying the same pipeline to written text or other languages might reveal whether the pattern is modality-specific.
  • Testing with different embedding models could show how robust the temporal organization signal is to the choice of semantic representation.

Load-bearing premise

That measures of word depth from WordNet and similarity from SBERT embeddings produce time series whose autocorrelation genuinely reflects the temporal organization of semantic content rather than artifacts of those resources.

What would settle it

Observing the same association between ACW-0 length and generic versus specific words even after randomizing word order and timing would falsify the claim that the measures capture non-trivial temporal organization.

Figures

Figures reproduced from arXiv: 2606.11371 by Agust\'in Ib\'a\~nez, Angelika Wolman, David Smith, Georg Northoff, Han-Jen Chang, I-Wen Su, Kai-Yuan Cheng, Yasir \c{C}atal.

Figure 1
Figure 1. Figure 1: Analytical pipeline overview. (a) We investigate whether there are fluctuations [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Text is cut into multiple overlapping 100s timeframes with 10s interval [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Demonstration of one subject’s PSD of Word Depths and SBERT similarities [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: LMM results. Due to the multitude of datapoints, the ACW-0 categories were [PITH_FULL_IMAGE:figures/full_fig_p025_4.png] view at source ↗
read the original abstract

Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated speech. We introduce a semantic-timescale analysis pipeline that turns word-level transcripts with timestamps into semantic time-series. For each spoken narrative, we compute (i) semantic specificity using WordNet-based word depth and (ii) contextual similarity using SBERT embeddings and quantify their temporal dependence using autocorrelation-window measures (ACW-0 and related metrics). We then compare original speech to multiple shuffled controls that selectively disrupt lexical identity, temporal order, and word duration. Across human-read autobiographical narratives, TTS readings, and LLM-generated texts rendered with TTS, we find that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words. These associations are strongly attenuated or abolished when word order and timing are randomized, indicating that ACW-based measures capture non-trivial temporal organization of semantic content beyond static lexical distributions. Our results suggest that ACW-based semantic timescales are a useful family of features for analyzing and comparing the temporal structure of human and AI-generated speech.

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

2 major / 1 minor

Summary. The manuscript introduces a semantic-timescale analysis pipeline that transforms word-level transcripts with timestamps into semantic time-series using WordNet-based word depth for semantic specificity and SBERT embeddings for contextual similarity. It quantifies temporal dependence via autocorrelation-window measures (ACW-0 and related metrics) and compares original human autobiographical narratives, TTS readings, and LLM-generated texts against shuffled controls that disrupt lexical identity, temporal order, and word duration. The central finding is that segments with longer ACW-0 tend to contain more generic vocabulary while shorter ACW-0 segments are enriched in specific words; these associations are strongly attenuated or abolished under randomization of word order and timing, indicating that the measures capture non-trivial temporal semantic organization beyond static lexical distributions.

Significance. If the results hold after detailed verification, the work supplies an interpretable family of time-series features for comparing the temporal structure of human and AI-generated language. The explicit randomization controls that preserve lexical items while disrupting order and timing constitute a clear methodological strength, directly testing against static distributional artifacts and supporting the claim of genuine temporal organization. This approach could enable falsifiable comparisons across language sources and timescales.

major comments (2)
  1. [Abstract] Abstract: The abstract describes the pipeline, controls, and directional findings but provides no implementation details, statistical tests, sample sizes, or verification that the controls fully isolate temporal effects. This absence prevents evaluation of the robustness of the reported ACW-0 associations with vocabulary specificity.
  2. [Methods] Methods (implied from pipeline description): The construction of semantic time-series from WordNet depth and SBERT embeddings is presented at a high level without explicit formulas for ACW-0 computation or discussion of how segment boundaries are defined; these choices are load-bearing for the central claim that longer ACW-0 segments contain more generic vocabulary.
minor comments (1)
  1. [Abstract] The abstract and title use 'ACW-0' without a brief parenthetical definition on first use; adding one would improve accessibility for readers outside the immediate subfield.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and positive assessment of the work's potential. We address the two major comments below and have revised the manuscript accordingly to increase transparency.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract describes the pipeline, controls, and directional findings but provides no implementation details, statistical tests, sample sizes, or verification that the controls fully isolate temporal effects. This absence prevents evaluation of the robustness of the reported ACW-0 associations with vocabulary specificity.

    Authors: We agree the original abstract was concise and omitted quantitative details. We have revised it to report sample sizes, the primary statistical tests and effect sizes for the ACW-0–specificity associations, and a statement that the randomization controls abolish the pattern (as verified in the results). Implementation details remain in the Methods, now cross-referenced in the abstract. revision: yes

  2. Referee: [Methods] Methods (implied from pipeline description): The construction of semantic time-series from WordNet depth and SBERT embeddings is presented at a high level without explicit formulas for ACW-0 computation or discussion of how segment boundaries are defined; these choices are load-bearing for the central claim that longer ACW-0 segments contain more generic vocabulary.

    Authors: We accept that explicit formulas and segment-boundary definitions were insufficiently detailed. The revised Methods section now supplies the autocorrelation function, the precise definition of ACW-0 (first lag below threshold), and the procedure for delineating segments. We also added a paragraph explaining why these choices allow the observed link between ACW length and lexical specificity to be attributed to temporal organization rather than static distributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The pipeline constructs semantic time-series from external resources (WordNet depths, SBERT embeddings) and applies autocorrelation measures, then tests associations against multiple shuffled controls that preserve lexical items while disrupting order and timing. The reported attenuation under randomization directly falsifies the possibility that results reduce to static properties of the lexical resources. No load-bearing step equates a derived quantity to its own inputs by definition, no self-citation chain justifies a uniqueness claim, and no fitted parameter is relabeled as a prediction. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

The central claim rests on the validity of two external NLP resources and one statistical construct whose appropriateness for semantic time-series is assumed rather than derived within the paper.

axioms (3)
  • domain assumption WordNet provides a valid hierarchy for measuring semantic specificity via word depth.
    Used directly to compute the specificity time-series from transcripts.
  • domain assumption SBERT embeddings capture contextual similarity in a manner suitable for autocorrelation analysis.
    Used to build the contextual similarity time-series.
  • domain assumption Autocorrelation-window measures (ACW-0) quantify non-trivial temporal dependence in semantic time-series.
    Central metric whose interpretation drives the main finding.

pith-pipeline@v0.9.1-grok · 5816 in / 1658 out tokens · 33427 ms · 2026-06-27T13:25:47.616149+00:00 · methodology

discussion (0)

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